diff --git a/experiments/callbacks/image_grid_val_visualization.py b/experiments/callbacks/image_grid_val_visualization.py
index 41e1e530..6493c960 100644
--- a/experiments/callbacks/image_grid_val_visualization.py
+++ b/experiments/callbacks/image_grid_val_visualization.py
@@ -138,9 +138,22 @@ def _log_image_grid(self, trainer: pl.Trainer, pl_module: pl.LightningModule, ev
         if condition is not None:
             condition = condition.to(device)
 
-        # Forward pass
+        # Forward pass.  Lightning's precision plugin only wraps
+        # training_step/validation_step/test_step in autocast — it does NOT
+        # wrap callbacks.  For bf16/fp16-mixed runs the model is trained under
+        # autocast and the forward output can differ catastrophically between
+        # autocast-ON and autocast-OFF (see Hyena+SIREN at large ω₀).  Enter
+        # the precision plugin's forward_context() so visualisations reflect
+        # the actual model output.  ``.float()`` afterwards converts any bf16
+        # tensors back to fp32 (numpy/matplotlib can't handle bf16).
         pl_module.eval()
-        preds = pl_module({"input": x, "condition": condition})["logits"]
+        forward_context = getattr(trainer.precision_plugin, "forward_context", None)
+        ctx = forward_context() if callable(forward_context) else torch.amp.autocast(
+            device_type="cuda" if x.is_cuda else "cpu", enabled=False
+        )
+        with ctx:
+            preds = pl_module({"input": x, "condition": condition})["logits"]
+        preds = preds.float()
 
         # Convert to NCHW images, supporting flattened inputs.
         x_nchw = self._as_nchw_images(x)
@@ -514,9 +527,15 @@ def _log_volume_grid(self, trainer: pl.Trainer, pl_module: pl.LightningModule, e
         if condition is not None:
             condition = condition.to(device)
 
-        # Forward pass
+        # Forward pass — see ValidationImageGridCallback above for rationale.
         pl_module.eval()
-        preds = pl_module({"input": x, "condition": condition})["logits"]
+        forward_context = getattr(trainer.precision_plugin, "forward_context", None)
+        ctx = forward_context() if callable(forward_context) else torch.amp.autocast(
+            device_type="cuda" if x.is_cuda else "cpu", enabled=False
+        )
+        with ctx:
+            preds = pl_module({"input": x, "condition": condition})["logits"]
+        preds = preds.float()
 
         # Limit samples
         n = min(self.num_samples, x.shape[0])
diff --git a/experiments/callbacks/sequence_visualization_1d.py b/experiments/callbacks/sequence_visualization_1d.py
index 51a0c8fe..118fd72b 100644
--- a/experiments/callbacks/sequence_visualization_1d.py
+++ b/experiments/callbacks/sequence_visualization_1d.py
@@ -223,9 +223,27 @@ def _log_visualization(self, trainer: pl.Trainer, pl_module: pl.LightningModule,
         if condition is not None:
             condition = condition.to(device)
 
-        # Forward pass
+        # Forward pass.  Lightning's precision plugin only wraps
+        # ``training_step``/``validation_step``/``test_step`` in autocast — it
+        # does NOT wrap callbacks like this one.  For bf16/fp16-mixed runs the
+        # model is trained under autocast, and the forward output can differ
+        # *significantly* (catastrophically, for SIRENs with large ω₀) between
+        # autocast-ON and autocast-OFF.  We therefore explicitly enter the
+        # precision plugin's forward context so the visualisation reflects what
+        # the model actually produces during validation/inference.
         pl_module.eval()
-        preds = pl_module({"input": x, "condition": condition})["logits"]
+        forward_context = getattr(trainer.precision_plugin, "forward_context", None)
+        if callable(forward_context):
+            ctx = forward_context()
+        else:
+            ctx = torch.amp.autocast(
+                device_type="cuda" if x.is_cuda else "cpu", enabled=False
+            )
+        with ctx:
+            preds = pl_module({"input": x, "condition": condition})["logits"]
+        # bf16/fp16 outputs are not supported by ``torch.Tensor.numpy()`` —
+        # cast back to fp32 before any downstream matplotlib/numpy use.
+        preds = preds.float()
 
         # x: [B, L, C_in], y: [B, segment_length, C_out], preds: [B, segment_length, C_out]
         n = min(self.num_samples, x.shape[0])