docs: position SDIL as debiasing plug-in

1 files changed, 7 insertions, 2 deletions

diff --git a/TWO_STATE_BIAS_PROGRAM.md b/TWO_STATE_BIAS_PROGRAM.md
index 58bdf6c..b0df1b2 100644
--- a/TWO_STATE_BIAS_PROGRAM.md
+++ b/TWO_STATE_BIAS_PROGRAM.md
@@ -18,6 +18,10 @@ one feedback backbone. Its central hypothesis is:
 > neutral, per-cell prediction removes the identifiable component before it is
 > consolidated into synapses.
 
+SDIL is presented as a local debiasing plug-in for existing two-state learners,
+not as a new optimizer on an accuracy--cost Pareto frontier. Comparisons use
+matched budgets where possible and report unmatched overhead in a table.
+
 “Two-state” includes equilibrium propagation (one recurrent network at free
 and nudged equilibria), coupled learning, contrastive Hebbian learning, Dual
 Propagation, and related positive/negative-state rules. It does not imply that
@@ -212,8 +216,9 @@ same bias variable and neutral observation have an exact, auditable meaning.
 Every backbone reports its native clean endpoint, biased raw endpoint, best
 bias-specific baseline, SDIL and oracle. “Beat” means a paired advantage under
 bias at matched architecture/data/training, together with no meaningful clean
-regression and an explicit cost coordinate. It does not mean that SDIL must
-beat BP or centered EP on clean accuracy.
+regression, a matched observation protocol and disclosed implementation
+overhead. It does not mean that SDIL must beat BP or centered EP on clean
+accuracy.
 
 | family | natural/independent bias | strongest required baseline | scale axis |
 |---|---|---|---|