baseline: add convolutional hierarchical feedback alignment

1 files changed, 5 insertions, 0 deletions

diff --git a/BASELINES.md b/BASELINES.md
index 9c3a320..de7dfe8 100644
--- a/BASELINES.md
+++ b/BASELINES.md
@@ -15,6 +15,11 @@ weak in-house reimplementations from defining the state of the art.
 The audited implementations and completed results are in `RESULTS.md`:
 
 - BP, FA, and DFA share the SDIL forward architecture and data order;
+- convolutional hierarchical FA (`hfa`) mirrors the ResNet residual DAG with
+  independent random 3x3 feedback tensors, exact parameter-free shortcut
+  adjoints, and local ReLU/BatchNorm Jacobians; it never copies or reads a
+  downstream forward convolution and is the mandatory baseline for any learned
+  hierarchical extension;
 - direct node perturbation uses a causal target on every hidden update and no
   learned vectorizer;
 - the no-traffic, `P=0` SDIL backbone is learned direct feedback by node