docs: sharpen innovation novelty boundary

1 files changed, 14 insertions, 2 deletions

diff --git a/NOVELTY.md b/NOVELTY.md
index c044927..cebc3ab 100644
--- a/NOVELTY.md
+++ b/NOVELTY.md
@@ -72,6 +72,14 @@ paper. It addresses an identifiability problem: task-period regression can subtr
 part of `A_l c` whenever output error correlates with somatic state, while neutral-period regression
 identifies the traffic relationship without observing the teaching component.
 
+The formal justification is an application of the standard conditional-expectation projection
+identity, not a new mathematical theorem. Among all predictors measurable from the chosen somatic
+statistic, the neutral conditional mean uniquely minimizes residual nuisance power and leaves an
+innovation orthogonal to every such predictor. The implemented per-cell affine model realizes the
+restricted projection onto `{1,h_i}`. Positive norm matching cannot change the raw signal's cosine
+direction at a fixed state, so the matched-raw ablation specifically distinguishes subtraction from
+gain control. `THEORY.md` states the assumptions and the executable finite-sample identities.
+
 ## Claims currently allowed
 
 - The inherited learned-feedback backbone preserves performance with depth better than fixed DFA
@@ -79,6 +87,9 @@ identifies the traffic relationship without observing the teaching component.
 - Frozen controls show that per-cell residualization protects learning from soma-coupled traffic.
   It mitigates but does not fully remove endogenous top-down traffic, so arbitrary contextual
   innovation is not an allowed teaching-signal claim.
+- Neutral residualization is the minimum-power nuisance removal within the predictor's somatic
+  function class. For the implemented diagonal affine class this is a narrow, testable statement,
+  not a claim of optimal population-level denoising.
 - Simultaneous batched perturbations have a frozen hardware-independent advantage: K1/every4
   retains 112.9% of the K16/every4 gain over DFA with 16x fewer logical loss queries, 11x less
   calibration work, and 5.3x less total forward-equivalent work on CIFAR-10 d20/w64.
@@ -102,8 +113,9 @@ identifies the traffic relationship without observing the teaching component.
    raw and norm-matched raw feedback, while the no-traffic methods coincide.
 2. Compare directly with Lansdell-style learned feedback and BurstCCN under validation-selected,
    disclosed compute/query budgets.
-3. Prove the conditional-bias/SNR benefit of neutral residualization and test its predicted failure
-   when traffic depends on the teaching signal after conditioning on soma.
+3. The conditional-projection/SNR result and task-period absorption prediction are now proved and
+   executable. A broader paper would still benefit from a separately frozen failure test where
+   traffic depends on the teaching signal after conditioning on the available soma statistic.
 4. For a biological contribution, make and test a prediction not used by Harnett or BurstCCN. Raw
    event-level data and analysis code require author coordination; the paper exposes only plotted
    source data and states that full data/code are available on request.