docs: position SDIL as debiasing plug-in

1 files changed, 22 insertions, 10 deletions

diff --git a/ICLR_2027_REPLAN.md b/ICLR_2027_REPLAN.md
index d5444b6..dbd74f4 100644
--- a/ICLR_2027_REPLAN.md
+++ b/ICLR_2027_REPLAN.md
@@ -29,6 +29,12 @@ locally identifiable operation beats strong bias-specific corrections on a
 measured physical model and transfers unchanged across local-learning
 backbones.
 
+SDIL is positioned as a local debiasing plug-in for existing two-state
+learners, not as a new optimizer competing on an accuracy--cost frontier. All
+comparisons therefore use a matched training/observation budget where
+possible, and disclose unmatched phase, storage and arithmetic overhead in a
+table rather than a Pareto figure.
+
 ## What the paper must and must not claim
 
 The paper may claim that structured differential bias does not disappear with
@@ -89,8 +95,9 @@ Pass conditions:
 - SDIL closes a substantial fraction of the raw-to-oracle gap on every
   identifiable state-dependent-bias setting;
 - SDIL beats constant calibration when the bias varies with local state;
-- SDIL is nondominated with overclamping on error/cycle span versus charged
-  observation and circuit cost;
+- under a predeclared matched observation/phase budget, SDIL is competitive
+  with overclamping on error floor and cycle span; unmatched implementation
+  overhead is reported separately;
 - common-mode bias gives no artificial SDIL advantage;
 - task leakage and neutral-to-task shift fail in the direction predicted by
   the identifiability theory.
@@ -137,8 +144,8 @@ Pass conditions:
 - neutral predictability, rather than corruption RMS, predicts recovery;
 - SDIL improves every biased raw endpoint and beats constant calibration on
   state-dependent bias;
-- SDIL is competitive with each family's strongest correction after cost is
-  charged;
+- SDIL is competitive with each family's strongest correction under the
+  matched protocol, with any observation/storage overhead disclosed;
 - the frozen adapter passes the executable BP-free audit in every family;
 - clean/common-mode controls show that the gain is not ordinary regularization
   or a changed optimizer.
@@ -176,14 +183,18 @@ appendix or is removed.
 1. **Problem and real evidence:** Harnett residual motivation, the two-state
    measurement model, and the released physical error plateau/cycle drift.
 2. **Mechanism on the physical model:** matched noise versus bias; raw,
-   calibration, overclamping, SDIL and oracle; error/cycle span against charged
-   cost.
+   calibration, overclamping, SDIL and oracle; error floor, drift and cycle
+   span under the matched protocol.
 3. **Scaling across backbones:** performance gap and residual bias versus depth,
    width, biased-block count and task difficulty for physical learning, DP and
    EP/CpL.
 4. **What makes recovery possible:** neutral predictability, drift rate,
-   task leakage and distribution shift, followed by an accuracy--cost Pareto
-   summary.
+   task leakage and distribution shift, showing the predicted success and
+   failure boundary of the plug-in.
+
+Phase, observation, storage, arithmetic, wall-time and memory overhead appear
+in one compact audit table. They are controls against an unfair comparison,
+not a separate Pareto claim.
 
 The key scaling plot must show both the failure term and its removal. Plotting
 only final accuracy across larger clean architectures is not evidence for the
@@ -209,7 +220,8 @@ operation, real measured evidence, causal controls and cross-backbone transfer.
 
 - extend EP and coupled-learning DCHNs across FashionMNIST, SVHN and CIFAR-10,
   multiple depths/biased-block counts and five seeds;
-- finish five-seed VGG16 DP and the full cost Pareto comparison;
+- finish five-seed VGG16 DP and the complete matched-budget bias-recovery
+  comparison;
 - cross fixed, state-dependent and slowly drifting bias with matched noise,
   predictability and neutral/task shift;
 - keep one frozen predictor configuration across comparable backbones;
@@ -269,4 +281,4 @@ Update one evidence table after every completed gate:
 
 Every progress report should state positive results, negative results, compute
 and a fresh reviewer score. Accuracy without the matched strongest baseline,
-cost coordinate or frozen protocol does not move the score.
+matched protocol or overhead disclosure does not move the score.