docs: synchronize novelty boundary with D4 and R2

1 files changed, 23 insertions, 7 deletions

diff --git a/NOVELTY.md b/NOVELTY.md
index 844c6c4..6106b07 100644
--- a/NOVELTY.md
+++ b/NOVELTY.md
@@ -148,10 +148,14 @@ gain control. `THEORY.md` states the assumptions and the executable finite-sampl
   bottleneck; it is not a novelty claim and the direct estimator's 68.4x work precludes presenting
   it as the scalable algorithm.
 - On the inherited reciprocal-KP substrate, dynamic paired-neutral innovation
-  passes a frozen 20-epoch ResNet-20 validation gate at 83.58%, versus 82.66%
-  for clean KP and 10% for the failed raw/norm-matched mixed-traffic controls.
-  This supports a short standard-scale load-bearing result, not yet a full or
-  independently confirmed scaling claim.
+  first passes a frozen 20-epoch ResNet-20 validation gate at 83.58%, versus
+  82.66% for clean KP and 10% for the failed raw/norm-matched mixed-traffic
+  controls. The subsequent 200-epoch D3 validation reaches 91.18%, and the
+  independently frozen paired D4 test panel reaches 91.584% across seeds
+  10--14 versus clean KP's 91.388%, with a 0.131-point one-sided upper bound on
+  the clean-minus-dynamic deficit. This supports load-bearing innovation and
+  robustness on ResNet-20. It does not establish positive utility from adding
+  standard-network depth.
 
 ## Claims currently forbidden
 
@@ -161,6 +165,13 @@ gain control. `THEORY.md` states the assumptions and the executable finite-sampl
 - SDIL is the first dendritic method to scale to deep vision tasks.
 - Dynamic neutral projection is a single-phase rule or has zero observation
   cost merely because it adds no convolutional MAC.
+- A single ResNet-20 architecture establishes scaling with added standard
+  depth, or licenses the sealed ResNet-32/56 panel after its oral-B
+  prerequisite failed.
+- SDIL reproduces the complete Harnett population outcome-vectorization,
+  longitudinal, desired-velocity, or online-control signature. The untouched
+  oral-B R2 panel fails its joint gate despite successful task learning and
+  sign inversion.
 - Reproducing the already-published Harnett/BurstCCN qualitative signatures is an oral-level
   biological contribution.
 
@@ -178,6 +189,11 @@ gain control. `THEORY.md` states the assumptions and the executable finite-sampl
    source data and states that full data/code are available on request.
 5. For a scaling contribution, target standard architectures and a simplicity/cost regime not
    already occupied by BurstCCN, rather than treating ImageNet alone as novelty.
-6. Complete the predeclared D3 full ResNet endpoint and an independent
-   multi-seed confirmation before promoting the dynamic short result into a
-   standard-scale claim.
+6. D3 and D4 now establish the narrow ResNet-20 result. A stronger scaling
+   contribution requires an independently frozen, genuinely depth-necessary
+   standard-network panel; the existing ResNet-20/32/56 panel remains sealed
+   because oral-B R2 failed.
+7. A biological contribution beyond Harnett/BurstCCN requires a new mechanism
+   and prediction frozen independently of the failed R2 metrics. Successful
+   task learning, lesion sensitivity, and sign inversion alone cannot be
+   relabelled as the failed joint population-signature claim.