docs: correct SDIL novelty boundary

1 files changed, 10 insertions, 4 deletions

diff --git a/RESULTS.md b/RESULTS.md
index cdbe4d7..da8c957 100644
--- a/RESULTS.md
+++ b/RESULTS.md
@@ -32,7 +32,8 @@ Per hidden layer l:
 - apical feedback `a_l = A_l c` (c = output error e = softmax−onehot, broadcast)
 - default per-neuron predictor `ĥ_l = p_l ⊙ h_l + b_l`; teaching signal `r_l = a_l − ĥ_l`
 - three-factor update `ΔW_l = η (r_l ⊙ φ'(u_l)) h_{l-1}^T`
-- `A_l` LEARNED by amortized node perturbation (q_l ≈ −∇_{h_l}L, forward-only, no weight transport)
+- `A_l` learned by node perturbation (q_l ≈ −∇_{h_l}L, forward-only, no weight transport),
+  following Lansdell, Prakash & Kording's learned synthetic-feedback method
 - `P_l` learned on neutral (c=0) periods only, so it strips the soma-predictable nuisance
   without eating the teaching signal.
 
@@ -40,9 +41,11 @@ The scalable perturbation mode injects independent Rademacher interventions at a
 in the same antithetic forward evaluations. Cross-layer interference adds variance but averages to
 zero; it trains all `A_l` using `O(depth)` rather than `O(depth^2)` forward work.
 
-Distinct from the failed `~/sdrn` (fixed-random feedback + post-hoc residualization) and from
-Dual Prop / DFA / FA: the feedback pathway is *learned by causal perturbation*, so the residual is
-aligned by construction rather than being a random projection.
+Relative to fixed DFA/FA, the feedback pathway is learned by causal perturbation. That ingredient
+is not novel: without traffic and `P`, it is the direct learned-feedback method of
+[Lansdell et al.](https://arxiv.org/abs/1906.00889). The candidate SDIL contribution is specifically
+the per-cell residual under mixed apical traffic and its neutral-period identification. The full
+claim boundary is frozen in `NOVELTY.md`.
 
 ## Critical fix
 Node-perturbation estimator must divide by σ, not σ² (with δ=σξ, ĝ = δ·ΔL/σ² = ξ·ΔL/σ ⇒ ‖q‖≈‖∇L‖).
@@ -212,6 +215,9 @@ their captions, source-hash manifest, and `results/audited_tables.md`, then runs
 
 ## Open items
 
+- Treat learned direct node-perturbation feedback (Lansdell et al.) as the exact backbone baseline,
+  and BurstCCN as the strongest dendritic/scaling baseline; do not attribute their ingredients to
+  SDIL.
 - Compare simultaneous calibration at fixed loss-evaluation budgets and sweep directions
   (4/8/16/32).
 - Validate SDIL on a genuinely depth-necessary compositional task and a CNN; flattened CIFAR does