diff --git a/src/h3_blackwell_runtime/vae_encoder.py b/src/h3_blackwell_runtime/vae_encoder.py
index c2aa87a..9aa6f9c 100644
--- a/src/h3_blackwell_runtime/vae_encoder.py
+++ b/src/h3_blackwell_runtime/vae_encoder.py
@@ -41,24 +41,23 @@ NIN_LEVELS = frozenset({1, 3, 5})
 DOWNSAMPLE_LEVELS = frozenset({0, 1, 2, 3})
 
 
-def _causal_front_padding(t_in: int, kernel: int, stride: int) -> int:
-    """Front-only zero padding: ``kernel-1-(ceil(t_in/stride)-1)*stride``."""
-    t_out = math.ceil(t_in / stride)
-    return max(0, kernel - 1 - (t_out - 1) * stride)
-
-
 def _causal_conv3d(x, weight, bias, *, kernel_size, stride, spatial_padding):
-    """Reflect-spatial / causal-temporal 3D conv (single-frame -> truncated taps)."""
+    """Reflect-spatial / causal-temporal 3D conv (matches upstream_vae.CausalConv3d).
+
+    Front-only zero temporal padding of ``(kernel_size - 1)`` (two taps on each
+    side) whenever ``spatial_padding > 0`` (the reference pads with ``(k//2,
+    k//2, 0)`` in time), no temporal padding otherwise.  A single input frame
+    has its temporal taps truncated instead of convolving zero frames
+    (``autopad="causal_zero"``) so a keyframe never leaks zero into the latent.
+    """
     t = x.shape[2]
-    if t == 1:
-        if spatial_padding > 0:
-            half = (kernel_size - 1) // 2
-            kernel = weight[:, :, half : half + 2 * spatial_padding + 1]
-            return F.conv3d(x, kernel, bias, (1, stride[1], stride[2]), (0, spatial_padding, spatial_padding))
+    if t == 1 and spatial_padding > 0:
+        half = (kernel_size - 1) // 2
+        kernel = weight[:, :, half : half + 2 * spatial_padding + 1]
+        return F.conv3d(x, kernel, bias, (1, stride[1], stride[2]), (0, spatial_padding, spatial_padding))
+    if spatial_padding > 0:
+        x = F.pad(x, (0, 0, 0, 0, kernel_size - 1, 0))
         return F.conv3d(x, weight, bias, stride, (0, spatial_padding, spatial_padding))
-    front = _causal_front_padding(t, kernel_size, stride[0])
-    if front > 0:
-        x = F.pad(x, (0, 0, 0, 0, front, 0))
     return F.conv3d(x, weight, bias, stride, (0, spatial_padding, spatial_padding))