diff --git a/backbones.py b/backbones.py
index ea61077..58d76cd 100644
--- a/backbones.py
+++ b/backbones.py
@@ -111,7 +111,10 @@ class TemporalAttention(nn.Module):
 
         # Layer-specific projection from shared RBF basis activations to per-head attention bias.
         self.rbf_proj = nn.Linear(n_rbf_bases, n_head, bias=False)
-        self.time_bias_scale = nn.Parameter(torch.tensor(0.0))
+        # Keep the initial RBF attention bias exactly zero through the
+        # zero-initialized projection, while leaving that projection with a
+        # live gradient from the first optimization step.
+        self.time_bias_scale = nn.Parameter(torch.tensor(1.0))
 
         self.resid_drop = nn.Dropout(dropout)
         self.reset_parameters()
diff --git a/test_temporal_attention.py b/test_temporal_attention.py
new file mode 100644
index 0000000..a1a3475
--- /dev/null
+++ b/test_temporal_attention.py
@@ -0,0 +1,46 @@
+import unittest
+
+import torch
+
+from backbones import TemporalAttention
+
+
+class TemporalAttentionTest(unittest.TestCase):
+    def test_zero_rbf_bias_has_live_projection_gradient(self) -> None:
+        torch.manual_seed(0)
+        attention = TemporalAttention(
+            n_embd=12,
+            n_head=3,
+            use_time_rope=False,
+            use_rbf_bias=True,
+        )
+        features = torch.randn(2, 4, 4, 16)
+        target = torch.randn(2, 4, 4, 3)
+
+        initial_bias = (
+            attention.time_bias_scale.tanh()
+            * attention.rbf_proj(features)
+        )
+        torch.testing.assert_close(initial_bias, torch.zeros_like(initial_bias))
+
+        (initial_bias * target).sum().backward()
+        projection_grad = attention.rbf_proj.weight.grad
+        self.assertIsNotNone(projection_grad)
+        self.assertGreater(projection_grad.abs().sum().item(), 0.0)
+
+        with torch.no_grad():
+            attention.rbf_proj.weight.add_(projection_grad, alpha=-1e-3)
+        attention.zero_grad(set_to_none=True)
+        updated_bias = (
+            attention.time_bias_scale.tanh()
+            * attention.rbf_proj(features)
+        )
+        (updated_bias * target).sum().backward()
+
+        scale_grad = attention.time_bias_scale.grad
+        self.assertIsNotNone(scale_grad)
+        self.assertGreater(scale_grad.abs().item(), 0.0)
+
+
+if __name__ == "__main__":
+    unittest.main()