results: pass residual mirror capture gate

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@@ -828,6 +828,31 @@ gain errors and the remaining compounding angular errors can materially change
 the trajectory. It would be misleading to present the capture result alone as
 BP-equivalent learning.
 
+### Residual response-mirror capture
+
+Residual response mirroring changes the local baseline's update from averaging
+a fresh noisy weight estimate to predicting each child response and updating
+from the local response residual. At exact symmetry each stochastic update is
+zero. The frozen RRM-1 screen gives:
+
+| method/rate | early alignment | all-layer alignment | feedback/forward cosine | norm range |
+|:--|--:|--:|--:|:--|
+| fixed HFA | -0.000263 | 0.011279 | 0.001614 | [0.959, 1.055] |
+| RRM 0.03 | 0.075016 | 0.235001 | 0.622965 | [0.695, 0.792] |
+| RRM 0.10 | **0.665070** | **0.730253** | **0.953680** | **[0.884, 1.018]** |
+| RRM 0.30 | 0.003489 | 0.053744 | 0.740396 | [0.999, 30.132] |
+
+Selected `eta_M=0.1` passes every frozen capture check after 20 batch-1 local
+observations. It consumes zero task-loss queries and `2.4458e9` MACs. Compared
+with the selected normalized mirror record, it gains 0.219 early-alignment and
+0.188 all-layer-alignment points while keeping feedback norms controlled. The
+failed high-rate record shows that the residual rule is not rate-insensitive:
+at 0.3 the maximum norm ratio reaches 30.13 and alignment collapses.
+
+RRM-2 is opened using the already frozen rate and cadence. This is a strong
+inherited learned-alignment baseline, not Harnett-specific evidence, so it does
+not raise the SDIL paper score by itself.
+
 ## How to run
 `experiments/run.py --mode {bp,fa,dfa,sdil} --dataset {mnist,fmnist,cifar10} --depth D --residual {0,1} --act {tanh,gelu,silu,relu}`
 Batteries: `experiments/run_v2.sh <ds> "<depths>" <res> <act> "<seeds>" <ep> <pfx>`.