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diff --git a/scripts/aep_depth.py b/scripts/aep_depth.py
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+++ b/scripts/aep_depth.py
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+"""B: does AEP gradient fidelity degrade as the non-conservative attention gets DEEPER?
+Stack K residual attention sub-layers (weight-tied) inside the force; measure naive vs
+AEP attention-param cosine vs BPTT, at fixed scale s."""
+import torch, aep_characterize as A
+from cet_aep import CETReal
+from cet_mvp import make_patch_mask, get_loaders
+
+dev = 'cuda' if torch.cuda.is_available() else 'cpu'
+torch.manual_seed(0)
+model = CETReal(28, 1, 7, 7, D=64, heads=4, dh=16, mem=128).to(dev)
+names = [n for n, _ in model.named_parameters()]
+trl, _ = get_loaders(32, dataset='fashionmnist')
+X, _ = next(iter(trl)); X = X.to(dev)
+M = make_patch_mask(X.size(0), model.gh, 7, 7, 28, 28, 0.5, dev)
+A.X, A.M, A.XBAR = X, M, X * (1 - M)
+
+base = model.real_attn
+def deep(K):
+    def f(z):
+        h = z
+        for _ in range(K):
+            h = h + base(h)
+        return h - z
+    return f
+
+print(f"{'depth K':>8} | {'naive(attn)':>11} {'AEP(attn)':>10}")
+for K in [1, 2, 3, 4]:
+    model.real_attn = deep(K)
+    r = A.measure(model, names, 1.0, 120, 30, 0.2, 0.02)   # s=1, T2=30 (enough per [3])
+    print(f"{K:>8} | {r['naive']:>11.3f} {r['aep']:>10.3f}")