diff --git a/pyproject.toml b/pyproject.toml
index df3a1dd..e641068 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -9,8 +9,8 @@ license = "MIT"
 requires-python = ">=3.10.15"
 dynamic = ["version"]
 dependencies = [
-    "ezmsg-baseproc>=1.0.2",
-    "ezmsg-sigproc>=2.14.0",
+    "ezmsg-baseproc>=1.3.0",
+    "ezmsg-sigproc>=2.15.0",
     "river>=0.22.0",
     "scikit-learn>=1.6.0",
     "torch>=2.6.0",
diff --git a/src/ezmsg/learn/process/adaptive_linear_regressor.py b/src/ezmsg/learn/process/adaptive_linear_regressor.py
index dbfbb00..3e00909 100644
--- a/src/ezmsg/learn/process/adaptive_linear_regressor.py
+++ b/src/ezmsg/learn/process/adaptive_linear_regressor.py
@@ -11,7 +11,6 @@
     BaseAdaptiveTransformerUnit,
     processor_state,
 )
-from ezmsg.sigproc.sampler import SampleMessage
 from ezmsg.util.messages.axisarray import AxisArray, replace
 
 from ..util import AdaptiveLinearRegressor, RegressorType, get_regressor
@@ -78,30 +77,30 @@ def _reset_state(self, message: AxisArray) -> None:
         #  .template is updated in partial_fit
         pass
 
-    def partial_fit(self, message: SampleMessage) -> None:
-        if np.any(np.isnan(message.sample.data)):
+    def partial_fit(self, message: AxisArray) -> None:
+        if np.any(np.isnan(message.data)):
             return
 
         if self.settings.model_type in [
             AdaptiveLinearRegressor.LINEAR,
             AdaptiveLinearRegressor.LOGISTIC,
         ]:
-            x = pd.DataFrame.from_dict({k: v for k, v in zip(message.sample.axes["ch"].data, message.sample.data.T)})
+            x = pd.DataFrame.from_dict({k: v for k, v in zip(message.axes["ch"].data, message.data.T)})
             y = pd.Series(
-                data=message.trigger.value.data[:, 0],
-                name=message.trigger.value.axes["ch"].data[0],
+                data=message.attrs["trigger"].value.data[:, 0],
+                name=message.attrs["trigger"].value.axes["ch"].data[0],
             )
             self.state.model.learn_many(x, y)
         else:
-            X = message.sample.data
-            if message.sample.get_axis_idx("time") != 0:
-                X = np.moveaxis(X, message.sample.get_axis_idx("time"), 0)
-            self.state.model.partial_fit(X, message.trigger.value.data)
+            X = message.data
+            if message.get_axis_idx("time") != 0:
+                X = np.moveaxis(X, message.get_axis_idx("time"), 0)
+            self.state.model.partial_fit(X, message.attrs["trigger"].value.data)
 
         self.state.template = replace(
-            message.trigger.value,
-            data=np.empty_like(message.trigger.value.data),
-            key=message.trigger.value.key + "_pred",
+            message.attrs["trigger"].value,
+            data=np.empty_like(message.attrs["trigger"].value.data),
+            key=message.attrs["trigger"].value.key + "_pred",
         )
 
     def _process(self, message: AxisArray) -> AxisArray | None:
diff --git a/src/ezmsg/learn/process/linear_regressor.py b/src/ezmsg/learn/process/linear_regressor.py
index 49567f5..873406a 100644
--- a/src/ezmsg/learn/process/linear_regressor.py
+++ b/src/ezmsg/learn/process/linear_regressor.py
@@ -7,7 +7,6 @@
     BaseAdaptiveTransformerUnit,
     processor_state,
 )
-from ezmsg.sigproc.sampler import SampleMessage
 from ezmsg.util.messages.axisarray import AxisArray, replace
 from sklearn.linear_model._base import LinearModel
 
@@ -53,18 +52,18 @@ def _reset_state(self, message: AxisArray) -> None:
         #  .model and .template are initialized in __init__
         pass
 
-    def partial_fit(self, message: SampleMessage) -> None:
-        if np.any(np.isnan(message.sample.data)):
+    def partial_fit(self, message: AxisArray) -> None:
+        if np.any(np.isnan(message.data)):
             return
 
-        X = message.sample.data
-        y = message.trigger.value.data
+        X = message.data
+        y = message.attrs["trigger"].value.data
         # TODO: Resample should provide identical durations.
         self.state.model = self.state.model.fit(X[: y.shape[0]], y[: X.shape[0]])
         self.state.template = replace(
-            message.trigger.value,
+            message.attrs["trigger"].value,
             data=np.array([[]]),
-            key=message.trigger.value.key + "_pred",
+            key=message.attrs["trigger"].value.key + "_pred",
         )
 
     def _process(self, message: AxisArray) -> AxisArray:
diff --git a/src/ezmsg/learn/process/mlp_old.py b/src/ezmsg/learn/process/mlp_old.py
index b059cbf..b8c87d3 100644
--- a/src/ezmsg/learn/process/mlp_old.py
+++ b/src/ezmsg/learn/process/mlp_old.py
@@ -9,7 +9,6 @@
     BaseAdaptiveTransformerUnit,
     processor_state,
 )
-from ezmsg.sigproc.sampler import SampleMessage
 from ezmsg.util.messages.axisarray import AxisArray
 from ezmsg.util.messages.util import replace
 
@@ -134,14 +133,14 @@ def _to_tensor(self, data: np.ndarray) -> torch.Tensor:
         dtype = torch.float32 if self.settings.single_precision else torch.float64
         return torch.tensor(data, dtype=dtype, device=self._state.device)
 
-    def partial_fit(self, message: SampleMessage) -> None:
+    def partial_fit(self, message: AxisArray) -> None:
         self._state.model.train()
 
         # TODO: loss_fn should be determined by setting
         loss_fn = torch.nn.functional.mse_loss
 
-        X = self._to_tensor(message.sample.data)
-        y_targ = self._to_tensor(message.trigger.value)
+        X = self._to_tensor(message.data)
+        y_targ = self._to_tensor(message.attrs["trigger"].value)
 
         with torch.set_grad_enabled(True):
             self._state.model.train()
diff --git a/src/ezmsg/learn/process/refit_kalman.py b/src/ezmsg/learn/process/refit_kalman.py
index bc04d12..6684bc9 100644
--- a/src/ezmsg/learn/process/refit_kalman.py
+++ b/src/ezmsg/learn/process/refit_kalman.py
@@ -8,7 +8,6 @@
     BaseAdaptiveTransformerUnit,
     processor_state,
 )
-from ezmsg.sigproc.sampler import SampleMessage
 from ezmsg.util.messages.axisarray import AxisArray
 from ezmsg.util.messages.util import replace
 
@@ -284,22 +283,22 @@ def _process(self, message: AxisArray) -> AxisArray:
             key=f"{message.key}_filtered" if hasattr(message, "key") else "filtered",
         )
 
-    def partial_fit(self, message: SampleMessage) -> None:
+    def partial_fit(self, message: AxisArray) -> None:
         """
         Perform refitting using externally provided data.
 
-        Expects message.sample.data (neural input) and message.trigger.value as a dict with:
+        Expects message.data (neural input) and message.attrs["trigger"].value as a dict with:
             - Y_state: (n_samples, n_states) array
             - intention_velocity_indices: Optional[int]
             - target_positions: Optional[np.ndarray]
             - cursor_positions: Optional[np.ndarray]
             - hold_flags: Optional[list[bool]]
         """
-        if not hasattr(message, "sample") or not hasattr(message, "trigger"):
+        if "trigger" not in message.attrs:
             raise ValueError("Invalid message format for partial_fit.")
 
-        X = np.array(message.sample.data)
-        values = message.trigger.value
+        X = np.array(message.data)
+        values = message.attrs["trigger"].value
 
         if not isinstance(values, dict) or "Y_state" not in values:
             raise ValueError("partial_fit expects trigger.value to include at least 'Y_state'.")
diff --git a/src/ezmsg/learn/process/rnn.py b/src/ezmsg/learn/process/rnn.py
index ec1f3da..b3cfd62 100644
--- a/src/ezmsg/learn/process/rnn.py
+++ b/src/ezmsg/learn/process/rnn.py
@@ -5,7 +5,6 @@
 import torch
 from ezmsg.baseproc import BaseAdaptiveTransformer, BaseAdaptiveTransformerUnit
 from ezmsg.baseproc.util.profile import profile_subpub
-from ezmsg.sigproc.sampler import SampleMessage
 from ezmsg.util.messages.axisarray import AxisArray
 from ezmsg.util.messages.util import replace
 
@@ -184,18 +183,18 @@ def _train_step(
         if self._state.scheduler is not None:
             self._state.scheduler.step()
 
-    def partial_fit(self, message: SampleMessage) -> None:
+    def partial_fit(self, message: AxisArray) -> None:
         self._state.model.train()
 
-        X = self._to_tensor(message.sample.data)
+        X = self._to_tensor(message.data)
 
         # Add batch dimension if missing
         X, batched = self._ensure_batched(X)
 
         batch_size = X.shape[0]
-        preserve_state = self._maybe_reset_state(message.sample, batch_size)
+        preserve_state = self._maybe_reset_state(message, batch_size)
 
-        y_targ = message.trigger.value
+        y_targ = message.attrs["trigger"].value
         if not isinstance(y_targ, dict):
             y_targ = {"output": y_targ}
         y_targ = {key: self._to_tensor(value) for key, value in y_targ.items()}
diff --git a/src/ezmsg/learn/process/sgd.py b/src/ezmsg/learn/process/sgd.py
index d00b39c..baedab5 100644
--- a/src/ezmsg/learn/process/sgd.py
+++ b/src/ezmsg/learn/process/sgd.py
@@ -5,7 +5,6 @@
 from ezmsg.baseproc import (
     BaseAdaptiveTransformer,
     BaseAdaptiveTransformerUnit,
-    SampleMessage,
     processor_state,
 )
 from ezmsg.util.messages.axisarray import AxisArray
@@ -87,23 +86,23 @@ def _process(self, message: AxisArray) -> ClassifierMessage | None:
             key=message.key,
         )
 
-    def partial_fit(self, message: SampleMessage) -> None:
+    def partial_fit(self, message: AxisArray) -> None:
         if self._hash != 0:
-            self._reset_state(message.sample)
+            self._reset_state(message)
             self._hash = 0
 
-        if np.any(np.isnan(message.sample.data)):
+        if np.any(np.isnan(message.data)):
             return
-        train_sample = message.sample.data.reshape(1, -1)
+        train_sample = message.data.reshape(1, -1)
         if self._state.b_first_train:
             self._state.model.partial_fit(
                 train_sample,
-                [message.trigger.value],
+                [message.attrs["trigger"].value],
                 classes=list(self.settings.label_weights.keys()),
             )
             self._state.b_first_train = False
         else:
-            self._state.model.partial_fit(train_sample, [message.trigger.value])
+            self._state.model.partial_fit(train_sample, [message.attrs["trigger"].value])
 
 
 class SGDDecoder(
diff --git a/src/ezmsg/learn/process/sklearn.py b/src/ezmsg/learn/process/sklearn.py
index 37d0b23..15a4596 100644
--- a/src/ezmsg/learn/process/sklearn.py
+++ b/src/ezmsg/learn/process/sklearn.py
@@ -10,7 +10,6 @@
     BaseAdaptiveTransformerUnit,
     processor_state,
 )
-from ezmsg.sigproc.sampler import SampleMessage
 from ezmsg.util.messages.axisarray import AxisArray
 from ezmsg.util.messages.util import replace
 
@@ -116,25 +115,25 @@ def _reset_state(self, message: AxisArray) -> None:
             # No checkpoint, initialize from scratch
             self._init_model()
 
-    def partial_fit(self, message: SampleMessage) -> None:
-        X = message.sample.data
-        y = message.trigger.value
+    def partial_fit(self, message: AxisArray) -> None:
+        X = message.data
+        y = message.attrs["trigger"].value
         if self._state.model is None:
-            self._reset_state(message.sample)
+            self._reset_state(message)
         if hasattr(self._state.model, "partial_fit"):
             kwargs = {}
             if self.settings.partial_fit_classes is not None:
                 kwargs["classes"] = self.settings.partial_fit_classes
             self._state.model.partial_fit(X, y, **kwargs)
         elif hasattr(self._state.model, "learn_many"):
-            df_X = pd.DataFrame({k: v for k, v in zip(message.sample.axes["ch"].data, message.sample.data.T)})
+            df_X = pd.DataFrame({k: v for k, v in zip(message.axes["ch"].data, message.data.T)})
             name = (
-                message.trigger.value.axes["ch"].data[0]
-                if hasattr(message.trigger.value, "axes") and "ch" in message.trigger.value.axes
+                message.attrs["trigger"].value.axes["ch"].data[0]
+                if hasattr(message.attrs["trigger"].value, "axes") and "ch" in message.attrs["trigger"].value.axes
                 else "target"
             )
             ser_y = pd.Series(
-                data=np.asarray(message.trigger.value.data).flatten(),
+                data=np.asarray(message.attrs["trigger"].value.data).flatten(),
                 name=name,
             )
             self._state.model.learn_many(df_X, ser_y)
diff --git a/src/ezmsg/learn/process/torch.py b/src/ezmsg/learn/process/torch.py
index db62022..ed3ef14 100644
--- a/src/ezmsg/learn/process/torch.py
+++ b/src/ezmsg/learn/process/torch.py
@@ -12,7 +12,6 @@
     processor_state,
 )
 from ezmsg.baseproc.util.profile import profile_subpub
-from ezmsg.sigproc.sampler import SampleMessage
 from ezmsg.util.messages.axisarray import AxisArray
 from ezmsg.util.messages.util import replace
 
@@ -294,13 +293,13 @@ def _reset_state(self, message: AxisArray) -> None:
     def _process(self, message: AxisArray) -> list[AxisArray]:
         return self._common_process(message)
 
-    def partial_fit(self, message: SampleMessage) -> None:
+    def partial_fit(self, message: AxisArray) -> None:
         self._state.model.train()
 
-        X = self._to_tensor(message.sample.data)
+        X = self._to_tensor(message.data)
         X, batched = self._ensure_batched(X)
 
-        y_targ = message.trigger.value
+        y_targ = message.attrs["trigger"].value
         if not isinstance(y_targ, dict):
             y_targ = {"output": y_targ}
         y_targ = {key: self._to_tensor(value) for key, value in y_targ.items()}
diff --git a/src/ezmsg/learn/process/transformer.py b/src/ezmsg/learn/process/transformer.py
index 9784192..b3abeb8 100644
--- a/src/ezmsg/learn/process/transformer.py
+++ b/src/ezmsg/learn/process/transformer.py
@@ -4,7 +4,6 @@
 import torch
 from ezmsg.baseproc import BaseAdaptiveTransformer, BaseAdaptiveTransformerUnit
 from ezmsg.baseproc.util.profile import profile_subpub
-from ezmsg.sigproc.sampler import SampleMessage
 from ezmsg.util.messages.axisarray import AxisArray
 from ezmsg.util.messages.util import replace
 
@@ -125,13 +124,13 @@ def _process(self, message: AxisArray) -> list[AxisArray]:
                 )
             ]
 
-    def partial_fit(self, message: SampleMessage) -> None:
+    def partial_fit(self, message: AxisArray) -> None:
         self._state.model.train()
 
-        X = self._to_tensor(message.sample.data)
+        X = self._to_tensor(message.data)
         X, batched = self._ensure_batched(X)
 
-        y_targ = message.trigger.value
+        y_targ = message.attrs["trigger"].value
         if not isinstance(y_targ, dict):
             y_targ = {"output": y_targ}
         y_targ = {key: self._to_tensor(value) for key, value in y_targ.items()}
diff --git a/tests/unit/test_adaptive_linear_regressor.py b/tests/unit/test_adaptive_linear_regressor.py
index d699f3a..6098aaa 100644
--- a/tests/unit/test_adaptive_linear_regressor.py
+++ b/tests/unit/test_adaptive_linear_regressor.py
@@ -1,6 +1,6 @@
 import numpy as np
 import pytest
-from ezmsg.sigproc.sampler import SampleMessage, SampleTriggerMessage
+from ezmsg.baseproc import SampleTriggerMessage
 from ezmsg.util.messages.axisarray import AxisArray, replace
 
 from ezmsg.learn.process.adaptive_linear_regressor import (
@@ -42,7 +42,7 @@ def test_adaptive_linear_regressor(model_type: str):
         period=(0.0, dur),
         value=value_axarr,
     )
-    samp = SampleMessage(trigger=samp_trig, sample=sig_axarr)
+    samp = replace(sig_axarr, attrs={"trigger": samp_trig})
 
     proc = AdaptiveLinearRegressorTransformer(model_type=model_type)
     _ = proc.send(samp)
diff --git a/tests/unit/test_linear_regressor.py b/tests/unit/test_linear_regressor.py
index 3b7ba8b..0bfbeff 100644
--- a/tests/unit/test_linear_regressor.py
+++ b/tests/unit/test_linear_regressor.py
@@ -1,6 +1,6 @@
 import numpy as np
 import pytest
-from ezmsg.sigproc.sampler import SampleMessage, SampleTriggerMessage
+from ezmsg.baseproc import SampleTriggerMessage
 from ezmsg.util.messages.axisarray import AxisArray, replace
 
 from ezmsg.learn.process.linear_regressor import LinearRegressorTransformer
@@ -40,7 +40,7 @@ def test_linear_regressor(model_type: str):
         period=(0.0, dur),
         value=value_axarr,
     )
-    samp = SampleMessage(trigger=samp_trig, sample=sig_axarr)
+    samp = replace(sig_axarr, attrs={"trigger": samp_trig})
 
     gen = LinearRegressorTransformer(model_type=model_type)
     _ = gen.send(samp)
diff --git a/tests/unit/test_mlp.py b/tests/unit/test_mlp.py
index 1479068..e40e957 100644
--- a/tests/unit/test_mlp.py
+++ b/tests/unit/test_mlp.py
@@ -77,7 +77,8 @@ def test_mlp_checkpoint_io(tmp_path, sample_input, mlp_settings):
 
 
 def test_mlp_partial_fit_learns(sample_input, mlp_settings):
-    from ezmsg.sigproc.sampler import SampleMessage, SampleTriggerMessage
+    from ezmsg.baseproc import SampleTriggerMessage
+    from ezmsg.util.messages.util import replace
 
     proc = TorchModelProcessor(
         model_class="ezmsg.learn.model.mlp.MLP",
@@ -88,13 +89,12 @@ def test_mlp_partial_fit_learns(sample_input, mlp_settings):
     )
     proc(sample_input)
 
-    sample = AxisArray(
-        data=sample_input.data[:1], dims=["time", "ch"], axes=sample_input.axes
-    )
+    sample = AxisArray(data=sample_input.data[:1], dims=["time", "ch"], axes=sample_input.axes)
     target = np.random.randn(1, 5)
 
-    msg = SampleMessage(
-        sample=sample, trigger=SampleTriggerMessage(timestamp=0.0, value=target)
+    msg = replace(
+        sample,
+        attrs={**sample.attrs, "trigger": SampleTriggerMessage(timestamp=0.0, value=target)},
     )
 
     before = [p.detach().clone() for p in proc.state.model.parameters()]
@@ -135,9 +135,9 @@ def test_mlp_hidden_size_integer(sample_input):
         device="cpu",
     )
     proc(sample_input)
-    hidden_layers = [
-        m for m in proc._state.model.modules() if isinstance(m, torch.nn.Linear)
-    ][:-1]  # Exclude the output head
+    hidden_layers = [m for m in proc._state.model.modules() if isinstance(m, torch.nn.Linear)][
+        :-1
+    ]  # Exclude the output head
     assert len(hidden_layers) == 3  # num_layers = 3
     assert hidden_layers[0].in_features == 8
     assert all(layer.out_features == 32 for layer in hidden_layers[:-1])
diff --git a/tests/unit/test_mlp_old.py b/tests/unit/test_mlp_old.py
index 9b6172e..169ec9a 100644
--- a/tests/unit/test_mlp_old.py
+++ b/tests/unit/test_mlp_old.py
@@ -4,8 +4,9 @@
 import pytest
 import torch
 import torch.nn
-from ezmsg.sigproc.sampler import SampleMessage, SampleTriggerMessage
+from ezmsg.baseproc import SampleTriggerMessage
 from ezmsg.util.messages.axisarray import AxisArray
+from ezmsg.util.messages.util import replace
 from sklearn.model_selection import train_test_split
 
 from ezmsg.learn.process.mlp_old import MLPProcessor
@@ -146,7 +147,10 @@ def xy_gen(set: int = 0):
             template.data[:] = X  # This would fail if n_samps / batch_size had a remainder.
             template.axes["time"].offset = ts
             if set == 0:
-                yield SampleMessage(trigger=SampleTriggerMessage(timestamp=ts, value=y), sample=template)
+                yield replace(
+                    template,
+                    attrs={**template.attrs, "trigger": SampleTriggerMessage(timestamp=ts, value=y)},
+                )
             else:
                 yield template, y
 
@@ -167,14 +171,15 @@ def xy_gen(set: int = 0):
     result = []
     train_loss = []
     for sample_msg in xy_gen(set=0):
-        # Naive closed-loop inference
-        result.append(proc(sample_msg.sample))
+        # Naive closed-loop inference — strip trigger attrs before inference
+        plain_msg = replace(sample_msg, attrs={})
+        result.append(proc(plain_msg))
 
         # Collect the loss to see if it decreases with training.
         train_loss.append(
             torch.nn.MSELoss()(
                 torch.tensor(result[-1].data),
-                torch.tensor(sample_msg.trigger.value.reshape(-1, 1), dtype=torch.float32),
+                torch.tensor(sample_msg.attrs["trigger"].value.reshape(-1, 1), dtype=torch.float32),
             ).item()
         )
 
diff --git a/tests/unit/test_refit_kalman.py b/tests/unit/test_refit_kalman.py
index bb3d83a..a4ea4ee 100644
--- a/tests/unit/test_refit_kalman.py
+++ b/tests/unit/test_refit_kalman.py
@@ -4,6 +4,7 @@
 
 import numpy as np
 import pytest
+from ezmsg.baseproc import SampleTriggerMessage
 from ezmsg.util.messages.axisarray import AxisArray
 
 from ezmsg.learn.process.refit_kalman import (
@@ -299,12 +300,6 @@ def test_partial_fit_functionality(create_test_message, checkpoint_file):
     H_initial = checkpoint_data["H_observation_matrix"]
     Q_initial = checkpoint_data["Q_measurement_noise_covariance"]
 
-    # Create a mock SampleMessage with the expected structure
-    class MockSampleMessage:
-        def __init__(self, neural_data, trigger_value):
-            self.sample = type("obj", (object,), {"data": neural_data})()
-            self.trigger = type("obj", (object,), {"value": trigger_value})()
-
     # Create test data
     neural_data = np.array([[0.1, 0.2], [0.3, 0.4], [0.5, 0.6]])  # 3 samples, 2 channels
     trigger_value = {
@@ -315,8 +310,12 @@ def __init__(self, neural_data, trigger_value):
         "hold_flags": [False, False, False],
     }
 
-    mock_message = MockSampleMessage(neural_data, trigger_value)
-    processor.partial_fit(mock_message)
+    sample_msg = AxisArray(
+        data=neural_data,
+        dims=["time", "ch"],
+        attrs={"trigger": SampleTriggerMessage(timestamp=0.0, value=trigger_value)},
+    )
+    processor.partial_fit(sample_msg)
 
     assert not np.allclose(H_initial, processor._state.model.H_observation_matrix)
     assert not np.allclose(Q_initial, processor._state.model.Q_measurement_noise_covariance)
diff --git a/tests/unit/test_rnn.py b/tests/unit/test_rnn.py
index f490d20..66cf42c 100644
--- a/tests/unit/test_rnn.py
+++ b/tests/unit/test_rnn.py
@@ -5,8 +5,9 @@
 import pytest
 import torch
 import torch.nn
-from ezmsg.sigproc.sampler import SampleMessage, SampleTriggerMessage
+from ezmsg.baseproc import SampleTriggerMessage
 from ezmsg.util.messages.axisarray import AxisArray
+from ezmsg.util.messages.util import replace
 
 from ezmsg.learn.process.rnn import RNNProcessor
 
@@ -107,9 +108,7 @@ def test_rnn_process(rnn_type, simple_message):
     # We don't pass in the hx state so it should be initialized to zeros, same as in the first call to proc.
     in_tensor = torch.tensor(simple_message.data[None, ...], dtype=torch.float32)
     with torch.no_grad():
-        expected_result = (
-            proc.state.model(in_tensor)[0]["output"].cpu().numpy().squeeze(0)
-        )
+        expected_result = proc.state.model(in_tensor)[0]["output"].cpu().numpy().squeeze(0)
     assert np.allclose(output.data, expected_result)
 
 
@@ -139,9 +138,9 @@ def test_rnn_partial_fit(simple_message):
 
     target_shape = (simple_message.data.shape[0], output_size)
     target_value = np.ones(target_shape, dtype=np.float32)
-    sample_message = SampleMessage(
-        trigger=SampleTriggerMessage(timestamp=0.0, value=target_value),
-        sample=simple_message,
+    sample_message = replace(
+        simple_message,
+        attrs={**simple_message.attrs, "trigger": SampleTriggerMessage(timestamp=0.0, value=target_value)},
     )
 
     proc(sample_message)
@@ -149,9 +148,7 @@ def test_rnn_partial_fit(simple_message):
     assert not proc.state.model.training
     updated_weights = [p.detach() for p in proc.state.model.parameters()]
 
-    assert any(
-        not torch.equal(w0, w1) for w0, w1 in zip(initial_weights, updated_weights)
-    )
+    assert any(not torch.equal(w0, w1) for w0, w1 in zip(initial_weights, updated_weights))
 
 
 def test_rnn_checkpoint_save_load(simple_message):
@@ -201,9 +198,7 @@ def test_rnn_checkpoint_save_load(simple_message):
 
         for key in state_dict1:
             assert key in state_dict2, f"Missing key {key} in loaded state_dict"
-            assert torch.equal(state_dict1[key], state_dict2[key]), (
-                f"Mismatch in parameter {key}"
-            )
+            assert torch.equal(state_dict1[key], state_dict2[key]), f"Mismatch in parameter {key}"
 
     finally:
         # Ensure the temporary file is deleted
@@ -244,20 +239,21 @@ def test_rnn_partial_fit_multiloss(simple_message):
         dtype=torch.long,
     )
 
-    sample_message = SampleMessage(
-        trigger=SampleTriggerMessage(
-            timestamp=0.0,
-            value={"traj": traj_target, "state": state_target},
-        ),
-        sample=simple_message,
+    sample_message = replace(
+        simple_message,
+        attrs={
+            **simple_message.attrs,
+            "trigger": SampleTriggerMessage(
+                timestamp=0.0,
+                value={"traj": traj_target, "state": state_target},
+            ),
+        },
     )
 
     proc.partial_fit(sample_message)
 
     updated_weights = [p.detach() for p in proc.state.model.parameters()]
-    assert any(
-        not torch.equal(w0, w1) for w0, w1 in zip(initial_weights, updated_weights)
-    )
+    assert any(not torch.equal(w0, w1) for w0, w1 in zip(initial_weights, updated_weights))
 
 
 @pytest.mark.parametrize(
@@ -269,9 +265,7 @@ def test_rnn_partial_fit_multiloss(simple_message):
         ("auto", 0.05, 0.1, False),  # overlapping → reset
     ],
 )
-def test_rnn_preserve_state(
-    preserve_state_across_windows, win_stride, win_len, should_preserve
-):
+def test_rnn_preserve_state(preserve_state_across_windows, win_stride, win_len, should_preserve):
     hidden_size = 16
     num_layers = 1
     output_size = 2
diff --git a/tests/unit/test_sgd.py b/tests/unit/test_sgd.py
index 0785224..b497e28 100644
--- a/tests/unit/test_sgd.py
+++ b/tests/unit/test_sgd.py
@@ -1,5 +1,5 @@
 import numpy as np
-from ezmsg.sigproc.sampler import SampleMessage, SampleTriggerMessage
+from ezmsg.baseproc import SampleTriggerMessage
 from ezmsg.util.messages.axisarray import AxisArray
 
 from ezmsg.learn.process.sgd import SGDDecoderSettings, SGDDecoderTransformer
@@ -13,9 +13,10 @@ def test_sgd():
         data = np.random.normal(scale=0.05, size=(3, 2, 1))
         data[time_idx[label] : time_idx[label] + 1, 0, 0] += 1.0
         samples.append(
-            SampleMessage(
-                trigger=SampleTriggerMessage(timestamp=len(samples), period=None, value=label),
-                sample=AxisArray(data=data, dims=["time", "ch", "freq"]),
+            AxisArray(
+                data=data,
+                dims=["time", "ch", "freq"],
+                attrs={"trigger": SampleTriggerMessage(timestamp=len(samples), period=None, value=label)},
             )
         )
 
diff --git a/tests/unit/test_sklearn.py b/tests/unit/test_sklearn.py
index 398d335..ca32c72 100644
--- a/tests/unit/test_sklearn.py
+++ b/tests/unit/test_sklearn.py
@@ -1,7 +1,8 @@
 import numpy as np
 import pytest
-from ezmsg.sigproc.sampler import SampleMessage, SampleTriggerMessage
+from ezmsg.baseproc import SampleTriggerMessage
 from ezmsg.util.messages.axisarray import AxisArray
+from ezmsg.util.messages.util import replace
 
 from ezmsg.learn.process.sklearn import SklearnModelProcessor
 
@@ -83,9 +84,9 @@ def test_partial_fit_supported_models(
     proc = SklearnModelProcessor(**settings_kwargs)
     proc._reset_state(input_axisarray)
 
-    sample_msg = SampleMessage(
-        sample=input_axisarray,
-        trigger=SampleTriggerMessage(timestamp=0.0, value=labels),
+    sample_msg = replace(
+        input_axisarray,
+        attrs={**input_axisarray.attrs, "trigger": SampleTriggerMessage(timestamp=0.0, value=labels)},
     )
 
     proc.partial_fit(sample_msg)
@@ -96,9 +97,9 @@ def test_partial_fit_supported_models(
 def test_partial_fit_unsupported_model(input_axisarray, labels_regression):
     proc = SklearnModelProcessor(model_class="sklearn.linear_model.Ridge")
     proc._reset_state(input_axisarray)
-    sample_msg = SampleMessage(
-        sample=input_axisarray,
-        trigger=SampleTriggerMessage(timestamp=0.0, value=labels_regression),
+    sample_msg = replace(
+        input_axisarray,
+        attrs={**input_axisarray.attrs, "trigger": SampleTriggerMessage(timestamp=0.0, value=labels_regression)},
     )
     with pytest.raises(NotImplementedError, match="partial_fit"):
         proc.partial_fit(sample_msg)
@@ -108,9 +109,9 @@ def test_partial_fit_changes_model(input_axisarray, labels_regression):
     proc = SklearnModelProcessor(model_class="sklearn.linear_model.SGDRegressor")
     proc._reset_state(input_axisarray)
 
-    sample_msg = SampleMessage(
-        sample=input_axisarray,
-        trigger=SampleTriggerMessage(timestamp=0.0, value=labels_regression),
+    sample_msg = replace(
+        input_axisarray,
+        attrs={**input_axisarray.attrs, "trigger": SampleTriggerMessage(timestamp=0.0, value=labels_regression)},
     )
 
     proc.partial_fit(sample_msg)
@@ -127,9 +128,7 @@ def test_model_save_and_load(tmp_path, input_axisarray):
     checkpoint_path = tmp_path / "model_checkpoint.pkl"
     proc.save_checkpoint(str(checkpoint_path))
 
-    new_proc = SklearnModelProcessor(
-        model_class="sklearn.linear_model.Ridge", checkpoint_path=str(checkpoint_path)
-    )
+    new_proc = SklearnModelProcessor(model_class="sklearn.linear_model.Ridge", checkpoint_path=str(checkpoint_path))
     new_proc._reset_state(input_axisarray)
     assert new_proc._state.model is not None
 
diff --git a/tests/unit/test_torch.py b/tests/unit/test_torch.py
index 6d36228..6fb0789 100644
--- a/tests/unit/test_torch.py
+++ b/tests/unit/test_torch.py
@@ -5,8 +5,9 @@
 import numpy as np
 import pytest
 import torch
-from ezmsg.sigproc.sampler import SampleMessage, SampleTriggerMessage
+from ezmsg.baseproc import SampleTriggerMessage
 from ezmsg.util.messages.axisarray import AxisArray
+from ezmsg.util.messages.util import replace
 
 from ezmsg.learn.process.torch import TorchModelProcessor
 
@@ -185,9 +186,9 @@ def test_partial_fit_changes_weights(batch_message, device):
         },
     )
 
-    msg = SampleMessage(
-        sample=sample,
-        trigger=SampleTriggerMessage(timestamp=0.0, value=y),
+    msg = replace(
+        sample,
+        attrs={"trigger": SampleTriggerMessage(timestamp=0.0, value=y)},
     )
 
     proc(sample)  # run forward pass once to init model
@@ -318,14 +319,11 @@ def test_multihead_partial_fit_with_loss_dict(batch_message, device):
         "head_a": np.random.randn(1, 2),
         "head_b": np.random.randn(1, 3),
     }
-    sample = AxisArray(
+    msg = AxisArray(
         data=batch_message.data[:1],
         dims=["time", "ch"],
         axes=batch_message.axes,
-    )
-    msg = SampleMessage(
-        sample=sample,
-        trigger=SampleTriggerMessage(timestamp=0.0, value=y_targ),
+        attrs={"trigger": SampleTriggerMessage(timestamp=0.0, value=y_targ)},
     )
 
     before_a = proc._state.model.head_a.weight.clone()
@@ -360,14 +358,11 @@ def test_partial_fit_with_loss_weights(batch_message, device):
         "head_a": np.random.randn(1, 2),
         "head_b": np.random.randn(1, 3),
     }
-    sample = AxisArray(
+    msg = AxisArray(
         data=batch_message.data[:1],
         dims=["time", "ch"],
         axes=batch_message.axes,
-    )
-    msg = SampleMessage(
-        sample=sample,
-        trigger=SampleTriggerMessage(timestamp=0.0, value=y_targ),
+        attrs={"trigger": SampleTriggerMessage(timestamp=0.0, value=y_targ)},
     )
 
     # Expect no error, and just run once
diff --git a/tests/unit/test_transformer.py b/tests/unit/test_transformer.py
index 71952b0..73efd6c 100644
--- a/tests/unit/test_transformer.py
+++ b/tests/unit/test_transformer.py
@@ -5,8 +5,9 @@
 import pytest
 import torch
 import torch.nn
-from ezmsg.sigproc.sampler import SampleMessage, SampleTriggerMessage
+from ezmsg.baseproc import SampleTriggerMessage
 from ezmsg.util.messages.axisarray import AxisArray
+from ezmsg.util.messages.util import replace
 
 from ezmsg.learn.process.transformer import TransformerProcessor
 
@@ -138,9 +139,9 @@ def test_transformer_partial_fit(simple_message, decoder_layers):
 
     target_shape = (simple_message.data.shape[0], output_size)
     target_value = np.ones(target_shape, dtype=np.float32)
-    sample_message = SampleMessage(
-        trigger=SampleTriggerMessage(timestamp=0.0, value=target_value),
-        sample=simple_message,
+    sample_message = replace(
+        simple_message,
+        attrs={**simple_message.attrs, "trigger": SampleTriggerMessage(timestamp=0.0, value=target_value)},
     )
 
     proc.partial_fit(sample_message)
@@ -149,9 +150,7 @@ def test_transformer_partial_fit(simple_message, decoder_layers):
     assert proc.state.tgt_cache is None
     updated_weights = [p.detach() for p in proc.state.model.parameters()]
 
-    assert any(
-        not torch.equal(w0, w1) for w0, w1 in zip(initial_weights, updated_weights)
-    )
+    assert any(not torch.equal(w0, w1) for w0, w1 in zip(initial_weights, updated_weights))
 
 
 def test_transformer_checkpoint_save_load(simple_message):
@@ -201,9 +200,7 @@ def test_transformer_checkpoint_save_load(simple_message):
 
         for key in state_dict1:
             assert key in state_dict2, f"Missing key {key} in loaded state_dict"
-            assert torch.equal(state_dict1[key], state_dict2[key]), (
-                f"Mismatch in parameter {key}"
-            )
+            assert torch.equal(state_dict1[key], state_dict2[key]), f"Mismatch in parameter {key}"
 
     finally:
         # Ensure the temporary file is deleted
@@ -244,20 +241,21 @@ def test_transformer_partial_fit_multiloss(simple_message):
         dtype=torch.long,
     )
 
-    sample_message = SampleMessage(
-        trigger=SampleTriggerMessage(
-            timestamp=0.0,
-            value={"traj": traj_target, "state": state_target},
-        ),
-        sample=simple_message,
+    sample_message = replace(
+        simple_message,
+        attrs={
+            **simple_message.attrs,
+            "trigger": SampleTriggerMessage(
+                timestamp=0.0,
+                value={"traj": traj_target, "state": state_target},
+            ),
+        },
     )
 
     proc.partial_fit(sample_message)
 
     updated_weights = [p.detach() for p in proc.state.model.parameters()]
-    assert any(
-        not torch.equal(w0, w1) for w0, w1 in zip(initial_weights, updated_weights)
-    )
+    assert any(not torch.equal(w0, w1) for w0, w1 in zip(initial_weights, updated_weights))
 
 
 def test_autoregressive_cache_behavior(simple_message):