docs: define novelty boundary for fast neutral projection

1 files changed, 27 insertions, 0 deletions

diff --git a/NOVELTY.md b/NOVELTY.md
index 1ad1a5c..844c6c4 100644
--- a/NOVELTY.md
+++ b/NOVELTY.md
@@ -83,6 +83,23 @@ accuracy, it only supplies a stable feedback substrate. The candidate novelty
 still requires neutral somato-dendritic subtraction to outperform raw and
 norm-matched raw apical activity under the same mixed-traffic intervention.
 
+The post-MT-1 fast neutral projection does not make reciprocal KP novel. It
+adds a second-timescale operation to the innovation mechanism:
+
+```text
+e0_l = a0_l - P_l(h_l)                         neutral residual
+q_l  = Cov(e0_l,h_l) / Var(h_l)                current local coupling
+r_l  = s_l + e0_l - E[e0_l] - q_l(h_l-E[h_l]) stabilized innovation
+```
+
+The coefficient fit is local and instruction-off, so it does not use a task
+loss, task-nudged state, downstream weight, or reverse pass. Its defensible
+novelty is not “two phases” or generic normalization; it is the explicit
+operator-stability role of a per-cell somato-dendritic innovation measured from
+paired neutral observations. The paper must nevertheless count and disclose
+the neutral microphase. It cannot describe this variant as single-phase, and
+must discuss proximity to contrastive/phase-separated local-learning methods.
+
 ## Candidate SDIL contribution
 
 The candidate contribution is the combination
@@ -130,6 +147,11 @@ gain control. `THEORY.md` states the assumptions and the executable finite-sampl
   vectorizer does not. This identifies feedback amortization as the current engineering
   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.
 
 ## Claims currently forbidden
 
@@ -137,6 +159,8 @@ gain control. `THEORY.md` states the assumptions and the executable finite-sampl
 - Simultaneously perturbing all hidden layers is novel.
 - The no-traffic scaling panel demonstrates the necessity of somato-dendritic residuals.
 - 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.
 - Reproducing the already-published Harnett/BurstCCN qualitative signatures is an oral-level
   biological contribution.
 
@@ -154,3 +178,6 @@ 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.