fix: report the configured teaching-signal alignment

3 files changed, 13 insertions, 4 deletions

diff --git a/experiments/run.py b/experiments/run.py
index eb9f356..3cb2e00 100644
--- a/experiments/run.py
+++ b/experiments/run.py
@@ -115,6 +115,7 @@ def train(args):
                 if args.mode != "bp":
                     al = probes.alignment_report(net, px, py, poh, cfg)
                     rec["cos_r_negg"] = al["cos_r_negg"]
+                    rec["cos_innovation_negg"] = al["cos_innovation_negg"]
                     rec["cos_apical_negg"] = al["cos_apical_negg"]
                     rec["cos_Ac_negg"] = al["cos_Ac_negg"]
                     rec["r_norm"] = al["r_norm"]
@@ -141,6 +142,7 @@ def train(args):
     if args.mode != "bp":
         al = probes.alignment_report(net, px, py, poh, cfg)
         log["final"]["cos_r_negg"] = al["cos_r_negg"]
+        log["final"]["cos_innovation_negg"] = al["cos_innovation_negg"]
         log["final"]["cos_apical_negg"] = al["cos_apical_negg"]
         log["final"]["cos_Ac_negg"] = al["cos_Ac_negg"]
     os.makedirs(args.outdir, exist_ok=True)
diff --git a/experiments/smoke.py b/experiments/smoke.py
index f440f7f..575f0bd 100644
--- a/experiments/smoke.py
+++ b/experiments/smoke.py
@@ -159,6 +159,10 @@ def main():
     print(f"  raw apical cos(a,-g)    = {apical_cos:+.3f}")
     print(f"  pure A c   cos(Ac,-g)   = {sum(al_n['cos_Ac_negg'])/len(al_n['cos_Ac_negg']):+.3f}")
     assert residual_cos > apical_cos + 0.1
+    cfg_raw = SDILConfig(use_residual=False, learn_A=False, learn_P=True)
+    al_raw = probes.alignment_report(net_n, x, y, yoh, cfg_raw)
+    assert al_raw["cos_r_negg"] == al_raw["cos_apical_negg"], (
+        "reported teaching alignment must honor use_residual=False")
 
     # ---- CHECK 5: single-step loss-decrease ratio vs exact GD ----
     print("\nCHECK5 single-step loss-decrease ratio vs exact GD:")
diff --git a/sdil/probes.py b/sdil/probes.py
index b58fe89..16aca21 100644
--- a/sdil/probes.py
+++ b/sdil/probes.py
@@ -55,21 +55,24 @@ def alignment_report(net, x, y, y_onehot, cfg):
     e = net.output_error(logits, y_onehot)
     c = e
 
-    out = {"cos_r_negg": [], "cos_apical_negg": [], "cos_Ac_negg": [],
+    out = {"cos_r_negg": [], "cos_innovation_negg": [],
+           "cos_apical_negg": [], "cos_Ac_negg": [],
            "r_norm": [], "g_norm": [], "loss": loss}
     for l in range(net.L - 1):
         g = grads[l]
         negg = -g
         a = net.apical(l, c, h[l + 1])                  # raw apical (with nuisance)
         Ac = c @ net.A[l].t()                           # pure vectorizer output
-        r = a - net.baseline(l, h[l + 1])               # innovation
+        innovation = a - net.baseline(l, h[l + 1])
+        teaching = innovation if cfg.use_residual else a
         # include the phi' gain that the plasticity rule actually applies,
         # so the reported alignment is what the weight update "sees"
         gain = net.act_prime(u[l])
-        out["cos_r_negg"].append(_row_cos(r * gain, negg * gain))
+        out["cos_r_negg"].append(_row_cos(teaching * gain, negg * gain))
+        out["cos_innovation_negg"].append(_row_cos(innovation * gain, negg * gain))
         out["cos_apical_negg"].append(_row_cos(a * gain, negg * gain))
         out["cos_Ac_negg"].append(_row_cos(Ac * gain, negg * gain))
-        out["r_norm"].append(r.norm(dim=1).mean().item())
+        out["r_norm"].append(teaching.norm(dim=1).mean().item())
         out["g_norm"].append(g.norm(dim=1).mean().item())
     return out