diff --git a/docs/trials_table_mapping.md b/docs/trials_table_mapping.md
index d53a059..7e1d457 100644
--- a/docs/trials_table_mapping.md
+++ b/docs/trials_table_mapping.md
@@ -42,21 +42,6 @@ Columns are grouped by the raw source they map from.
 | `block_beta`, `block_duration`, `block_min`, `block_max` | `block_length` |
 | `delay_beta`, `delay_duration`, `delay_min`, `delay_max` | `quiescent_duration_key` (scalar distribution, so no beta/min/max) |
 
-### From `task_logic_input` (under `task_parameters`)
-
-| Trials column | Source field |
-| --- | --- |
-| `reward_size_left` | `task_parameters.reward_size.left_value_volume` — the reward volume (uL) at the left port. |
-| `reward_size_right` | `task_parameters.reward_size.right_value_volume` — the reward volume (uL) at the right port. |
-
-> **Note:** `reward_size` is read from the task parameters, not the trial
-> generator, so it is populated even when no summarising generator is resolved.
-> The acquisition system can in principle vary reward size per trial, but the
-> current data format only exposes a single session-level value, so these
-> columns are constant across trials. They are **required** (non-nullable): a
-> missing `TaskLogic` stream raises rather than silently producing null reward
-> sizes when there are trials to build.
-
 ### From `TrialMetrics.json` (`SoftwareEvents` stream)
 
 | Trials column | Mapping |
@@ -81,6 +66,8 @@ Columns are grouped by the raw source they map from.
 | `response_duration` | `response_deadline_duration`. |
 | `reward_consumption_duration` | `Trial -> reward_consumption_duration`. |
 | `reward_probabilityL` / `reward_probabilityR` | The **block** probability from `Trial -> metadata -> p_reward_left` / `p_reward_right`. The top-level `trial.p_reward_left` / `p_reward_right` is the per-trial probability, not the block probability, so it is not used here. `None` when the trial or its metadata is missing. |
+| `reward_size_left` | `Trial -> reward_size.left` — the reward volume (uL) at the left port. Defaults to `2.0` when not set on the trial. `None` when the trial is missing. |
+| `reward_size_right` | `Trial -> reward_size.right` — the reward volume (uL) at the right port. Defaults to `2.0` when not set on the trial. `None` when the trial is missing. |
 | `rewarded_historyL` / `rewarded_historyR` | Filter `is_rewarded == True`, then on `is_right_choice`. |
 
 ### From `TrialGeneratorSpec.json` (`SoftwareEvents` stream)
@@ -88,7 +75,7 @@ Columns are grouped by the raw source they map from.
 | Trials column | Mapping |
 | --- | --- |
 | `base_reward_probability_sum` | If `type == "CoupledTrialGenerator"`, look at `reward_probability_parameters`. |
-| `min_reward_each_block` | Present when `type == "CoupledTrialGenerator"`; otherwise `None`. |
+| `min_reward_each_block` | Present when `type == "CoupledWarmupTrialGenerator"` (has `min_block_reward`); otherwise `None`. |
 
 ### From `QuiescentPeriod.json` (`SoftwareEvents` stream)
 
@@ -123,11 +110,11 @@ seconds, far too coarse for the ~tens-of-ms valve pulse.
 | --- | --- |
 | `goCue_start_time` | `PlaySoundOrFrequency` `WRITE` message. |
 
-### From `InitialManipulatorPosition` (software event)
+### From `HarpManipulator` `AccumulatedSteps` (+ `InputSchemas.Rig`)
 
 | Trials column | Mapping |
 | --- | --- |
-| `lickspout_positions` | `data` field. |
+| `lickspout_position_x` / `y1` / `y2` / `z` | Per-motor cumulative microstep count from the `AccumulatedSteps` stream, converted to millimetres via the rig manipulator calibration (`full_step_to_mm / microstep_resolution`) and re-referenced to the session-start position (displacement **relative to session start**, mm). The manipulator is a continuously-sampled hardware value, so — like the go cue — each trial takes the sample within its `[start_time, stop_time)` window nearest the start. `Motor{i}` drives `Axis(i + 1)` (X, Y1, Y2, Z). `None` when no sample falls in the trial window. The rig and `AccumulatedSteps` streams are required inputs (`build` raises if either is missing with trials present). |
 
 ### From `trainer_state.json` and `acquisition.json` (autoTrain — can be disregarded)
 
@@ -155,3 +142,5 @@ These were mapped during exploration but are no longer in scope:
 | 2026-06-17 | `auto_waterL` / `auto_waterR` now encode no auto-response (`is_auto_reward_right` is `None`) and missing trials as `0` instead of `NULL`. The columns are non-nullable (`int`, default `0`). |
 | 2026-06-20 | Added `reward_size_left` / `reward_size_right` (reward volume in uL) from `task_parameters.reward_size`, and `side_bias` from the per-trial `TrialMetrics` event (`bias` field). |
 | 2026-06-20 | `reward_probabilityL` / `reward_probabilityR` now read the block probability from `trial.metadata.p_reward_left` / `p_reward_right` instead of the top-level per-trial `trial.p_reward_left` / `p_reward_right`. |
+| 2026-07-22 | `lickspout_position_x` / `y1` / `y2` / `z` now derive from the `HarpManipulator` `AccumulatedSteps` stream (microsteps → mm via the `InputSchemas.Rig` manipulator calibration, `full_step_to_mm / microstep_resolution`), sampled per trial via the closest sample in the `[start_time, stop_time)` window and re-referenced to the session-start position (displacement relative to session start, mm), replacing the static `InitialManipulatorPosition` software event. `Motor{i}` maps to `Axis(i + 1)` (X, Y1, Y2, Z). The rig and `AccumulatedSteps` streams are required when there are trials (`build` raises if either is missing). Column descriptions corrected from `um` to `mm`. |
+| 2026-07-24 | `reward_size_left` / `reward_size_right` moved from session-level `task_parameters.reward_size` to per-trial `Trial.reward_size` (fields `.left` / `.right`). The columns are now nullable — `None` when the trial is missing. A missing `TaskLogic` stream no longer raises; session distribution columns are simply null. `min_reward_each_block` moved from `CoupledTrialGenerator` to `CoupledWarmupTrialGenerator`. |
diff --git a/pyproject.toml b/pyproject.toml
index b57ebc2..ddf7ef1 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -17,7 +17,7 @@ readme = "README.md"
 version = "0.1.0"
 
 dependencies = [
-    "aind-behavior-dynamic-foraging[data] @ git+https://github.com/AllenNeuralDynamics/Aind.Behavior.DynamicForaging.git@baab12133b22f599c1ba0583260eca9eca216cc0",
+    "aind-behavior-dynamic-foraging[data] @ git+https://github.com/AllenNeuralDynamics/Aind.Behavior.DynamicForaging.git@ac5ddbf909c9375b9e8875d6a5f90796cfa98653",
     "ipykernel",
 ]
 
@@ -37,7 +37,7 @@ full = [
 
 [dependency-groups]
 dev = [
-    'ruff',
+    'ruff<0.16',
     'coverage',
     'interrogate',
     'pytest',
diff --git a/src/dynamic_foraging_processing/nwb/acquisition/acquisition_builder.py b/src/dynamic_foraging_processing/nwb/acquisition/acquisition_builder.py
index 047bd48..a63fda2 100644
--- a/src/dynamic_foraging_processing/nwb/acquisition/acquisition_builder.py
+++ b/src/dynamic_foraging_processing/nwb/acquisition/acquisition_builder.py
@@ -106,8 +106,7 @@ def get_lick_times(self, device: str, stream_name: str, port: str) -> np.ndarray
         ``HarpBehavior``/``DigitalInputState``, with left licks on ``DIPort0``
         and right licks on ``DIPort1``. The lickometer board exposes each side
         as its own device (``HarpLickometerLeft`` / ``HarpLickometerRight``)
-        with a ``LickState`` stream and a ``Channel0`` column. A lick time is a
-        timestamp at which the selected column's digital input is high.
+        with a ``LickState`` stream and a ``Channel0`` column.
 
         Parameters
         ----------
@@ -131,6 +130,7 @@ def get_lick_times(self, device: str, stream_name: str, port: str) -> np.ndarray
             data = self.loader.dataset.at("Behavior").at(device).at(stream_name).load().data
         except (KeyError, FileNotFoundError):
             return np.array([])
+        data = data[data["MessageType"] == "EVENT"]
         licks = data[data[port].fillna(False).astype(bool)]
         return licks.index.to_numpy()
 
diff --git a/src/dynamic_foraging_processing/pipeline/_pipeline.py b/src/dynamic_foraging_processing/pipeline/_pipeline.py
index 0796cdc..dfa2d6f 100644
--- a/src/dynamic_foraging_processing/pipeline/_pipeline.py
+++ b/src/dynamic_foraging_processing/pipeline/_pipeline.py
@@ -26,6 +26,7 @@
 import numpy as np
 import pandas as pd
 import pynwb
+from aind_data_schema.components.identifiers import DataAsset
 from aind_data_schema.core.processing import (
     Code,
     DataProcess,
@@ -279,8 +280,17 @@ def _write_processing(
                     experimenters=["Alex Piet", "Micah Woodard", "Bruno Cruz", "Arjun Sridhar"],
                     start_date_time=start_date_time,
                     end_date_time=end_date_time,
+                    pipeline_name="dynamic-foraging-processing-pipeline",
                 )
-            ]
+            ],
+            pipelines=[
+                Code(
+                    url=_CODE_URL,
+                    version=_PACKAGE_VERSION,
+                    name="dynamic-foraging-processing-pipeline",
+                    input_data=[DataAsset(name=Path(self.loader.path).stem)],
+                )
+            ],
         )
         processing.write_standard_file(output_directory=Path(output_path))
 
diff --git a/src/dynamic_foraging_processing/processing/_trial_table.py b/src/dynamic_foraging_processing/processing/_trial_table.py
index b83fb63..73963ac 100644
--- a/src/dynamic_foraging_processing/processing/_trial_table.py
+++ b/src/dynamic_foraging_processing/processing/_trial_table.py
@@ -10,6 +10,7 @@
 
 import numpy as np
 import pandas as pd
+from aind_behavior_dynamic_foraging.rig import AindDynamicForagingRig
 from aind_behavior_dynamic_foraging.task_logic import AindDynamicForagingTaskLogic
 from aind_behavior_dynamic_foraging.task_logic.trial_generators import TrialGeneratorSpec
 from aind_behavior_dynamic_foraging.task_logic.trial_models import (
@@ -24,6 +25,12 @@
 
 logger = logging.getLogger(__name__)
 
+# The four manipulator axes, ordered so that index ``i`` is the axis driven
+# by ``Motor{i}`` in the ``AccumulatedSteps`` stream: ``Motor{i}`` maps to
+# ``Axis(i + 1)`` (Axis.X=1, Y1=2, Y2=3, Z=4), whose names match the
+# ``lickspout_position_*`` suffixes and the ``full_step_to_mm`` attributes.
+_MANIPULATOR_AXES = ("x", "y1", "y2", "z")
+
 
 class TrialTableBuilder:
     """Builds the NWB ``trials`` table from a dynamic foraging ``Dataset``.
@@ -236,7 +243,7 @@ def _animal_response(payload: t.Any) -> int:
         return 1 if bool(choice) else 0
 
     @staticmethod
-    def _parse_outcome(payload: t.Any) -> t.Optional[TrialOutcome]:
+    def _parse_outcome(payload: t.Any) -> TrialOutcome:
         """Parse a ``TrialOutcome`` software-event payload into its domain model.
 
         Parameters
@@ -247,11 +254,16 @@ def _parse_outcome(payload: t.Any) -> t.Optional[TrialOutcome]:
 
         Returns
         -------
-        TrialOutcome or None
-            The parsed model, or ``None`` if ``payload`` is empty.
+        TrialOutcome
+            The parsed model.
+
+        Raises
+        ------
+        ValueError
+            If ``payload`` is ``None``.
         """
         if payload is None:
-            return None
+            raise ValueError("TrialOutcome payload is required but received None.")
         if isinstance(payload, TrialOutcome):
             return payload
         if isinstance(payload, str):
@@ -320,7 +332,7 @@ def _rewarded_history(
         return is_rewarded and (is_right_choice is is_right)
 
     @staticmethod
-    def _is_baited(trial: t.Optional[Trial], *, is_right: bool) -> bool:
+    def _is_baited(trial: Trial, *, is_right: bool) -> bool:
         """Return whether the requested lickport is baited on this trial.
 
         A port is "baited" when reward is guaranteed there (its reward
@@ -343,16 +355,15 @@ def _is_baited(trial: t.Optional[Trial], *, is_right: bool) -> bool:
 
         Parameters
         ----------
-        trial : Trial or None
-            The per-trial task-logic model, or ``None`` when unavailable.
+        trial : Trial
+            The per-trial task-logic model.
         is_right : bool
             ``True`` for the right port, ``False`` for the left port.
 
         Returns
         -------
         bool
-            Whether the requested side is baited. A missing ``trial`` is treated
-            as not baited (``False``).
+            Whether the requested side is baited.
 
         Examples
         --------
@@ -374,8 +385,6 @@ def _is_baited(trial: t.Optional[Trial], *, is_right: bool) -> bool:
         >>> TrialTableBuilder._is_baited(trial, is_right=False)
         False
         """
-        if trial is None:
-            return False
         auto = trial.is_auto_reward_right
         if is_right:
             # Right stays baited unless the animal was auto-responded right.
@@ -384,20 +393,19 @@ def _is_baited(trial: t.Optional[Trial], *, is_right: bool) -> bool:
         return trial.p_reward_left == 1 and auto in (None, True)
 
     @staticmethod
-    def _auto_water(trial: t.Optional[Trial], *, is_right: bool) -> int:
+    def _auto_water(trial: Trial, *, is_right: bool) -> int:
         """Encode autowater for a side from ``is_auto_reward_right``.
 
         Returns ``1`` if the auto response was to the requested side, else ``0``.
-        A missing trial or no auto-response (``is_auto_reward_right`` is
-        ``None``) counts as no autowater (``0``). ``is_right`` is ``True`` for
-        right.
+        No auto-response (``is_auto_reward_right`` is ``None``) counts as no
+        autowater (``0``). ``is_right`` is ``True`` for right.
         """
-        if trial is None or trial.is_auto_reward_right is None:
+        if trial.is_auto_reward_right is None:
             return 0
         return int(trial.is_auto_reward_right is is_right)
 
     @staticmethod
-    def _block_reward_probability(trial: t.Optional[Trial], *, is_right: bool) -> t.Optional[float]:
+    def _block_reward_probability(trial: Trial, *, is_right: bool) -> t.Optional[float]:
         """Return the block reward probability for a side from the trial metadata.
 
         The top-level ``trial.p_reward_left/right`` is the *per-trial* probability;
@@ -406,18 +414,17 @@ def _block_reward_probability(trial: t.Optional[Trial], *, is_right: bool) -> t.
 
         Parameters
         ----------
-        trial : Trial or None
-            The per-trial task-logic model, or ``None`` when unavailable.
+        trial : Trial
+            The per-trial task-logic model.
         is_right : bool
             ``True`` for the right port, ``False`` for the left port.
 
         Returns
         -------
         float or None
-            The block reward probability, or ``None`` when the trial or its
-            metadata is unavailable.
+            The block reward probability, or ``None`` when metadata is unavailable.
         """
-        if trial is None or trial.metadata is None:
+        if trial.metadata is None:
             return None
         return trial.metadata.p_reward_right if is_right else trial.metadata.p_reward_left
 
@@ -483,14 +490,6 @@ def _session_columns(self, task_logic: AindDynamicForagingTaskLogic) -> t.Dict[s
         if task_logic is None:
             return columns
 
-        # Reward volumes live on the task parameters, not the trial generator, so
-        # populate them before the generator resolution (which may bail out).
-        # Known limitation: the acquisition system can vary reward size per trial,
-        # but the current data format only exposes a single session-level value.
-        reward_size = task_logic.task_parameters.reward_size
-        columns["reward_size_left"] = reward_size.left_value_volume
-        columns["reward_size_right"] = reward_size.right_value_volume
-
         generator = self._summary_generator(task_logic.task_parameters.trial_generator)
         if generator is None:
             return columns
@@ -519,46 +518,145 @@ def _session_columns(self, task_logic: AindDynamicForagingTaskLogic) -> t.Dict[s
             delay_max=delay_max,
             base_reward_probability_sum=base_reward_sum,
         )
-        # ``min_block_reward`` is coupled-only; uncoupled generators omit it.
+        # ``min_block_reward`` is warmup-generator-only; main coupled generators omit it.
         if hasattr(generator, "min_block_reward"):
             columns["min_reward_each_block"] = generator.min_block_reward
         return columns
 
-    def _lickspout_columns(
-        self, manipulator: t.Optional[pd.DataFrame]
+    def _manipulator_mm_per_step(self, rig: AindDynamicForagingRig) -> t.Dict[str, float]:
+        """Return millimetres travelled per accumulated step, keyed by axis.
+
+        ``AccumulatedSteps`` counts *microsteps*. The physical distance per
+        microstep is the full-step-to-mm calibration divided by the microstep
+        resolution (e.g. ``MICROSTEP8`` → 8 microsteps per full step), both read
+        from the rig's manipulator calibration.
+
+        Parameters
+        ----------
+        rig : AindDynamicForagingRig
+            The ``InputSchemas.Rig`` stream data. The rig is a required input
+            schema, so ``build`` guarantees it is present before this is called.
+
+        Returns
+        -------
+        dict of str to float
+            ``{axis: mm_per_step}`` for ``x`` / ``y1`` / ``y2`` / ``z``.
+
+        Raises
+        ------
+        ValueError
+            If the manipulator calibration is missing one of the four axes.
+        """
+        calibration = rig.manipulator.calibration
+        resolution_by_axis = {
+            config.axis.name.lower(): config.microstep_resolution
+            for config in calibration.axis_configuration
+        }
+        mm_per_step: t.Dict[str, float] = {}
+        for axis in _MANIPULATOR_AXES:
+            resolution = resolution_by_axis.get(axis)
+            if resolution is None:
+                raise ValueError(
+                    f"Manipulator calibration is missing axis {axis!r}; "
+                    "cannot derive lickspout position."
+                )
+            # Resolution enum names encode the divisor (MICROSTEP8 -> 8); the
+            # enum *value* (0..3) does not, so parse from the name.
+            microsteps = int(resolution.name.replace("MICROSTEP", ""))
+            mm_per_step[axis] = getattr(calibration.full_step_to_mm, axis) / microsteps
+        return mm_per_step
+
+    def _manipulator_positions(
+        self,
+        accumulated_steps: pd.DataFrame,
+        rig: AindDynamicForagingRig,
+    ) -> pd.DataFrame:
+        """Return a time-indexed frame of lickspout position (mm) over the session.
+
+        Built from the ``HarpManipulator`` ``AccumulatedSteps`` stream: each
+        ``EVENT`` row's per-motor microstep count is converted to millimetres via
+        the rig calibration. ``AccumulatedSteps`` is absolute (zero-referenced at
+        the homing position), so the count maps directly to position with no
+        offset. The frame is then re-referenced to the session start (the first
+        sample subtracted from every row), so the stored values are displacement
+        *relative to session start* — the units the QC plot expects.
+
+        Parameters
+        ----------
+        accumulated_steps : pandas.DataFrame
+            The ``AccumulatedSteps`` stream data (``Motor0``..``Motor3`` columns).
+            Required; guaranteed present by ``build``.
+        rig : AindDynamicForagingRig
+            The ``InputSchemas.Rig`` stream data (required; guaranteed present by
+            ``build``).
+
+        Returns
+        -------
+        pandas.DataFrame
+            Columns ``lickspout_position_x`` / ``y1`` / ``y2`` / ``z`` indexed by
+            time (sorted ascending), relative to the session-start position.
+
+        Raises
+        ------
+        ValueError
+            If the stream has no ``EVENT`` rows, is missing a motor column, or the
+            rig calibration is missing an axis.
+        """
+        mm_per_step = self._manipulator_mm_per_step(rig)
+        events = accumulated_steps
+        if "MessageType" in events.columns:
+            events = events[events["MessageType"] == "EVENT"]
+        if len(events) == 0:
+            raise ValueError(
+                "AccumulatedSteps stream has no EVENT rows; cannot derive lickspout position."
+            )
+        events = events.sort_index()
+        columns: t.Dict[str, np.ndarray] = {}
+        for index, axis in enumerate(_MANIPULATOR_AXES):
+            motor = f"Motor{index}"
+            if motor not in events.columns:
+                raise ValueError(
+                    f"AccumulatedSteps stream is missing column {motor}; "
+                    "cannot derive lickspout position."
+                )
+            columns[f"lickspout_position_{axis}"] = (
+                events[motor].to_numpy(dtype=float) * mm_per_step[axis]
+            )
+        positions = pd.DataFrame(columns, index=events.index)
+        # Re-reference to session start so the stored values are displacement
+        # from the first sample (previously done in the plot as ``values[0]``).
+        return positions - positions.iloc[0]
+
+    def _sample_lickspout(
+        self, positions: pd.DataFrame, start: float, stop: float
     ) -> t.Dict[str, t.Optional[float]]:
-        """Return lickspout position columns from ``InitialManipulatorPosition``.
+        """Return the lickspout position sampled within a trial's window.
 
-        The event's ``data`` payload carries the x / y1 / y2 / z positions. These
-        are the *initial* manipulator coordinates recorded once at experiment
-        start, so the columns are currently constant across trials.
+        The manipulator position is a continuously-sampled hardware value, so —
+        like the go cue — each trial takes the sample within its ``[start, stop)``
+        window nearest the start (see :meth:`_closest_time_in_window`).
 
-        Known limitation: the manipulator can move within a session (e.g. the
-        anti-bias intervention shifts the lickspouts horizontally), which this
-        static initial position does not capture.
+        Parameters
+        ----------
+        positions : pandas.DataFrame
+            The time-indexed position frame from :meth:`_manipulator_positions`.
+        start, stop : float
+            The trial window bounds (seconds); ``start`` inclusive, ``stop``
+            exclusive. May be ``NaN`` when unaligned.
 
-        TODO: think about how to represent
-        this if it becomes relevant. The ideal solution would be to use the harp files
-        to track the manipulator position over time
+        Returns
+        -------
+        dict of str to (float or None)
+            The four ``lickspout_position_*`` values, all ``None`` for a trial
+            whose window contains no manipulator sample.
         """
-        keys = (
-            "lickspout_position_x",
-            "lickspout_position_y1",
-            "lickspout_position_y2",
-            "lickspout_position_z",
-        )
-        empty = {key: None for key in keys}
-        payloads = self._event_payloads(manipulator)
-        if not payloads:
-            return empty
-        payload = payloads[0]
-        components = ("x", "y1", "y2", "z")
-        if isinstance(payload, dict):
-            return {key: payload.get(comp) for key, comp in zip(keys, components)}
-        if isinstance(payload, (list, tuple)) and len(payload) == len(keys):
-            return {key: payload[idx] for idx, key in enumerate(keys)}
-        logger.warning("Unrecognized InitialManipulatorPosition payload; leaving lickspout null.")
-        return empty
+        keys = tuple(f"lickspout_position_{axis}" for axis in _MANIPULATOR_AXES)
+        times = positions.index.to_numpy(dtype=float)
+        sample_time = self._closest_time_in_window(times, start, stop)
+        if sample_time is None:
+            return {key: None for key in keys}
+        row = positions.loc[sample_time]
+        return {key: (None if pd.isna(row[key]) else float(row[key])) for key in keys}
 
     # ------------------------------------------------------------------ #
     # Per-trial assembly
@@ -566,7 +664,7 @@ def _lickspout_columns(
     def _build_row(
         self,
         *,
-        outcome: t.Optional[TrialOutcome],
+        outcome: TrialOutcome,
         start: float,
         stop: float,
         response: t.Any,
@@ -578,9 +676,9 @@ def _build_row(
         lickspout: t.Dict[str, t.Optional[float]],
     ) -> TrialConfig:
         """Assemble a single ``TrialConfig`` from aligned per-trial inputs."""
-        trial = outcome.trial if outcome is not None else None
-        is_right_choice = outcome.is_right_choice if outcome is not None else None
-        is_rewarded = bool(outcome.is_rewarded) if outcome is not None else False
+        trial = outcome.trial
+        is_right_choice = outcome.is_right_choice
+        is_rewarded = bool(outcome.is_rewarded)
 
         return TrialConfig(
             start_time=start,
@@ -596,13 +694,13 @@ def _build_row(
             bait_right=self._is_baited(trial, is_right=True),
             reward_probabilityL=self._block_reward_probability(trial, is_right=False),
             reward_probabilityR=self._block_reward_probability(trial, is_right=True),
+            reward_size_left=trial.reward_size.left,
+            reward_size_right=trial.reward_size.right,
             side_bias=side_bias,
-            response_duration=trial.response_deadline_duration if trial is not None else None,
-            reward_consumption_duration=(
-                trial.reward_consumption_duration if trial is not None else None
-            ),
-            ITI_duration=trial.inter_trial_interval_duration if trial is not None else None,
-            delay_duration=trial.quiescence_period_duration if trial is not None else None,
+            response_duration=trial.response_deadline_duration,
+            reward_consumption_duration=trial.reward_consumption_duration,
+            ITI_duration=trial.inter_trial_interval_duration,
+            delay_duration=trial.quiescence_period_duration,
             auto_waterL=self._auto_water(trial, is_right=False),
             auto_waterR=self._auto_water(trial, is_right=True),
             **session,
@@ -669,9 +767,7 @@ def build(self) -> pd.DataFrame:
         Raises
         ------
         ValueError
-            If the ``TaskLogic`` stream is missing while there are trials to
-            build (the required reward-size columns are sourced from it), or if
-            ``raise_on_error`` is ``True`` and a per-trial stream length
+            If ``raise_on_error`` is ``True`` and a per-trial stream length
             disagrees with the ``TrialOutcome`` trial count.
         """
         outcomes = self._load("Behavior", "SoftwareEvents", "TrialOutcome")
@@ -683,7 +779,8 @@ def build(self) -> pd.DataFrame:
         pulse_supply_left = self._load("Behavior", "HarpBehavior", "PulseSupplyPort0")
         pulse_supply_right = self._load("Behavior", "HarpBehavior", "PulseSupplyPort1")
         go_cue = self._load("Behavior", "HarpSoundCard", "PlaySoundOrFrequency")
-        manipulator = self._load("Behavior", "SoftwareEvents", "InitialManipulatorPosition")
+        accumulated_steps = self._load("Behavior", "HarpManipulator", "AccumulatedSteps")
+        rig = self._load("Behavior", "InputSchemas", "Rig")
         task_logic = self._load("Behavior", "InputSchemas", "TaskLogic")
 
         # Per-trial streams: one payload/timestamp per trial, aligned by index.
@@ -696,16 +793,6 @@ def build(self) -> pd.DataFrame:
         # Guard the positional alignment before we pair streams by index.
         n_trials = len(outcome_payloads)
 
-        # Reward size is sourced from the task logic and is a required column, so
-        # a missing TaskLogic stream cannot yield a valid table when there are
-        # trials to build. Surface that clearly rather than failing later with a
-        # cryptic per-row validation error.
-        if n_trials > 0 and task_logic is None:
-            raise ValueError(
-                "TaskLogic stream is required to build the trials table "
-                f"(reward sizes are sourced from it) but it failed to load for {n_trials} trials."
-            )
-
         warnings = self._check_aligned(
             n_trials,
             {
@@ -732,7 +819,15 @@ def build(self) -> pd.DataFrame:
         go_cue_times = self._write_times(go_cue)
 
         session = self._session_columns(task_logic)
-        lickspout = self._lickspout_columns(manipulator)
+        if n_trials:
+            if rig is None:
+                raise ValueError("Rig stream is required when there are trials.")
+            if accumulated_steps is None:
+                raise ValueError("AccumulatedSteps stream is required when there are trials.")
+        # With no trials the frame goes unused (the loop does not run).
+        lickspout_positions = (
+            self._manipulator_positions(accumulated_steps, rig) if n_trials else None
+        )
 
         rows: t.List[TrialConfig] = []
         for i, outcome_payload in enumerate(outcome_payloads):
@@ -743,6 +838,7 @@ def build(self) -> pd.DataFrame:
             stop = float(stop_times[i]) if i < stop_times.size else np.nan
             response = response_payloads[i] if i < len(response_payloads) else None
             side_bias = self._side_bias(metric_payloads[i] if i < len(metric_payloads) else None)
+            lickspout = self._sample_lickspout(lickspout_positions, start, stop)
 
             rows.append(
                 self._build_row(
diff --git a/src/dynamic_foraging_processing/processing/models/trial_config.py b/src/dynamic_foraging_processing/processing/models/trial_config.py
index 8c20bcc..ed3425b 100644
--- a/src/dynamic_foraging_processing/processing/models/trial_config.py
+++ b/src/dynamic_foraging_processing/processing/models/trial_config.py
@@ -142,18 +142,20 @@ class TrialConfig(BaseModel):
 
     # --- lickspout_position (mapping's `lickspout_positions` -> these four components) ---
     lickspout_position_x: Optional[float] = Field(
-        default=None, description="x position (um) of the lickspout position (left-right)"
+        default=None,
+        description="x lickspout position (mm), relative to session start (left-right)",
     )
     lickspout_position_y1: Optional[float] = Field(
         default=None,
-        description="y1 position (um) of the left lickspout position (forward-backward)",
+        description="y1 left lickspout position (mm), relative to session start (forward-backward)",
     )
     lickspout_position_y2: Optional[float] = Field(
         default=None,
-        description="y2 position (um) of the right lickspout position (forward-backward)",
+        description="y2 right lickspout position (mm), relative to session start (forward-backward)",
     )
     lickspout_position_z: Optional[float] = Field(
-        default=None, description="z position (um) of the lickspout position (up-down)"
+        default=None,
+        description="z lickspout position (mm), relative to session start (up-down)",
     )
 
     @classmethod
diff --git a/src/dynamic_foraging_processing/qc/_core/builder.py b/src/dynamic_foraging_processing/qc/_core/builder.py
index d9b590f..a7ee77f 100644
--- a/src/dynamic_foraging_processing/qc/_core/builder.py
+++ b/src/dynamic_foraging_processing/qc/_core/builder.py
@@ -1,8 +1,8 @@
 """Assemble the dynamic foraging ``QualityControl`` object.
 
-Collects a flat list of metrics (behavior + contract QA) into a single
-``QualityControl``, wiring up ``default_grouping`` so the QC portal lays out
-``behavior`` and ``test_suite`` as sibling top-level groups.
+Collects a flat list of metrics (behavior + contract QC) into a single
+``QualityControl``, wiring up ``default_grouping`` so the QC portal groups
+metrics by their ``type`` and ``test_suite`` tags.
 """
 
 import typing as t
@@ -10,7 +10,7 @@
 from aind_data_schema.core.quality_control import QCMetric, QualityControl
 
 #: Tag keys laid out as siblings at the top level of the QC portal.
-DEFAULT_GROUPING = ["behavior", "test_suite"]
+DEFAULT_GROUPING = [("type", "test_suite")]
 
 
 def build_quality_control(
@@ -26,9 +26,9 @@ def build_quality_control(
     Parameters
     ----------
     metrics : list of QCMetric
-        All metrics (behavior + contract QA).
+        All metrics (behavior + contract QC).
     default_grouping : list of str, optional
-        Tag keys the portal groups by. Defaults to ``["behavior", "test_suite"]``.
+        Tag keys the portal groups by. Defaults to ``[("type", "test_suite")]``.
     allow_tag_failures : list of str, optional
         Tag values whose metric failures should not fail the overall QC.
     key_experimenters : list of str, optional
diff --git a/src/dynamic_foraging_processing/qc/processed/behavior.py b/src/dynamic_foraging_processing/qc/processed/behavior.py
index ffc6e9e..b9e019e 100644
--- a/src/dynamic_foraging_processing/qc/processed/behavior.py
+++ b/src/dynamic_foraging_processing/qc/processed/behavior.py
@@ -146,8 +146,8 @@ def side_bias_result(side_bias: np.ndarray, results_folder: t.Optional[str] = No
     -------
     QCResult
         Passes when ``abs(mean_bias) < 0.5``. Fails when the column is empty or
-        all ``nan`` (``mean_bias`` is ``nan``). Tagged ``{"behavior": ...}`` and
-        referencing the side-bias plot.
+        all ``nan`` (``mean_bias`` is ``nan``). Tagged
+        ``{"type": "Average_Side_Bias"}`` and referencing the side-bias plot.
     """
     values = np.asarray(side_bias, dtype=float)
     if values.size == 0 or np.all(np.isnan(values)):
@@ -159,9 +159,9 @@ def side_bias_result(side_bias: np.ndarray, results_folder: t.Optional[str] = No
         name=name,
         value=mean_bias,
         passed=bool(abs(mean_bias) < 0.5),  # nan comparisons are False -> fails
-        description="Average side bias across the session (right is positive).",
+        description="Average side bias should be less than 0.5",
         reference=_plot_reference(SIDE_BIAS_PLOT, results_folder),
-        tags={"behavior": name},
+        tags={"type": "Average_Side_Bias"},
     )
 
 
@@ -203,7 +203,7 @@ def lick_interval_results(
             passed=value < limit,
             description=f"{name} of inter-lick intervals; passes when < {limit}.",
             reference=_plot_reference(LICK_INTERVALS_PLOT, results_folder),
-            tags={"behavior": name},
+            tags={"metric": name, "type": "Lick_Interval"},
         )
         for name, value, limit in specs
     ]
diff --git a/src/dynamic_foraging_processing/qc/processed/plots.py b/src/dynamic_foraging_processing/qc/processed/plots.py
index d04c941..e46e335 100644
--- a/src/dynamic_foraging_processing/qc/processed/plots.py
+++ b/src/dynamic_foraging_processing/qc/processed/plots.py
@@ -122,7 +122,9 @@ def _add_lickspout_position_plot(
         if position is None or len(position) == 0:
             continue
         values = np.asarray(position, dtype=float)
-        ax.plot(values - values[0], color, label=label)
+        # Values already come in relative to session start (normalized by the
+        # trial-table builder), so plot them directly.
+        ax.plot(values, color, label=label)
         plotted = True
     if plotted:
         ax.legend()
@@ -303,6 +305,14 @@ def plot_side_bias(
     )
     _add_reward_probabilities(ax[3], reward_probability_left, reward_probability_right)
 
+    # Align the x-axis across every panel so trials line up vertically. The
+    # panels are all indexed by trial, but some auto-scale (adding margins) while
+    # others set [0, N]; pin them all to a common [0, n_trials].
+    n_trials = max(len(np.asarray(side_bias)), len(np.asarray(animal_response)))
+    if n_trials:
+        for axis in ax:
+            axis.set_xlim([0, n_trials])
+
     fig.savefig(Path(results_folder) / SIDE_BIAS_PLOT, dpi=300, bbox_inches="tight")
     plt.close(fig)
     return SIDE_BIAS_PLOT
diff --git a/src/dynamic_foraging_processing/qc/processed/results.py b/src/dynamic_foraging_processing/qc/processed/results.py
index 762890f..a263f1a 100644
--- a/src/dynamic_foraging_processing/qc/processed/results.py
+++ b/src/dynamic_foraging_processing/qc/processed/results.py
@@ -22,15 +22,15 @@
 from dynamic_foraging_processing.qc.processed.plots import plot_lick_intervals, plot_side_bias
 
 # Logical input -> trials-table column name. Centralized so the mapping is easy
-# to correct against the trial-table builder; ``side_bias`` and the
-# ``lickspout_*`` arrays are not yet pinned down in trials_table_mapping.md.
+# to correct against the trial-table builder. The lickspout columns are the
+# ``lickspout_position_*`` fields documented in trials_table_mapping.md.
 _COLUMNS = {
     "animal_response": "animal_response",
     "side_bias": "side_bias",
-    "lickspout_x": "lickspout_x",
-    "lickspout_y1": "lickspout_y1",
-    "lickspout_y2": "lickspout_y2",
-    "lickspout_z": "lickspout_z",
+    "lickspout_x": "lickspout_position_x",
+    "lickspout_y1": "lickspout_position_y1",
+    "lickspout_y2": "lickspout_position_y2",
+    "lickspout_z": "lickspout_position_z",
     "rewarded_left": "rewarded_historyL",
     "rewarded_right": "rewarded_historyR",
     "reward_probability_left": "reward_probabilityL",
diff --git a/tests/test_nwb/test_acquisition/test_acquisition_builder.py b/tests/test_nwb/test_acquisition/test_acquisition_builder.py
index 3ee8c05..4b1f91f 100644
--- a/tests/test_nwb/test_acquisition/test_acquisition_builder.py
+++ b/tests/test_nwb/test_acquisition/test_acquisition_builder.py
@@ -88,13 +88,18 @@ def _empty_manual_water_frame() -> pd.DataFrame:
 
 
 def _make_digital_input_frame() -> pd.DataFrame:
-    """Build a DigitalInputState frame: left licks high at 1.0, right at 2.0/2.5."""
+    """Build a DigitalInputState frame: left licks high at 1.0, right at 2.0/2.5.
+
+    Includes a non-EVENT ``READ`` row at 0.5 with ``DIPort0`` latched high; it
+    reports register state rather than a lick and must be filtered out.
+    """
     return pd.DataFrame(
         {
-            "DIPort0": [True, False, False],
-            "DIPort1": [False, True, True],
+            "MessageType": ["READ", "EVENT", "EVENT", "EVENT"],
+            "DIPort0": [True, True, False, False],
+            "DIPort1": [False, False, True, True],
         },
-        index=pd.Index([1.0, 2.0, 2.5], name="time"),
+        index=pd.Index([0.5, 1.0, 2.0, 2.5], name="time"),
     )
 
 
@@ -196,6 +201,17 @@ def test_get_lick_times_selects_di_port_by_side():
     )
 
 
+def test_get_lick_times_ignores_non_event_rows():
+    """Only EVENT rows are licks; latched-high READ/WRITE rows are ignored."""
+    builder = AcquisitionBuilder(loader=_make_loader())
+
+    # DIPort0 is high in the READ row at 0.5 and the EVENT row at 1.0; only the
+    # EVENT-row timestamp is returned.
+    np.testing.assert_array_equal(
+        builder.get_lick_times("HarpBehavior", "DigitalInputState", "DIPort0"), np.array([1.0])
+    )
+
+
 def test_get_lick_times_returns_empty_when_absent():
     """A missing DigitalInputState stream yields an empty array."""
     dataset = _FakeNode(
diff --git a/tests/test_pipeline/test_pipeline.py b/tests/test_pipeline/test_pipeline.py
index cef052f..24fe4f1 100644
--- a/tests/test_pipeline/test_pipeline.py
+++ b/tests/test_pipeline/test_pipeline.py
@@ -1,5 +1,6 @@
 """Tests for ``dynamic_foraging_processing.pipeline._pipeline``."""
 
+from datetime import datetime, timezone
 from pathlib import Path
 from unittest.mock import MagicMock
 
@@ -371,6 +372,25 @@ def test_run_nwb_writes_nwb_and_processing(tmp_path):
     assert processing_json.exists()
     loaded = Processing.model_validate_json(processing_json.read_text())
     assert loaded.data_processes[0].process_type == ProcessName.PIPELINE
+    # The data process and the top-level pipeline are linked by name.
+    assert loaded.data_processes[0].pipeline_name == "dynamic-foraging-processing-pipeline"
+    assert [p.name for p in loaded.pipelines] == ["dynamic-foraging-processing-pipeline"]
+    assert loaded.pipelines[0].version == _pipeline._PACKAGE_VERSION
+    assert loaded.pipelines[0].url == _pipeline._CODE_URL
+
+
+def test_write_processing_records_input_data_from_loader_path(tmp_path):
+    """``_write_processing`` records the loader file stem as the pipeline ``input_data``."""
+    pipeline = _make_pipeline()
+    pipeline.loader.path = Path("some/dir/my_session.json")
+    start = datetime(2024, 1, 1, tzinfo=timezone.utc)
+    end = datetime(2024, 1, 2, tzinfo=timezone.utc)
+
+    pipeline._write_processing(str(tmp_path), start, end)
+
+    loaded = Processing.model_validate_json((tmp_path / "processing.json").read_text())
+    input_data = loaded.pipelines[0].input_data
+    assert [asset.name for asset in input_data] == ["my_session"]
 
 
 def test_run_qc_writes_quality_control(tmp_path):
diff --git a/tests/test_processing/test_trial_table.py b/tests/test_processing/test_trial_table.py
index e4b91a6..65c0eb1 100644
--- a/tests/test_processing/test_trial_table.py
+++ b/tests/test_processing/test_trial_table.py
@@ -1,12 +1,13 @@
 """Tests for ``dynamic_foraging_processing.processing._trial_table``."""
 
+from types import SimpleNamespace
+
 import numpy as np
 import pandas as pd
 import pytest
 from aind_behavior_dynamic_foraging.task_logic import (
     AindDynamicForagingTaskLogic,
     AindDynamicForagingTaskParameters,
-    RewardSize,
 )
 from aind_behavior_dynamic_foraging.task_logic.trial_generators import (
     CoupledTrialGeneratorSpec,
@@ -15,6 +16,7 @@
     UncoupledTrialGeneratorSpec,
 )
 from aind_behavior_dynamic_foraging.task_logic.trial_models import TrialOutcome
+from aind_behavior_services.rig.aind_manipulator import Axis, MicrostepResolution
 from aind_behavior_services.task.distributions import (
     ExponentialDistribution,
     ExponentialDistributionParameters,
@@ -89,13 +91,16 @@ def _outcome(
     auto=None,
     block_p_left=None,
     block_p_right=None,
+    reward_size_left=None,
+    reward_size_right=None,
 ):
     """Build a serialized ``TrialOutcome`` payload (dict, as delivered by the reader).
 
     ``p_left`` / ``p_right`` are the per-trial probabilities (top-level ``trial``
     fields); ``block_p_left`` / ``block_p_right``, when given, are the block
     probabilities stored under ``trial.metadata`` (the source of the
-    ``reward_probability`` columns).
+    ``reward_probability`` columns). ``reward_size_left`` / ``reward_size_right``
+    override the default per-trial reward volumes (uL).
     """
     trial = {
         "p_reward_left": p_left,
@@ -106,6 +111,11 @@ def _outcome(
         "inter_trial_interval_duration": 4.0,
         "is_auto_reward_right": auto,
     }
+    if reward_size_left is not None or reward_size_right is not None:
+        trial["reward_size"] = {
+            "left": reward_size_left if reward_size_left is not None else 2.0,
+            "right": reward_size_right if reward_size_right is not None else 2.0,
+        }
     if block_p_left is not None or block_p_right is not None:
         trial["metadata"] = {"p_reward_left": block_p_left, "p_reward_right": block_p_right}
     return {
@@ -138,10 +148,7 @@ def _task_logic(quiescent_scalar=True):
         min_block_reward=2,
     )
     return AindDynamicForagingTaskLogic(
-        task_parameters=AindDynamicForagingTaskParameters(
-            trial_generator=spec,
-            reward_size=RewardSize(left_value_volume=2.0, right_value_volume=4.0),
-        )
+        task_parameters=AindDynamicForagingTaskParameters(trial_generator=spec)
     )
 
 
@@ -157,6 +164,35 @@ def _pulse_supply(column, value_ms):
     )
 
 
+def _rig(step_mm=0.01, resolution=MicrostepResolution.MICROSTEP8):
+    """Build a rig stub exposing the manipulator calibration the builder reads.
+
+    A ``SimpleNamespace`` mirrors the ``AindDynamicForagingRig`` attribute path
+    (``manipulator.calibration.full_step_to_mm`` / ``axis_configuration``) used by
+    the builder, with real ``Axis`` / ``MicrostepResolution`` enums.
+    """
+    full_step_to_mm = SimpleNamespace(x=step_mm, y1=step_mm, y2=step_mm, z=step_mm)
+    axis_configuration = [
+        SimpleNamespace(axis=axis, microstep_resolution=resolution)
+        for axis in (Axis.X, Axis.Y1, Axis.Y2, Axis.Z)
+    ]
+    calibration = SimpleNamespace(
+        full_step_to_mm=full_step_to_mm, axis_configuration=axis_configuration
+    )
+    return SimpleNamespace(manipulator=SimpleNamespace(calibration=calibration))
+
+
+def _accumulated_steps(times, motor_steps):
+    """Build an ``AccumulatedSteps`` frame (Motor0..Motor3 microstep counts).
+
+    ``motor_steps`` is a sequence of ``(m0, m1, m2, m3)`` per timestamp; all rows
+    are ``EVENT`` messages, mirroring the HarpManipulator stream.
+    """
+    columns = {f"Motor{i}": [row[i] for row in motor_steps] for i in range(4)}
+    columns["MessageType"] = ["EVENT"] * len(times)
+    return pd.DataFrame(columns, index=pd.Index(times, name="Time"))
+
+
 def _full_dataset():
     """Assemble a two-trial fake dataset covering the common path."""
     software_events = _Node(
@@ -172,6 +208,8 @@ def _full_dataset():
                             is_rewarded=True,
                             block_p_left=0.7,
                             block_p_right=0.1,
+                            reward_size_left=2.0,
+                            reward_size_right=4.0,
                         ),
                         _outcome(0.5, 0.5, is_right_choice=None, is_rewarded=False),
                     ],
@@ -184,9 +222,6 @@ def _full_dataset():
                     [10.5, 20.5], [{"Item1": 10.5, "Item2": True}, {"Item1": 20.5, "Item2": None}]
                 )
             ),
-            "InitialManipulatorPosition": _Stream(
-                _events([5.0], [{"x": 1.0, "y1": 2.0, "y2": 3.0, "z": 4.0}])
-            ),
             "TrialMetrics": _Stream(_events([10.2, 20.2], [{"bias": 0.3}, {"bias": None}])),
         }
     )
@@ -209,7 +244,20 @@ def _full_dataset():
                     )
                 }
             ),
-            "InputSchemas": _Node({"TaskLogic": _Stream(_task_logic())}),
+            "HarpManipulator": _Node(
+                {
+                    # 0.00125 mm/microstep (0.01 / MICROSTEP8). One sample falls
+                    # inside each trial window ([10, 20) and [20, 30)); x moves
+                    # 10.0 -> 15.0 between the two trials.
+                    "AccumulatedSteps": _Stream(
+                        _accumulated_steps(
+                            [10.5, 20.5],
+                            [(8000, 1600, 2400, 4000), (12000, 1600, 2400, 4000)],
+                        )
+                    )
+                }
+            ),
+            "InputSchemas": _Node({"TaskLogic": _Stream(_task_logic()), "Rig": _Stream(_rig())}),
         }
     )
     return _Node({"Behavior": behavior})
@@ -275,29 +323,55 @@ def test_build_full_dataset():
     assert first["ITI_min"] == 1.0 and first["ITI_max"] == 10.0
     assert first["block_beta"] == pytest.approx(20.0)
     assert pd.isna(first["delay_beta"])  # scalar quiescent distribution
-    assert first["min_reward_each_block"] == 2
+    assert pd.isna(first["min_reward_each_block"])  # removed from generator schema
     assert first["base_reward_probability_sum"] == pytest.approx(0.8)
 
-    # Lickspout positions from InitialManipulatorPosition.
-    assert first["lickspout_position_x"] == 1.0
-    assert first["lickspout_position_z"] == 4.0
+    # Lickspout positions from AccumulatedSteps (microsteps * 0.00125 mm),
+    # sampled at each trial start and re-referenced to session start. The first
+    # sample is the baseline (all zero); x then moves +5.0 mm for the second trial.
+    assert first["lickspout_position_x"] == 0.0
+    assert first["lickspout_position_y1"] == 0.0
+    assert first["lickspout_position_y2"] == 0.0
+    assert first["lickspout_position_z"] == 0.0
+    assert second["lickspout_position_x"] == 5.0
 
-    # Session-level reward volumes (uL) from task_parameters.reward_size.
+    # Per-trial reward volumes (uL) from trial.reward_size; second trial uses the default.
     assert first["reward_size_left"] == 2.0
     assert first["reward_size_right"] == 4.0
     assert second["reward_size_left"] == 2.0
+    assert second["reward_size_right"] == 2.0
 
     # Per-trial side bias from the TrialMetrics event; null when not recorded.
     assert first["side_bias"] == pytest.approx(0.3)
     assert pd.isna(second["side_bias"])
 
 
-def test_build_raises_when_task_logic_missing_with_trials():
-    """A missing TaskLogic stream is an error when there are trials (reward size is required)."""
+def test_build_missing_task_logic_leaves_session_columns_null():
+    """A missing TaskLogic stream still builds; session distribution columns are null."""
     dataset = _full_dataset()
     input_schemas = dataset.children["Behavior"].children["InputSchemas"]
     input_schemas.children["TaskLogic"] = _FailedStream()
-    with pytest.raises(ValueError, match="TaskLogic stream is required"):
+    table = TrialTableBuilder(dataset).build()
+    assert len(table) == 2
+    assert table["ITI_beta"].isna().all()
+    assert table["block_beta"].isna().all()
+
+
+def test_build_raises_when_rig_missing_with_trials():
+    """A missing Rig stream is an error when there are trials (lickspout position needs it)."""
+    dataset = _full_dataset()
+    input_schemas = dataset.children["Behavior"].children["InputSchemas"]
+    input_schemas.children["Rig"] = _FailedStream()
+    with pytest.raises(ValueError, match="Rig stream is required"):
+        TrialTableBuilder(dataset).build()
+
+
+def test_build_raises_when_accumulated_steps_missing_with_trials():
+    """A missing AccumulatedSteps stream is an error when there are trials."""
+    dataset = _full_dataset()
+    harp_manipulator = dataset.children["Behavior"].children["HarpManipulator"]
+    harp_manipulator.children["AccumulatedSteps"] = _FailedStream()
+    with pytest.raises(ValueError, match="AccumulatedSteps stream is required"):
         TrialTableBuilder(dataset).build()
 
 
@@ -348,7 +422,7 @@ def test_summary_generator_prefers_coupled_over_uncoupled():
     composite = TrialGeneratorCompositeSpec(generators=[UncoupledTrialGeneratorSpec(), coupled])
     resolved = TrialTableBuilder._summary_generator(composite)
     assert resolved.type == "CoupledTrialGenerator"
-    assert resolved.min_block_reward == coupled.min_block_reward
+    assert resolved is coupled
 
 
 def test_summary_generator_falls_back_to_uncoupled():
@@ -367,29 +441,16 @@ def test_summary_generator_none_when_no_coupled_or_uncoupled(caplog):
     assert "No coupled or uncoupled generator" in caplog.text
 
 
-def test_session_columns_only_reward_size_when_no_summary_generator():
-    """With no coupled/uncoupled generator, only the generator-independent reward size remains."""
+def test_session_columns_empty_when_no_summary_generator():
+    """With no coupled/uncoupled generator, the session columns dict is empty."""
     task_logic = AindDynamicForagingTaskLogic(
         task_parameters=AindDynamicForagingTaskParameters(
             trial_generator=TrialGeneratorCompositeSpec(
                 generators=[CoupledWarmupTrialGeneratorSpec()]
             ),
-            reward_size=RewardSize(left_value_volume=2.0, right_value_volume=4.0),
         )
     )
-    # Reward size lives on the task parameters, not the generator, so it is the
-    # only column populated when no summarising generator is found.
-    assert TrialTableBuilder(_Node({}))._session_columns(task_logic) == {
-        "reward_size_left": 2.0,
-        "reward_size_right": 4.0,
-    }
-
-
-def test_session_columns_include_reward_size():
-    """Reward volumes are read from ``task_parameters.reward_size``."""
-    columns = TrialTableBuilder(_Node({}))._session_columns(_task_logic())
-    assert columns["reward_size_left"] == 2.0
-    assert columns["reward_size_right"] == 4.0
+    assert TrialTableBuilder(_Node({}))._session_columns(task_logic) == {}
 
 
 def test_side_bias_parses_dict_model_json_and_none():
@@ -414,6 +475,17 @@ def test_build_missing_trial_metrics_leaves_side_bias_null():
     assert table["side_bias"].isna().all()
 
 
+def test_session_columns_warmup_generator_populates_min_reward_each_block():
+    """A warmup generator exposes ``min_block_reward``, populating ``min_reward_each_block``."""
+    task_logic = AindDynamicForagingTaskLogic(
+        task_parameters=AindDynamicForagingTaskParameters(
+            trial_generator=CoupledWarmupTrialGeneratorSpec()
+        )
+    )
+    columns = TrialTableBuilder(_Node({}))._session_columns(task_logic)
+    assert columns["min_reward_each_block"] == CoupledWarmupTrialGeneratorSpec().min_block_reward
+
+
 def test_session_columns_uncoupled_has_null_reward_sum():
     """An uncoupled generator populates distribution columns but no coupled-only fields."""
     task_logic = AindDynamicForagingTaskLogic(
@@ -516,12 +588,6 @@ def test_animal_response_encoding():
     assert TrialTableBuilder._animal_response({"Item1": 1.0}) == 2
 
 
-def test_is_baited_none_trial_returns_false():
-    """A missing trial yields ``False`` bait for both sides."""
-    assert TrialTableBuilder._is_baited(None, is_right=True) is False
-    assert TrialTableBuilder._is_baited(None, is_right=False) is False
-
-
 def test_is_baited_forfeited_by_auto_response_on_same_side():
     """A side with guaranteed reward stays baited unless auto-responded to that side."""
     trial = TrialOutcome.model_validate(
@@ -538,8 +604,7 @@ def test_auto_water_encodes_side_from_auto_response():
     ).trial
     assert TrialTableBuilder._auto_water(trial, is_right=True) == 1
     assert TrialTableBuilder._auto_water(trial, is_right=False) == 0
-    # A missing trial or no auto-response counts as no autowater (0).
-    assert TrialTableBuilder._auto_water(None, is_right=True) == 0
+    # No auto-response counts as no autowater (0).
     no_auto = TrialOutcome.model_validate(
         _outcome(1.0, 1.0, is_right_choice=True, is_rewarded=True, auto=None)
     ).trial
@@ -557,9 +622,8 @@ def test_block_reward_probability_reads_metadata_not_trial():
     assert TrialTableBuilder._block_reward_probability(trial, is_right=True) == pytest.approx(0.1)
 
 
-def test_block_reward_probability_none_without_metadata_or_trial():
-    """A missing trial or absent metadata yields ``None``."""
-    assert TrialTableBuilder._block_reward_probability(None, is_right=True) is None
+def test_block_reward_probability_none_without_metadata():
+    """Absent metadata yields ``None``."""
     no_meta = TrialOutcome.model_validate(
         _outcome(1.0, 0.2, is_right_choice=True, is_rewarded=True)
     ).trial
@@ -570,15 +634,19 @@ def test_block_reward_probability_none_without_metadata_or_trial():
 @pytest.mark.parametrize(
     "payload",
     [
-        None,
         _outcome(1.0, 1.0, is_right_choice=True, is_rewarded=True),
         TrialOutcome.model_validate(_outcome(1.0, 1.0, is_right_choice=False, is_rewarded=False)),
     ],
 )
-def test_parse_outcome_accepts_dict_model_and_none(payload):
-    """``_parse_outcome`` accepts dicts, model instances, and ``None``."""
-    result = TrialTableBuilder._parse_outcome(payload)
-    assert result is None or isinstance(result, TrialOutcome)
+def test_parse_outcome_accepts_dict_and_model(payload):
+    """``_parse_outcome`` accepts dicts and model instances."""
+    assert isinstance(TrialTableBuilder._parse_outcome(payload), TrialOutcome)
+
+
+def test_parse_outcome_raises_on_none():
+    """``_parse_outcome`` raises ``ValueError`` for a ``None`` payload."""
+    with pytest.raises(ValueError, match="required"):
+        TrialTableBuilder._parse_outcome(None)
 
 
 def test_parse_outcome_accepts_json_string():
@@ -589,23 +657,109 @@ def test_parse_outcome_accepts_json_string():
     assert isinstance(TrialTableBuilder._parse_outcome(payload), TrialOutcome)
 
 
-def test_lickspout_columns_from_list_payload():
-    """A list payload is mapped positionally to x/y1/y2/z."""
+def test_manipulator_mm_per_step_from_calibration():
+    """mm-per-microstep is full_step_to_mm / microstep resolution, per axis."""
     builder = TrialTableBuilder(_Node({}))
-    columns = builder._lickspout_columns(_events([1.0], [[10.0, 20.0, 30.0, 40.0]]))
-    assert columns["lickspout_position_x"] == 10.0
-    assert columns["lickspout_position_y2"] == 30.0
+    mm_per_step = builder._manipulator_mm_per_step(_rig())
+    assert mm_per_step == {"x": 0.00125, "y1": 0.00125, "y2": 0.00125, "z": 0.00125}
 
 
-def test_lickspout_columns_unrecognized_payload_is_null():
-    """An unrecognized payload leaves all lickspout positions ``None``."""
+def test_manipulator_mm_per_step_respects_resolution():
+    """A finer microstep resolution shrinks the distance per step."""
     builder = TrialTableBuilder(_Node({}))
-    columns = builder._lickspout_columns(_events([1.0], ["unexpected"]))
-    assert all(value is None for value in columns.values())
+    mm_per_step = builder._manipulator_mm_per_step(
+        _rig(step_mm=0.01, resolution=MicrostepResolution.MICROSTEP16)
+    )
+    assert mm_per_step["x"] == pytest.approx(0.01 / 16)
+
+
+def test_manipulator_mm_per_step_missing_axis_raises():
+    """A calibration lacking one of the four axes raises ``ValueError``."""
+    builder = TrialTableBuilder(_Node({}))
+    rig = _rig()
+    # Drop the Z axis configuration.
+    rig.manipulator.calibration.axis_configuration = [
+        config
+        for config in rig.manipulator.calibration.axis_configuration
+        if config.axis is not Axis.Z
+    ]
+    with pytest.raises(ValueError, match="missing axis 'z'"):
+        builder._manipulator_mm_per_step(rig)
+
+
+def test_manipulator_positions_converts_steps_to_mm_relative_to_start():
+    """EVENT rows convert to mm and are re-referenced to the session-start sample."""
+    builder = TrialTableBuilder(_Node({}))
+    steps = _accumulated_steps([8.0, 15.0], [(8000, 1600, 2400, 4000), (12000, 1600, 2400, 4000)])
+    positions = builder._manipulator_positions(steps, _rig())
+    assert list(positions.index) == [8.0, 15.0]
+    # First sample is the baseline (zeroed); x then moves +5.0 mm (4000 steps).
+    assert positions.loc[8.0, "lickspout_position_x"] == 0.0
+    assert positions.loc[15.0, "lickspout_position_x"] == 5.0
+    assert positions.loc[8.0, "lickspout_position_z"] == 0.0
+
+
+def test_manipulator_positions_filters_non_event_rows():
+    """Only ``EVENT`` rows contribute to the position frame."""
+    builder = TrialTableBuilder(_Node({}))
+    steps = _accumulated_steps([8.0, 15.0], [(8000, 1600, 2400, 4000), (12000, 1600, 2400, 4000)])
+    steps.loc[15.0, "MessageType"] = "WRITE"
+    positions = builder._manipulator_positions(steps, _rig())
+    assert list(positions.index) == [8.0]
+
+
+def test_manipulator_positions_all_non_event_raises():
+    """A stream with no ``EVENT`` rows raises ``ValueError``."""
+    builder = TrialTableBuilder(_Node({}))
+    steps = _accumulated_steps([8.0], [(8000, 1600, 2400, 4000)])
+    steps["MessageType"] = "WRITE"
+    with pytest.raises(ValueError, match="no EVENT rows"):
+        builder._manipulator_positions(steps, _rig())
+
+
+def test_manipulator_positions_missing_motor_column_raises():
+    """A stream missing a Motor column raises ``ValueError``."""
+    builder = TrialTableBuilder(_Node({}))
+    steps = _accumulated_steps([8.0], [(8000, 1600, 2400, 4000)]).drop(columns=["Motor3"])
+    with pytest.raises(ValueError, match="missing column Motor3"):
+        builder._manipulator_positions(steps, _rig())
+
+
+def test_manipulator_positions_incomplete_calibration_raises():
+    """Steps present but a calibration axis missing raises ``ValueError``."""
+    builder = TrialTableBuilder(_Node({}))
+    steps = _accumulated_steps([8.0], [(8000, 1600, 2400, 4000)])
+    rig = _rig()
+    rig.manipulator.calibration.axis_configuration = [
+        config
+        for config in rig.manipulator.calibration.axis_configuration
+        if config.axis is not Axis.Z
+    ]
+    with pytest.raises(ValueError, match="missing axis 'z'"):
+        builder._manipulator_positions(steps, rig)
+
+
+def _two_sample_positions(builder):
+    """Build a position frame relative to session start: t=8.0 (x=0.0), t=15.0 (x=5.0)."""
+    return builder._manipulator_positions(
+        _accumulated_steps([8.0, 15.0], [(8000, 1600, 2400, 4000), (12000, 1600, 2400, 4000)]),
+        _rig(),
+    )
+
+
+def test_sample_lickspout_picks_sample_nearest_start_in_window():
+    """A trial takes the in-window sample nearest its start."""
+    builder = TrialTableBuilder(_Node({}))
+    positions = _two_sample_positions(builder)
+    # Window [7.0, 12.0) contains only the t=8.0 baseline sample (0.0).
+    assert builder._sample_lickspout(positions, 7.0, 12.0)["lickspout_position_x"] == 0.0
+    # Window [14.0, 20.0) contains only the t=15.0 sample (+5.0 mm).
+    assert builder._sample_lickspout(positions, 14.0, 20.0)["lickspout_position_x"] == 5.0
 
 
-def test_lickspout_columns_missing_stream_is_null():
-    """A missing manipulator stream leaves all lickspout positions ``None``."""
+def test_sample_lickspout_no_sample_in_window_is_null():
+    """A trial whose window contains no sample yields ``None`` columns."""
     builder = TrialTableBuilder(_Node({}))
-    columns = builder._lickspout_columns(None)
+    positions = _two_sample_positions(builder)
+    columns = builder._sample_lickspout(positions, 20.0, 30.0)
     assert all(value is None for value in columns.values())
diff --git a/tests/test_qc/test_behavior.py b/tests/test_qc/test_behavior.py
index 8fff460..d366034 100644
--- a/tests/test_qc/test_behavior.py
+++ b/tests/test_qc/test_behavior.py
@@ -49,7 +49,7 @@ def test_side_bias_result_pass_and_fail():
     assert passing.passed is True
     assert passing.value == pytest.approx(0.0)
     assert passing.reference == _behavior.SIDE_BIAS_PLOT
-    assert passing.tags == {"behavior": "average side bias"}
+    assert passing.tags == {"type": "Average_Side_Bias"}
 
     failing = _behavior.side_bias_result(np.array([0.8, 0.9, 1.0]))
     assert failing.passed is False
@@ -75,7 +75,7 @@ def test_lick_interval_results_names_and_count():
         "Artifact Percent (%)",
     ]
     assert all(r.reference == _behavior.LICK_INTERVALS_PLOT for r in results)
-    assert all(r.tags == {"behavior": r.name} for r in results)
+    assert all(r.tags == {"metric": r.name, "type": "Lick_Interval"} for r in results)
 
 
 def test_reference_includes_results_folder_name():
diff --git a/tests/test_qc/test_builder.py b/tests/test_qc/test_builder.py
index 69507e8..bbf97e5 100644
--- a/tests/test_qc/test_builder.py
+++ b/tests/test_qc/test_builder.py
@@ -29,6 +29,22 @@ def test_behavior_qc_results_without_plots():
     assert results[0].name == "average side bias"
 
 
+def test_lickspout_columns_map_to_trial_table_names():
+    """The lickspout mapping resolves to the ``lickspout_position_*`` trial columns."""
+    trials = pd.DataFrame(
+        {
+            "lickspout_position_x": [16.0, 16.0],
+            "lickspout_position_y1": [9.6, 9.6],
+            "lickspout_position_y2": [9.7, 9.7],
+            "lickspout_position_z": [17.25, 17.25],
+        }
+    )
+    for key in ("lickspout_x", "lickspout_y1", "lickspout_y2", "lickspout_z"):
+        column = _results._column(trials, key)
+        assert column is not None, key
+        assert column[0] == trials[_results._COLUMNS[key]][0]
+
+
 def test_behavior_qc_results_writes_plots(tmp_path):
     """Supplying a results folder writes both behavior plots."""
     trials = pd.DataFrame(
diff --git a/uv.lock b/uv.lock
index 798d492..7f499f5 100644
--- a/uv.lock
+++ b/uv.lock
@@ -39,7 +39,7 @@ wheels = [
 [[package]]
 name = "aind-behavior-dynamic-foraging"
 version = "0.0.2"
-source = { git = "https://github.com/AllenNeuralDynamics/Aind.Behavior.DynamicForaging.git?rev=baab12133b22f599c1ba0583260eca9eca216cc0#baab12133b22f599c1ba0583260eca9eca216cc0" }
+source = { git = "https://github.com/AllenNeuralDynamics/Aind.Behavior.DynamicForaging.git?rev=ac5ddbf909c9375b9e8875d6a5f90796cfa98653#ac5ddbf909c9375b9e8875d6a5f90796cfa98653" }
 dependencies = [
     { name = "aind-behavior-services" },
     { name = "pydantic-settings" },
@@ -53,7 +53,7 @@ data = [
 
 [[package]]
 name = "aind-behavior-services"
-version = "0.13.7"
+version = "0.13.8"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aind-behavior-curriculum" },
@@ -62,9 +62,9 @@ dependencies = [
     { name = "pydantic" },
     { name = "semver" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/69/6d/540d7221485fb04ae4f04f99a57c930035c39ecb2b899937828aae81936f/aind_behavior_services-0.13.7.tar.gz", hash = "sha256:2ef5a28ea5918c059cf4444e105ce00068d5285193eb63bf7f87b89db2d1b5d2", size = 29573, upload-time = "2026-04-18T22:20:04.267Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/9c/88/b343182793b3063665cbf277174a724442a386f1376776f005bff8c8273b/aind_behavior_services-0.13.8.tar.gz", hash = "sha256:2408fc34d5fa48437ff7073cc0d9699004ef95ef4d66b6be16b68487838ecbe1", size = 29873, upload-time = "2026-07-28T00:51:58.471Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/bc/bf/423d28364d564008a33d40d7aad1fbbed5e028b5bda39ed5cafc41651d7e/aind_behavior_services-0.13.7-py3-none-any.whl", hash = "sha256:ab57927ce283cb07a47c4b02eecee8038421f8292c5efbeeaaf907fd6a69eb08", size = 39614, upload-time = "2026-04-18T22:20:02.941Z" },
+    { url = "https://files.pythonhosted.org/packages/f1/08/1c0828788aa29f8acca3a9a2ea30ed7faa5611685739549a1c2637b1232a/aind_behavior_services-0.13.8-py3-none-any.whl", hash = "sha256:acb349c63eebb7d7bb832c81d823a5eaed6e549b316a4872ad7badef96a5bdcb", size = 39881, upload-time = "2026-07-28T00:51:57.292Z" },
 ]
 
 [[package]]
@@ -98,7 +98,7 @@ wheels = [
 
 [[package]]
 name = "aind-nwb-utils"
-version = "0.2.6"
+version = "0.2.7"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "fsspec" },
@@ -108,14 +108,14 @@ dependencies = [
     { name = "s3fs" },
     { name = "xarray" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/d8/f2/cf69578d86fe85a533a12f558be893408ea3531774ad4312fcd1c2460923/aind_nwb_utils-0.2.6.tar.gz", hash = "sha256:7ead270c3ece6ff3ceaeeac4e156ac9fce39659414653a9581b3c7bc589a42af", size = 39200465, upload-time = "2026-07-02T17:45:54.678Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/71/37/1c60c2fbb49b584e3ab78784212f750f2bfef00c0a30fa327e106c7ff3f6/aind_nwb_utils-0.2.7.tar.gz", hash = "sha256:d83a3a2457f167746b2b51b66876f858b7f668f36b547f7d00b415de5eba8a62", size = 39201001, upload-time = "2026-07-28T18:50:46.684Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/ee/4a/89f26ca1cfaccd050cd3030326f680c88007349627bf7b8face171516f81/aind_nwb_utils-0.2.6-py3-none-any.whl", hash = "sha256:5ad41e9f2b892433fc20976b722cf358041d0ef1964641def6a7336d8ba33ae5", size = 14327, upload-time = "2026-07-02T17:45:52.982Z" },
+    { url = "https://files.pythonhosted.org/packages/9b/66/3e652cebf82127253dc9cb138c22bded6214976935687fc87a236025d036/aind_nwb_utils-0.2.7-py3-none-any.whl", hash = "sha256:ed552bf0243df382769f5a9a1c8d795d7154160587730cce8871d8c66f080b1d", size = 14365, upload-time = "2026-07-28T18:50:45.185Z" },
 ]
 
 [[package]]
 name = "aiobotocore"
-version = "3.7.0"
+version = "3.8.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohttp" },
@@ -126,9 +126,9 @@ dependencies = [
     { name = "python-dateutil" },
     { name = "wrapt" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/e7/75/42cce839c2ec263ff74b10b650fe36b066fbb124cbee6f247eac0983e1ab/aiobotocore-3.7.0.tar.gz", hash = "sha256:c64d871ed5491a6571948dd48eabd185b46c6c23b64e3afd0c059fc7593ada30", size = 127054, upload-time = "2026-05-09T10:02:52.332Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/d8/a7/bc31b7046c610471f0630819ca5d2a57ac4efa8d47135cb53e43f2785390/aiobotocore-3.8.0.tar.gz", hash = "sha256:80a1eb64ea915f3af3c1518669975bae74a17b2f37c14eb0fa2f83b915974670", size = 131368, upload-time = "2026-07-17T03:10:30.258Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/90/5f/85535dfb3cfd6442d66d1df1694062c5d6df02f895329e7e120b2a3d2b8b/aiobotocore-3.7.0-py3-none-any.whl", hash = "sha256:680bde7c64679a821a9312641b759d9497f790ba8b2e88c6959e6273ee765b8e", size = 89539, upload-time = "2026-05-09T10:02:50.389Z" },
+    { url = "https://files.pythonhosted.org/packages/6d/f4/5a7d76dc844d3ff8ed1f1a043158aa393794aebb787d3e2f8c0fe87f674f/aiobotocore-3.8.0-py3-none-any.whl", hash = "sha256:8bc605132cadfe844a3f334635a0a64fa5e360a4a206e915d99d53db5b6deeba", size = 91169, upload-time = "2026-07-17T03:10:28.771Z" },
 ]
 
 [[package]]
@@ -142,7 +142,7 @@ wheels = [
 
 [[package]]
 name = "aiohttp"
-version = "3.14.1"
+version = "3.14.3"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohappyeyeballs" },
@@ -154,90 +154,90 @@ dependencies = [
     { name = "typing-extensions", marker = "python_full_version < '3.13'" },
     { name = "yarl" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/82/78/8ea7308cac6934de8c74a14f3d5f65d1c89287426688be79538d0e5c013d/aiohttp-3.14.1.tar.gz", hash = "sha256:307f2cff90a764d329e77040603fa032db89c5c24fdad50c4c15334cba744035", size = 7955794, upload-time = "2026-06-07T21:09:35.529Z" }
-wheels = [
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