diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml
index 07fe787af..ab5bb219c 100644
--- a/.github/workflows/lint.yml
+++ b/.github/workflows/lint.yml
@@ -2,9 +2,9 @@ name: Lint
 
 on:
   push:
-    branches: [main]
+    branches: [main, modular-viscy-staging]
   pull_request:
-    branches: [main]
+    branches: [main, modular-viscy-staging]
 
 concurrency:
   group: ${{ github.workflow }}-${{ github.ref }}
diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
index 5bc5b03ea..616044d9a 100644
--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@@ -2,9 +2,9 @@ name: Test
 
 on:
   push:
-    branches: [main]
+    branches: [main, modular-viscy-staging]
   pull_request:
-    branches: [main]
+    branches: [main, modular-viscy-staging]
 
 concurrency:
   group: ${{ github.workflow }}-${{ github.ref }}
@@ -18,7 +18,7 @@ jobs:
       fail-fast: true
       matrix:
         os: [ubuntu-latest, macos-latest, windows-latest]
-        python-version: ["3.11", "3.12", "3.13"]
+        python-version: ["3.12", "3.13"]
         package: [viscy-transforms, viscy-models]
 
     steps:
@@ -47,7 +47,7 @@ jobs:
       fail-fast: true
       matrix:
         os: [ubuntu-latest, macos-latest, windows-latest]
-        python-version: ["3.11", "3.12", "3.13"]
+        python-version: ["3.12", "3.13"]
 
     steps:
       - name: Checkout repository
@@ -133,19 +133,24 @@ jobs:
           enable-cache: true
           cache-suffix: ubuntu-latest-3.13
 
-      - name: Install minimal dynacell (base deps + test group)
-        run: uv sync --frozen --group test
+      - name: Install dynacell with eval deps + test group (CPU)
+        # The eval-pipeline tests import dynacell.evaluation.pipeline, a hard
+        # consumer of the eval stack (cubic, cellpose, …). cubic is CPU-capable
+        # (falls back to numpy/scikit-image without CUDA), so the eval extra
+        # installs and runs on a GPU-less runner. eval_gpu (cupy/cucim) is NOT
+        # installed — those are the only CUDA-only deps.
+        run: uv sync --frozen --extra eval --group test
         working-directory: applications/dynacell
 
       - name: Run benchmark-schema + submit-tool + eval-runtime tests
-        # tests/test_runtime.py + tests/test_evaluation_pipeline_parallel.py
-        # cover the dynacell.evaluation.runtime module + FovResult pickle
-        # contract. tests/test_evaluation_pipeline_parallel_cpu.py drives
-        # evaluate_predictions end-to-end (serial vs spawn-process) on a
-        # tiny iohub fixture + prebuilt mask cache — no eval extras (no
-        # cellpose, transformers, cubic) needed since target_name=er +
-        # require_complete_cache=true short-circuit segmenter and feature
-        # extractor loads. tests/test_evaluation_grouped.py drives the
+        # test_benchmark_config_composition + test_submit_benchmark_job cover
+        # config composition + the launcher. test_runtime + the two
+        # test_evaluation_pipeline_parallel* suites cover the runtime module,
+        # the FovResult pickle contract, and evaluate_predictions end-to-end
+        # (serial vs spawn-process) on a tiny iohub fixture + prebuilt mask
+        # cache (target_name=er + require_complete_cache=true short-circuit the
+        # segmenter + feature-extractor model loads — but pipeline import still
+        # needs the eval stack). test_evaluation_grouped drives the
         # multi-condition driver against the same cache-only fixture.
         run: |
           uv run --frozen pytest \
diff --git a/applications/airtable/pyproject.toml b/applications/airtable/pyproject.toml
index 9b11bc441..4504690f7 100644
--- a/applications/airtable/pyproject.toml
+++ b/applications/airtable/pyproject.toml
@@ -23,7 +23,7 @@ classifiers = [
 dynamic = [ "version" ]
 dependencies = [
   "click",
-  "iohub",
+  "iohub>=0.3.6",
   "pandas",
   "pyairtable",
   "pydantic",
diff --git a/applications/dynacell/pyproject.toml b/applications/dynacell/pyproject.toml
index 468727d80..6ec61c954 100644
--- a/applications/dynacell/pyproject.toml
+++ b/applications/dynacell/pyproject.toml
@@ -29,6 +29,7 @@ classifiers = [
 ]
 dynamic = [ "version" ]
 dependencies = [
+  "hydra-core>=1.2",
   "lightning>=2.3",
   "monai",
   "omegaconf",
@@ -46,7 +47,7 @@ optional-dependencies.eval = [
   "cubic==0.7.0a9",
   "dynaclr",
   "hydra-core>=1.2",
-  "iohub",
+  "iohub>=0.3.6",
   "itk",
   "matplotlib",
   "pandas",
@@ -72,7 +73,7 @@ optional-dependencies.eval_gpu = [
   "cupy-cuda13x",
 ]
 optional-dependencies.preprocess = [
-  "iohub",
+  "iohub>=0.3.6",
   "tqdm",
 ]
 optional-dependencies.report = [
diff --git a/applications/dynacell/src/dynacell/evaluation/cross_condition_probe.py b/applications/dynacell/src/dynacell/evaluation/cross_condition_probe.py
index 0b554931b..6a80a6a6f 100644
--- a/applications/dynacell/src/dynacell/evaluation/cross_condition_probe.py
+++ b/applications/dynacell/src/dynacell/evaluation/cross_condition_probe.py
@@ -40,6 +40,20 @@
 _SOURCES = ("pred", "gt")
 _CONDITION_TOKENS = ("mock", "denv", "zikv")
 _DEFAULT_PAIRS = (("mock", "denv"), ("mock", "zikv"))
+_FIELDNAMES = (
+    "feature_type",
+    "pair",
+    "source",
+    "n_cells_c0",
+    "n_cells_c1",
+    "n_fovs",
+    "n_folds",
+    "auroc_mean",
+    "auroc_std",
+    "skipped_reason",
+)
+#: Filename written into each infected condition's eval dir by :func:`run_for_group`.
+GROUP_PROBE_FILENAME = "cross_condition_probe.csv"
 
 
 def _detect_condition(eval_dir: Path) -> str:
@@ -51,14 +65,31 @@ def _detect_condition(eval_dir: Path) -> str:
     raise ValueError(f"cannot infer condition from eval_dir name {name!r}: expected trailing _{{mock,denv,zikv}}")
 
 
-def _load_embeddings(eval_dir: Path, source: str, feature: str) -> tuple[np.ndarray, np.ndarray]:
+def _load_embeddings(
+    eval_dir: Path,
+    source: str,
+    feature: str,
+    cache: dict[Path, tuple[np.ndarray, np.ndarray]] | None = None,
+) -> tuple[np.ndarray, np.ndarray]:
     """Return ``(embeddings, fov_ids)`` from one ``*_single_cell_embeddings.npz``.
 
-    ``np.load`` raises ``FileNotFoundError`` when the NPZ is missing.
+    ``np.load`` raises ``FileNotFoundError`` when the NPZ is missing. When
+    *cache* is given, the result is memoized on the resolved NPZ path so the
+    shared reference (``mock``) side is read from disk once per group instead
+    of once per pair (see :func:`run` / :func:`run_for_group`). The key is
+    resolved so the same file reached via different relative paths/symlinks
+    still hits the cache.
     """
-    npz_path = eval_dir / "embeddings" / f"{source}_{feature}_single_cell_embeddings.npz"
+    # ``.resolve()`` (strict=False) canonicalizes the key without requiring the
+    # file to exist — np.load still raises FileNotFoundError below if it's missing.
+    npz_path = (eval_dir / "embeddings" / f"{source}_{feature}_single_cell_embeddings.npz").resolve()
+    if cache is not None and npz_path in cache:
+        return cache[npz_path]
     with np.load(npz_path) as data:
-        return np.asarray(data["embeddings"]), np.asarray(data["fov"])
+        result = (np.asarray(data["embeddings"]), np.asarray(data["fov"]))
+    if cache is not None:
+        cache[npz_path] = result
+    return result
 
 
 def _probe_pair(
@@ -68,6 +99,7 @@ def _probe_pair(
     source: str,
     n_splits: int,
     rng_seed: int,
+    cache: dict[Path, tuple[np.ndarray, np.ndarray]] | None = None,
 ) -> dict:
     """Run one ``fov_stratified_auroc`` call for the given (pair, feature, source).
 
@@ -92,8 +124,8 @@ def _probe_pair(
         row["skipped_reason"] = "missing eval dir for one side of pair"
         return row
     try:
-        x0, fov0 = _load_embeddings(eval_dirs_by_condition[c0], source, feature)
-        x1, fov1 = _load_embeddings(eval_dirs_by_condition[c1], source, feature)
+        x0, fov0 = _load_embeddings(eval_dirs_by_condition[c0], source, feature, cache)
+        x1, fov1 = _load_embeddings(eval_dirs_by_condition[c1], source, feature, cache)
     except FileNotFoundError as e:
         row["skipped_reason"] = f"missing embeddings file: {e}"
         return row
@@ -135,6 +167,75 @@ def _probe_pair(
     return row
 
 
+def _write_rows(out_path: Path, rows: list[dict]) -> None:
+    """Write probe rows as a CSV with the canonical field order."""
+    out_path.parent.mkdir(parents=True, exist_ok=True)
+    with out_path.open("w", newline="") as f:
+        writer = csv.DictWriter(f, fieldnames=_FIELDNAMES)
+        writer.writeheader()
+        writer.writerows(rows)
+
+
+def run_for_group(
+    eval_dirs: list[Path],
+    n_splits: int = 5,
+    rng_seed: int = 2020,
+) -> list[Path]:
+    """Probe each infected condition against mock and write a per-condition CSV.
+
+    Unlike :func:`run` (long-form CSV over all pairs at one ``out_path``),
+    this writes one :data:`GROUP_PROBE_FILENAME` into *each infected
+    condition's* eval dir, holding only that condition's ``mock_vs_<cond>``
+    rows (every feature × {pred, gt}). This colocates the probe with the eval
+    dir the reporting layer already resolves per (model, pool, organelle,
+    condition), so the table generator can read it without knowing about
+    sibling conditions.
+
+    Requires a ``mock`` reference dir plus at least one infected dir; returns
+    the list of CSV paths written (empty when the group has no mock or no
+    infected condition, e.g. the in-distribution iPSC eval).
+
+    Parameters
+    ----------
+    eval_dirs : list[Path]
+        Per-condition eval dirs of one (model, pool, organelle) group. The
+        condition is inferred from each dir's trailing ``_{mock,denv,zikv}``;
+        dirs without a recognized token are ignored. Two dirs mapping to the
+        same condition raise ``ValueError`` (an ambiguous group) rather than
+        silently picking one.
+    n_splits, rng_seed : int
+        Forwarded to :func:`fov_stratified_auroc`.
+    """
+    by_condition: dict[str, Path] = {}
+    for d in eval_dirs:
+        try:
+            cond = _detect_condition(d)
+        except ValueError:
+            continue
+        if cond in by_condition:
+            raise ValueError(f"duplicate condition {cond!r}: {by_condition[cond]} and {d}")
+        by_condition[cond] = d
+    if "mock" not in by_condition:
+        return []
+
+    # Shared across pairs so the mock reference embeddings are read once, not
+    # re-read for every infected condition. Local to this call -> released on return.
+    cache: dict[Path, tuple[np.ndarray, np.ndarray]] = {}
+    written: list[Path] = []
+    for ref, cond in _DEFAULT_PAIRS:  # ref == "mock" for every default pair
+        if cond not in by_condition:
+            continue
+        rows = [
+            _probe_pair(by_condition, (ref, cond), feature, source, n_splits, rng_seed, cache)
+            for feature in _FEATURE_TYPES
+            for source in _SOURCES
+        ]
+        out_path = by_condition[cond] / GROUP_PROBE_FILENAME
+        _write_rows(out_path, rows)
+        written.append(out_path)
+    return written
+
+
 def run(
     eval_dirs: list[Path],
     out_path: Path,
@@ -163,29 +264,16 @@ def run(
             raise ValueError(f"duplicate condition {cond!r}: {eval_dirs_by_condition[cond]} and {d}")
         eval_dirs_by_condition[cond] = d
 
+    # Shared across pairs so a condition's embeddings (e.g. the mock reference
+    # reused by every pair) are read once. Local to this call -> released on return.
+    cache: dict[Path, tuple[np.ndarray, np.ndarray]] = {}
     rows = []
     for feature in _FEATURE_TYPES:
         for pair in pairs:
             for source in _SOURCES:
-                rows.append(_probe_pair(eval_dirs_by_condition, pair, feature, source, n_splits, rng_seed))
+                rows.append(_probe_pair(eval_dirs_by_condition, pair, feature, source, n_splits, rng_seed, cache))
 
-    out_path.parent.mkdir(parents=True, exist_ok=True)
-    fieldnames = [
-        "feature_type",
-        "pair",
-        "source",
-        "n_cells_c0",
-        "n_cells_c1",
-        "n_fovs",
-        "n_folds",
-        "auroc_mean",
-        "auroc_std",
-        "skipped_reason",
-    ]
-    with out_path.open("w", newline="") as f:
-        writer = csv.DictWriter(f, fieldnames=fieldnames)
-        writer.writeheader()
-        writer.writerows(rows)
+    _write_rows(out_path, rows)
     return out_path
 
 
diff --git a/applications/dynacell/src/dynacell/evaluation/io.py b/applications/dynacell/src/dynacell/evaluation/io.py
index dcf05fc49..4ac5dfca7 100644
--- a/applications/dynacell/src/dynacell/evaluation/io.py
+++ b/applications/dynacell/src/dynacell/evaluation/io.py
@@ -29,18 +29,14 @@ def _require_skimage():
 
 
 def _require_cubic():
+    # cubic only: callers fall back to the numpy/CPU path when no GPU is
+    # present, so do not hard-require the eval_gpu (cupy/cucim) stack here — that
+    # would block CPU-only use. ``ascupy`` raises a clear error if a GPU upload
+    # is attempted without cupy.
     if ascupy is None:
         raise ImportError(
             "cubic is required for GPU array operations. Install via the `eval` extra: `uv sync --extra eval`."
         )
-    try:
-        import cucim  # noqa: F401
-        import cupy  # noqa: F401
-    except ImportError as e:
-        raise ImportError(
-            f"{e.name} is required for GPU-backed I/O. Install cupy-cuda12x "
-            "and cucim-cu12 via the `eval_gpu` extra: `uv sync --extra eval_gpu`."
-        ) from e
 
 
 def _is_zarr_path(path: Path) -> bool:
diff --git a/applications/dynacell/src/dynacell/evaluation/metrics.py b/applications/dynacell/src/dynacell/evaluation/metrics.py
index c5e47ff60..61b01bc05 100644
--- a/applications/dynacell/src/dynacell/evaluation/metrics.py
+++ b/applications/dynacell/src/dynacell/evaluation/metrics.py
@@ -24,19 +24,17 @@
 
 
 def _require_cubic():
+    # Only cubic itself is required: the metric helpers below gate the GPU
+    # upload on ``torch.cuda.is_available()`` and otherwise run on numpy, where
+    # cubic dispatches to its CPU (numpy / scikit-image) path. cucim / cupy are
+    # needed only when a GPU is actually present and used — and ``ascupy`` raises
+    # a clear "GPU requested but not available" there if they are missing — so
+    # this must NOT hard-require the eval_gpu stack (it would block the CPU path).
     if ascupy is None:
         raise ImportError(
             "cubic is required for resolution and feature metrics. "
             "Install via the `eval` extra: `uv sync --extra eval`."
         )
-    try:
-        import cucim  # noqa: F401
-        import cupy  # noqa: F401
-    except ImportError as e:
-        raise ImportError(
-            f"{e.name} is required for GPU-backed metrics. Install cupy-cuda12x "
-            "and cucim-cu12 via the `eval_gpu` extra: `uv sync --extra eval_gpu`."
-        ) from e
 
 
 @torch.inference_mode()
diff --git a/applications/dynacell/tests/test_benchmark_config_composition.py b/applications/dynacell/tests/test_benchmark_config_composition.py
index 2bfc77d24..8a4252e90 100644
--- a/applications/dynacell/tests/test_benchmark_config_composition.py
+++ b/applications/dynacell/tests/test_benchmark_config_composition.py
@@ -42,6 +42,13 @@
     (organelle, model) for organelle in ("er", "mito", "nucleus", "membrane") for model in ("celldiff", "unetvit3d")
 ]
 
+# celldiff predict leaves are split per inference method; unetvit3d has a single file.
+PREDICT_LEAF_FILES = [
+    (organelle, "celldiff", f"predict__ipsc_confocal__{method}.yml")
+    for organelle in ("er", "mito", "nucleus", "membrane")
+    for method in ("denoise", "iterative", "sliding_window")
+] + [(organelle, "unetvit3d", "predict__ipsc_confocal.yml") for organelle in ("er", "mito", "nucleus", "membrane")]
+
 
 @pytest.mark.parametrize("organelle,model", TRAIN_LEAVES)
 def test_train_leaf_composes(organelle: str, model: str) -> None:
@@ -56,11 +63,11 @@ def test_train_leaf_composes(organelle: str, model: str) -> None:
     assert "precision" in t
 
 
-@pytest.mark.parametrize("organelle,model", PREDICT_LEAVES)
-def test_predict_leaf_composes(organelle: str, model: str, monkeypatch) -> None:
+@pytest.mark.parametrize("organelle,model,predict_file", PREDICT_LEAF_FILES)
+def test_predict_leaf_composes(organelle: str, model: str, predict_file: str, monkeypatch) -> None:
     """Predict leaves compose and point at the test_cropped store."""
     monkeypatch.setattr("sys.argv", ["dynacell", "predict"])
-    leaf = BENCHMARKS / organelle / model / "ipsc_confocal" / "predict__ipsc_confocal.yml"
+    leaf = BENCHMARKS / organelle / model / "ipsc_confocal" / predict_file
     cfg = load_composed_config(leaf, resolver=_dynacell_ref_resolver)
     t = cfg["trainer"]
     assert t["accelerator"] == "gpu"
@@ -237,11 +244,21 @@ def test_migrated_target_train_resolves_to_manifest_paths(organelle: str, model:
     assert ia["target_channel"] == target_channel
 
 
+# celldiff predict leaves are split per inference method; unetvit3d has a single
+# file. The test store + channel resolution is identical across the three
+# celldiff method variants (they differ only in model/predict-method fields),
+# so we pick sliding_window as the representative for celldiff.
+_MIGRATED_PREDICT_FILES = {
+    "celldiff": "predict__ipsc_confocal__sliding_window.yml",
+    "unetvit3d": "predict__ipsc_confocal.yml",
+}
+
+
 @pytest.mark.parametrize("organelle,model", _MIGRATED_PREDICT_LEAVES)
 def test_migrated_target_predict_resolves_to_test_store(organelle: str, model: str, monkeypatch) -> None:
     """Full dataset_ref on a migrated predict leaf splices the test store + channels."""
     monkeypatch.setattr("sys.argv", ["dynacell", "predict"])
-    leaf = BENCHMARKS / organelle / model / "ipsc_confocal" / "predict__ipsc_confocal.yml"
+    leaf = BENCHMARKS / organelle / model / "ipsc_confocal" / _MIGRATED_PREDICT_FILES[model]
     cfg = load_composed_config(leaf, resolver=_dynacell_ref_resolver)
     _, test_store, target_channel = _MIGRATED_TARGET_INFO[organelle]
     ia = cfg["data"]["init_args"]
@@ -252,90 +269,52 @@ def test_migrated_target_predict_resolves_to_test_store(organelle: str, model: s
 
 # -- Stage 6: A549 cross-eval predict leaves ------------------------------
 
-# Each cross-eval cell → (plate_date, target_slug, target_channel,
-# gt_test_substring). Plate is per-organelle because the a549 plates are
-# split: 2024_11_07 has SEC61B, 2024_11_21 has TOMM20, 2026_03_26 has both
-# h2b (nucleus) + caax (membrane). Target_slug is the manifest's
-# target key — same as iPSC for er/mito but gene-keyed for a549's
-# nucleus + membrane.
+# The a549_mantis predict leaf was split per treatment condition into
+# predict__a549_mantis_{mock,denv,zikv}.yml. Each condition points at the
+# condition-pooled test store in mantis_v1/test/. Per-organelle the gene
+# slug differs: er → SEC61B, mito → TOMM20, nucleus → H2B, membrane → CAAX.
+# The condition flag in the store filename is mock → "_mock", denv →
+# "_DENV", zikv → "_ZIKV".
+#
+# Each cross-eval cell → (organelle, model, condition, gene_slug,
+# target_channel, store_filename). dataset_ref.dataset is
+# f"a549-mantis-{gene_slug}-{condition}", dataset_ref.target is gene_slug,
+# and experiment_id is
+# f"{organelle}__ipsc_confocal__{model}__a549_mantis_{gene_slug}_{condition}".
+_A549_GENE_INFO = {
+    "er": ("sec61b", "SEC61B", "Structure"),
+    "mito": ("tomm20", "TOMM20", "Structure"),
+    "nucleus": ("h2b", "H2B", "Nuclei"),
+    "membrane": ("caax", "CAAX", "Membrane"),
+}
+# Condition → suffix in the test store filename.
+_A549_CONDITION_SUFFIX = {"mock": "_mock", "denv": "_DENV", "zikv": "_ZIKV"}
+
 _A549_PREDICT_EXPECTATIONS = [
     (
-        "er",
-        "celldiff",
-        "2024_11_07",
-        "sec61b",
-        "Structure",
-        "2024_11_07_A549_SEC61_DENV/test/SEC61B.zarr",
-    ),
-    (
-        "er",
-        "unetvit3d",
-        "2024_11_07",
-        "sec61b",
-        "Structure",
-        "2024_11_07_A549_SEC61_DENV/test/SEC61B.zarr",
-    ),
-    (
-        "mito",
-        "celldiff",
-        "2024_11_21",
-        "tomm20",
-        "Structure",
-        "2024_11_21_A549_TOMM20_DENV/test/TOMM20.zarr",
-    ),
-    (
-        "mito",
-        "unetvit3d",
-        "2024_11_21",
-        "tomm20",
-        "Structure",
-        "2024_11_21_A549_TOMM20_DENV/test/TOMM20.zarr",
-    ),
-    (
-        "nucleus",
-        "celldiff",
-        "2026_03_26",
-        "h2b",
-        "Nuclei",
-        "2026_03_26_A549_CAAX_H2B_DENV_ZIKV/test/H2B.zarr",
-    ),
-    (
-        "nucleus",
-        "unetvit3d",
-        "2026_03_26",
-        "h2b",
-        "Nuclei",
-        "2026_03_26_A549_CAAX_H2B_DENV_ZIKV/test/H2B.zarr",
-    ),
-    (
-        "membrane",
-        "celldiff",
-        "2026_03_26",
-        "caax",
-        "Membrane",
-        "2026_03_26_A549_CAAX_H2B_DENV_ZIKV/test/CAAX.zarr",
-    ),
-    (
-        "membrane",
-        "unetvit3d",
-        "2026_03_26",
-        "caax",
-        "Membrane",
-        "2026_03_26_A549_CAAX_H2B_DENV_ZIKV/test/CAAX.zarr",
-    ),
+        organelle,
+        model,
+        condition,
+        gene_slug,
+        target_channel,
+        f"mantis_v1/test/{gene_upper}{_A549_CONDITION_SUFFIX[condition]}.ozx",
+    )
+    for organelle, (gene_slug, gene_upper, target_channel) in _A549_GENE_INFO.items()
+    for model in ("celldiff", "unetvit3d")
+    for condition in ("mock", "denv", "zikv")
 ]
 
 
 @pytest.mark.parametrize(
-    "organelle,model,plate_date,target_slug,target_channel,gt_test_substring",
+    "organelle,model,condition,gene_slug,target_channel,gt_test_substring",
     _A549_PREDICT_EXPECTATIONS,
     ids=lambda v: v if isinstance(v, str) else None,
 )
 def test_a549_predict_leaf_composes(
     organelle: str,
     model: str,
-    plate_date: str,
-    target_slug: str,
+    condition: str,
+    gene_slug: str,
     target_channel: str,
     gt_test_substring: str,
     monkeypatch,
@@ -343,33 +322,35 @@ def test_a549_predict_leaf_composes(
     """Stage 6 cross-eval predict leaves compose against a549 manifests.
 
     Verifies for each cell:
-    - data.init_args.data_path resolves to the right plate's test store.
+    - data.init_args.data_path resolves to the condition's test store.
     - target_channel is a bare string (not a list) per _compose_hook.py.
     - dataset_ref.{dataset,target} carry through composition (proves the
-      nucleus → h2b and membrane → caax leaf-level overrides took effect).
-    - experiment_id reflects the cross-eval pairing.
-    - sbatch.constraint inherits "h200" from hardware_h200_single (these
-      are single-GPU predict leaves).
+      gene-keyed predict_set + targets overlays took effect).
+    - experiment_id reflects the cross-eval pairing (per condition).
+    - sbatch.constraint is unset (None): predict leaves use
+      hardware_predict_any_gpu (any GPU), not the H200 pin.
     """
     monkeypatch.setattr("sys.argv", ["dynacell", "predict"])
-    leaf = BENCHMARKS / organelle / model / "ipsc_confocal" / "predict__a549_mantis.yml"
+    leaf = BENCHMARKS / organelle / model / "ipsc_confocal" / f"predict__a549_mantis_{condition}.yml"
     assert leaf.is_file(), f"missing predict leaf: {leaf}"
     cfg = load_composed_config(leaf, resolver=_dynacell_ref_resolver)
 
     ia = cfg["data"]["init_args"]
     assert ia["data_path"].endswith(gt_test_substring), (
-        f"{organelle}/{model}: data_path={ia['data_path']!r} does not end with {gt_test_substring!r}"
+        f"{organelle}/{model}/{condition}: data_path={ia['data_path']!r} does not end with {gt_test_substring!r}"
     )
     assert ia["source_channel"] == "Phase3D"
     assert ia["target_channel"] == target_channel  # bare string, not [list]
 
     bench = cfg["benchmark"]
-    assert bench["dataset_ref"]["dataset"] == f"a549-mantis-{plate_date}"
-    assert bench["dataset_ref"]["target"] == target_slug
-    assert bench["experiment_id"] == f"{organelle}__ipsc_confocal__{model}__a549_mantis"
+    assert bench["dataset_ref"]["dataset"] == f"a549-mantis-{gene_slug}-{condition}"
+    assert bench["dataset_ref"]["target"] == gene_slug
+    assert bench["experiment_id"] == f"{organelle}__ipsc_confocal__{model}__a549_mantis_{gene_slug}_{condition}"
 
-    # Single-GPU predict topology: h200 only, not the 4-GPU alternation.
-    assert cfg["launcher"]["sbatch"].get("constraint") == "h200"
+    # Single-GPU predict topology, any GPU: the any-GPU profile composes an
+    # explicit constraint=null. Subscript (not .get) so a dropped hardware
+    # profile surfaces as a failure instead of silently passing.
+    assert cfg["launcher"]["sbatch"]["constraint"] is None
 
 
 def test_manifest_spacing_propagates(monkeypatch) -> None:
@@ -416,12 +397,12 @@ def test_synthetic_target_only_partial_ref_is_noop(tmp_path) -> None:
 
 def test_joint_train_leaf_composes() -> None:
     """First joint train leaf — BatchedConcatDataModule wrapping two
-    HCSDataModule children (ipsc SEC61B + a549_mantis_2024_11_07 SEC61B).
+    HCSDataModule children (ipsc SEC61B + a549_mantis pooled SEC61B).
 
     Joint leaves bypass the single-dataset resolver: no benchmark.dataset_ref.
     The data block is authored inline; the base chain composes only model
-    + launcher overlays. Topology is overridden to 4-GPU DDP because the
-    BatchedConcatDataModule sharded-sampler path is the whole point.
+    + launcher overlays. Topology is single H200 single-GPU — the leaf keeps
+    the paper baseline pattern so iPSC-only and joint runs are apples-to-apples.
     """
     leaf = BENCHMARKS / "er" / "celldiff" / "joint_ipsc_confocal_a549_mantis" / "train.yml"
     assert leaf.is_file(), f"joint leaf missing: {leaf}"
@@ -433,11 +414,11 @@ def test_joint_train_leaf_composes() -> None:
     leaked = [k for k in cfg if k.startswith("_")]
     assert not leaked, f"private anchor keys leaked into composed config: {leaked}"
 
-    # Topology: DDP 4-GPU overrides the single_gpu.yml pulled in by model_overlays.
+    # Topology: single GPU on H200 (hardware_h200_single.yml), no DDP override.
     t = cfg["trainer"]
     assert t["accelerator"] == "gpu"
-    assert t["strategy"] == "ddp"
-    assert t["devices"] == 4
+    assert t.get("strategy", "auto") != "ddp"
+    assert t["devices"] == 1
     assert t["num_nodes"] == 1
     assert t["precision"] == "bf16-mixed"
 
@@ -452,11 +433,14 @@ def test_joint_train_leaf_composes() -> None:
     for child in children:
         assert child["class_path"] == "viscy_data.hcs.HCSDataModule"
         ia = child["init_args"]
-        # Anchor-shared hparams reach each child.
-        assert ia["source_channel"] == ["Phase3D"]
-        assert ia["target_channel"] == ["Structure"]
+        # Anchor-shared hparams reach each child (bare-string channels).
+        assert ia["source_channel"] == "Phase3D"
+        assert ia["target_channel"] == "Structure"
         assert ia["z_window_size"] == 13
-        assert ia["batch_size"] == 4
+        # batch_size=2 (not the celldiff_fit.yml default of 4): BatchedConcatDataModule
+        # does NOT divide by num_samples (see CLAUDE.md), so the product is the
+        # effective per-step sample count.
+        assert ia["batch_size"] == 2
         assert ia["yx_patch_size"] == [512, 512]
         assert ia["gpu_augmentations"], "gpu_augmentations missing"
         assert ia["normalizations"], "normalizations missing"
@@ -464,9 +448,9 @@ def test_joint_train_leaf_composes() -> None:
 
     # Child ordering + paths.
     assert children[0]["init_args"]["data_path"].endswith("ipsc/dataset_v4/train/SEC61B.zarr")
-    assert children[1]["init_args"]["data_path"].endswith("2024_11_07_A549_SEC61_DENV/train/SEC61B.zarr")
+    assert children[1]["init_args"]["data_path"].endswith("a549/mantis_v1/train/SEC61B_all.zarr")
 
-    # Launcher: 4 GPUs matches topology, SLURM invariant holds.
+    # Launcher: single GPU matches topology, SLURM invariant holds.
     assert cfg["launcher"]["mode"] == "fit"
     sbatch = cfg["launcher"]["sbatch"]
     nodes = sbatch.get("nodes", 1)
@@ -520,10 +504,10 @@ def test_joint_train_smoke_leaf_composes() -> None:
         assert ia["batch_size"] == 1
         assert ia["gpu_augmentations"], "gpu_augmentations missing"
 
-    # iPSC child: test48 zarr (smoke-sized). a549 child: 2024_11_07 SEC61B
-    # (already 4 FOVs, no smoke variant needed).
+    # iPSC child: test48 zarr (smoke-sized). a549 child: pooled SEC61B
+    # all-conditions train store (mantis_v1/train/SEC61B_all.zarr).
     assert children[0]["init_args"]["data_path"].endswith("SEC61B_test48.zarr")
-    assert children[1]["init_args"]["data_path"].endswith("2024_11_07_A549_SEC61_DENV/train/SEC61B.zarr")
+    assert children[1]["init_args"]["data_path"].endswith("a549/mantis_v1/train/SEC61B_all.zarr")
 
     # Launcher: single GPU on H200, smoke-sized wall, SLURM invariant holds.
     assert cfg["launcher"]["mode"] == "fit"
@@ -536,12 +520,14 @@ def test_joint_train_smoke_leaf_composes() -> None:
     assert sbatch["time"] == "00:30:00"
 
 
-# Lock-in for the A100-exclude bake-in in hardware_4gpu.yml. After repeat
-# NCCL coordination hangs on this cluster's A100 partition (FCMAE jobs
-# 31474030 + 31474038, joint smoke 31480607), every 4-GPU train leaf
-# inherits this alternation by default. Future leaves needing A100 must
-# explicitly opt out via `--override launcher.sbatch.constraint=null`.
-_HARDWARE_4GPU_CONSTRAINT = "h100|h200|a40|a6000|l40s"
+# Lock-in for the H100/H200 restriction baked into hardware_4gpu.yml.
+# 4-GPU FCMAE/UNeXt2 leaves train at large spatial patches where a single
+# DDP rank needs 30-50 GB, so A40/A6000/L40S (48 GB) leave no headroom;
+# A100 is excluded separately due to repeat NCCL coordination hangs on this
+# cluster's A100 partition. Every 4-GPU train leaf inherits this constraint
+# by default. Leaves needing other cards must explicitly opt out via
+# `--override launcher.sbatch.constraint=null`.
+_HARDWARE_4GPU_CONSTRAINT = "h100|h200"
 
 
 def _all_train_leaves() -> list[Path]:
diff --git a/applications/dynaclr/pyproject.toml b/applications/dynaclr/pyproject.toml
index 185250a95..4cce8f180 100644
--- a/applications/dynaclr/pyproject.toml
+++ b/applications/dynaclr/pyproject.toml
@@ -31,7 +31,7 @@ classifiers = [
 dynamic = [ "version" ]
 dependencies = [
   "click",
-  "iohub>=0.3.5",
+  "iohub>=0.3.6",
   "pytorch-metric-learning",
   "pyyaml",
   # seaborn and tabulate are required by core eval pipeline code that runs
@@ -96,6 +96,9 @@ source = "uv-dynamic-versioning"
 [tool.hatch.build.targets.wheel]
 packages = [ "src/dynaclr" ]
 
+[tool.pytest.ini_options]
+testpaths = [ "tests" ]
+
 [tool.uv-dynamic-versioning]
 vcs = "git"
 style = "pep440"
diff --git a/applications/dynaclr/scripts/dataloader_inspection/test_2d_mip_augmentation.py b/applications/dynaclr/scripts/dataloader_inspection/demo_2d_mip_augmentation.py
similarity index 99%
rename from applications/dynaclr/scripts/dataloader_inspection/test_2d_mip_augmentation.py
rename to applications/dynaclr/scripts/dataloader_inspection/demo_2d_mip_augmentation.py
index 5f9687ea5..df842c2cb 100644
--- a/applications/dynaclr/scripts/dataloader_inspection/test_2d_mip_augmentation.py
+++ b/applications/dynaclr/scripts/dataloader_inspection/demo_2d_mip_augmentation.py
@@ -16,7 +16,7 @@
 
 Usage::
 
-    uv run python applications/dynaclr/scripts/dataloader_inspection/test_2d_mip_augmentation.py
+    uv run python applications/dynaclr/scripts/dataloader_inspection/demo_2d_mip_augmentation.py
 """
 
 # ruff: noqa: E402, D103
diff --git a/applications/dynaclr/scripts/plotting/plot_dim_reduct.py b/applications/dynaclr/scripts/plotting/plot_dim_reduct.py
index 4991c1e0b..f7b3a314d 100644
--- a/applications/dynaclr/scripts/plotting/plot_dim_reduct.py
+++ b/applications/dynaclr/scripts/plotting/plot_dim_reduct.py
@@ -1,6 +1,6 @@
-# %%
-"""Plot dimensionality reduction of cell embeddings."""
+"""Dimensionality-reduction plotting helpers for DynaCLR embeddings."""
 
+# %%
 from pathlib import Path
 
 import anndata as ad
diff --git a/applications/dynaclr/src/dynaclr/data/tau_sampling.py b/applications/dynaclr/src/dynaclr/data/tau_sampling.py
index 4715f6685..a29dd90f2 100644
--- a/applications/dynaclr/src/dynaclr/data/tau_sampling.py
+++ b/applications/dynaclr/src/dynaclr/data/tau_sampling.py
@@ -1,4 +1,4 @@
-"""Temporal tau sampling for triplet selection."""
+"""Temporal-offset (tau) sampling for triplet construction."""
 
 import numpy as np
 
diff --git a/applications/dynaclr/tests/test_flat_parquet_e2e.py b/applications/dynaclr/tests/test_flat_parquet_e2e.py
index 944c92a13..ca59fd34f 100644
--- a/applications/dynaclr/tests/test_flat_parquet_e2e.py
+++ b/applications/dynaclr/tests/test_flat_parquet_e2e.py
@@ -89,7 +89,7 @@ def flat_parquet_setup(tmp_path, _create_experiment, _write_collection_yaml):
     parquet_path = tmp_path / "flat.parquet"
     df = build_timelapse_cell_index(yaml_path, parquet_path, num_workers=1)
 
-    registry = ExperimentRegistry.from_cell_index(
+    registry, _ = ExperimentRegistry.from_cell_index(
         parquet_path,
         reference_pixel_size_xy_um=0.108,
         reference_pixel_size_z_um=0.3,
diff --git a/applications/dynaclr/tests/test_multi_experiment_integration.py b/applications/dynaclr/tests/test_multi_experiment_integration.py
index 26d22cac1..6ae79b907 100644
--- a/applications/dynaclr/tests/test_multi_experiment_integration.py
+++ b/applications/dynaclr/tests/test_multi_experiment_integration.py
@@ -18,10 +18,16 @@
 from viscy_models.contrastive.loss import NTXentHCL
 
 # ---------------------------------------------------------------------------
-# Constants — encoder input matches final_yx_patch_size=(24,24) with 2 channels
+# Constants — encoder input matches final_yx_patch_size=(24,24).
+#
+# These experiments have *different* channel sets, so the supported mode is
+# bag-of-channels (``channels_per_sample=1``): each sample carries a single
+# channel keyed ``channel_0``. All-channels mode is not defined for
+# heterogeneous experiments — its channel union (``source_channel_labels``)
+# cannot align positionally with any one experiment's patch.
 # ---------------------------------------------------------------------------
 
-_C = 2
+_C = 1
 _Z = 1
 _Y = 24
 _X = 24
@@ -67,10 +73,11 @@ def test_multi_experiment_fast_dev_run(tmp_path, _create_experiment, _write_coll
         tau_range=(0.5, 2.0),
         batch_size=4,
         num_workers=1,
+        channels_per_sample=1,
         batch_group_by="experiment",
         stratify_by=None,
         temporal_enrichment=False,
-        channel_dropout_channels=[1],
+        channel_dropout_channels=[0],
         channel_dropout_prob=0.5,
     )
 
@@ -125,7 +132,7 @@ def test_multi_experiment_fast_dev_run_with_parquet(
     build_timelapse_cell_index(yaml_path, parquet_path)
 
     datamodule = MultiExperimentDataModule(
-        collection_path=str(yaml_path),
+        cell_index_path=str(parquet_path),
         z_window=1,
         yx_patch_size=(32, 32),
         final_yx_patch_size=(24, 24),
@@ -133,12 +140,12 @@ def test_multi_experiment_fast_dev_run_with_parquet(
         tau_range=(0.5, 2.0),
         batch_size=4,
         num_workers=1,
+        channels_per_sample=1,
         batch_group_by="experiment",
         stratify_by=None,
         temporal_enrichment=False,
-        channel_dropout_channels=[1],
+        channel_dropout_channels=[0],
         channel_dropout_prob=0.5,
-        cell_index_path=str(parquet_path),
     )
 
     encoder = _SimpleEncoder(in_dim=_FLAT_DIM)
@@ -200,13 +207,14 @@ def test_multi_experiment_fast_dev_run_with_all_sampling_axes(
         tau_range=(0.5, 2.0),
         batch_size=4,
         num_workers=1,
+        channels_per_sample=1,
         # All sampling axes enabled
         batch_group_by="experiment",
         stratify_by="perturbation",
         temporal_enrichment=True,
         temporal_window_hours=2.0,
         temporal_global_fraction=0.3,
-        channel_dropout_channels=[1],
+        channel_dropout_channels=[0],
         channel_dropout_prob=0.5,
     )
 
@@ -237,16 +245,16 @@ def test_multi_experiment_fast_dev_run_with_all_sampling_axes(
 
 
 def test_multi_experiment_config_class_paths_resolve(_extract_class_paths, _resolve_class_path):
-    """All class_paths in multi_experiment_fit.yml resolve to importable classes."""
+    """All class_paths in demo_bag_of_channels_v3_fit.yml resolve to importable classes."""
     configs_dir = Path(__file__).parents[1] / "configs" / "training"
-    config_path = configs_dir / "multi_experiment_fit.yml"
+    config_path = configs_dir / "demo" / "demo_bag_of_channels_v3_fit.yml"
     assert config_path.exists(), f"Config file not found: {config_path}"
 
     with open(config_path) as f:
         config = yaml.safe_load(f)
 
     class_paths = _extract_class_paths(config)
-    assert len(class_paths) > 0, "No class_path entries found in multi_experiment_fit.yml"
+    assert len(class_paths) > 0, "No class_path entries found in demo_bag_of_channels_v3_fit.yml"
 
     for cp in class_paths:
         cls = _resolve_class_path(cp)
diff --git a/applications/dynaclr/tests/test_training_integration.py b/applications/dynaclr/tests/test_training_integration.py
index d07fe6bcc..fea654dd8 100644
--- a/applications/dynaclr/tests/test_training_integration.py
+++ b/applications/dynaclr/tests/test_training_integration.py
@@ -6,10 +6,10 @@
 import yaml
 from lightning.pytorch import Trainer, seed_everything
 from lightning.pytorch.loggers import TensorBoardLogger
-from pytorch_metric_learning.losses import NTXentLoss
 from torch import nn
 
 from dynaclr.engine import ContrastiveModule
+from viscy_models.contrastive.loss import NTXentLoss
 
 
 def test_contrastive_fast_dev_run(tmp_path, _SimpleEncoder, _SyntheticTripletDataModule, synth_dims):
@@ -54,7 +54,7 @@ def test_contrastive_ntxent_fast_dev_run(tmp_path, _SimpleEncoder, _SyntheticTri
 
 @pytest.mark.parametrize(
     "config_name,config_subdir",
-    [("fit.yml", "training"), ("predict.yml", "prediction")],
+    [("demo/demo_2d_fit.yml", "training"), ("predict.yml", "prediction")],
 )
 def test_config_class_paths_resolve(config_name, config_subdir, _extract_class_paths, _resolve_class_path):
     configs_dir = Path(__file__).parents[1] / "configs" / config_subdir
diff --git a/packages/viscy-data/pyproject.toml b/packages/viscy-data/pyproject.toml
index 2086eaaf4..959f9543e 100644
--- a/packages/viscy-data/pyproject.toml
+++ b/packages/viscy-data/pyproject.toml
@@ -31,7 +31,7 @@ classifiers = [
 dynamic = [ "version" ]
 dependencies = [
   "imageio",
-  "iohub>=0.3.5",
+  "iohub>=0.3.6",
   "lightning>=2.3",
   "monai>=1.5.2",
   "numpy>=2.4.1",
@@ -53,7 +53,7 @@ urls.Repository = "https://github.com/mehta-lab/VisCy"
 
 [dependency-groups]
 dev = [ { include-group = "test" } ]
-test = [ "pytest>=9.0.2", "pytest-cov>=7", "tensorstore" ]
+test = [ "pytest>=9.0.2", "pytest-cov>=7", "tensorstore", "viscy-transforms" ]
 
 [tool.hatch.version]
 source = "uv-dynamic-versioning"
diff --git a/packages/viscy-data/src/viscy_data/__init__.py b/packages/viscy-data/src/viscy_data/__init__.py
index 0c5397df9..edbec8ed6 100644
--- a/packages/viscy-data/src/viscy_data/__init__.py
+++ b/packages/viscy-data/src/viscy_data/__init__.py
@@ -18,6 +18,12 @@
     Use ``importlib.metadata.version('viscy-data')`` to get version.
 """
 
+# Pin iohub's default implementation to ``zarr-python`` before any store is
+# opened (avoids the broken ``zarrs`` Rust pipeline, which iohub still defaults
+# to). Imported first so the override is applied before any other import pulls
+# in iohub. See _zarr_codec.py.
+import viscy_data._zarr_codec  # noqa: F401  (import for side effect)
+
 # Type definitions (from _typing.py)
 from viscy_data._typing import (
     CELL_INDEX_CORE_COLUMNS,
diff --git a/packages/viscy-data/src/viscy_data/_zarr_codec.py b/packages/viscy-data/src/viscy_data/_zarr_codec.py
new file mode 100644
index 000000000..a6dd7dae5
--- /dev/null
+++ b/packages/viscy-data/src/viscy_data/_zarr_codec.py
@@ -0,0 +1,35 @@
+"""Force iohub to use the zarr-python implementation instead of ``zarrs``.
+
+iohub (>=0.3.6) selects its zarr backend by *implementation name* via
+:mod:`iohub.core.registry`, whose module-level default is still
+``"zarrs-python"`` — the Rust ``zarrs`` pipeline. ``zarrs`` 0.2.3 (the only
+version iohub's ``zarrs>=0.2.3`` floor accepts) builds a rank-6 read offset
+for rank-5 sharded arrays, raising ``RuntimeError: incompatible offset ...
+for region with start ...`` on every sharded zarr-v3 read, and deadlocks the
+spawned ranks of a DDP run.
+
+iohub 0.3.6 exposes :func:`iohub.core.registry.set_default_implementation`,
+the supported switch for choosing the pure-Python ``zarr-python``
+implementation (whose codec pipeline is zarr-python's native
+``BatchedCodecPipeline``). We call it once at import time so the choice is in
+place before any iohub store is opened. ``set_default_implementation`` is not
+re-exported at the iohub top level, hence the fully qualified import.
+"""
+
+from iohub.core.registry import set_default_implementation
+
+_ZARR_PYTHON = "zarr-python"
+
+
+def use_zarr_python_codec() -> None:
+    """Pin iohub's default implementation to ``zarr-python``.
+
+    Idempotent. Sets iohub's default implementation name so every store open
+    uses zarr-python's native ``BatchedCodecPipeline`` rather than the broken
+    ``zarrs`` Rust pipeline.
+    """
+    set_default_implementation(_ZARR_PYTHON)
+
+
+# Apply on import so the override is in place before any iohub store is opened.
+use_zarr_python_codec()
diff --git a/packages/viscy-data/src/viscy_data/channel_dropout.py b/packages/viscy-data/src/viscy_data/channel_dropout.py
index d6b73f7e9..4ea41707a 100644
--- a/packages/viscy-data/src/viscy_data/channel_dropout.py
+++ b/packages/viscy-data/src/viscy_data/channel_dropout.py
@@ -1,4 +1,4 @@
-"""Channel dropout augmentation for GPU batches."""
+"""GPU-side per-sample channel dropout for the augmentation pipeline."""
 
 import torch
 from torch import Tensor, nn
@@ -24,7 +24,7 @@ def __init__(self, channels: list[int], p: float = 0.5) -> None:
         self.p = p
 
     def forward(self, x: Tensor) -> Tensor:
-        """Drop specified channels per sample during training."""
+        """Drop selected channels per-sample with probability ``self.p``."""
         if not self.training or self.p == 0.0:
             return x
         out = x.clone()
diff --git a/packages/viscy-data/tests/test_hcs.py b/packages/viscy-data/tests/test_hcs.py
index 1df26955a..1242e1d81 100644
--- a/packages/viscy-data/tests/test_hcs.py
+++ b/packages/viscy-data/tests/test_hcs.py
@@ -7,7 +7,7 @@
 from iohub import open_ome_zarr
 from lightning.pytorch.trainer.states import TrainerFn
 from monai.transforms import Compose, RandAdjustContrastd, RandAffined, RandFlipd, RandSpatialCropSamplesd
-from pytest import TempPathFactory, fixture, importorskip, mark, raises
+from pytest import TempPathFactory, fixture, importorskip, mark, raises, skip
 
 from viscy_data import HCSDataModule
 from viscy_data.sliding_window import SlidingWindowDataset
@@ -952,12 +952,41 @@ def test_mmap_preload_recovers_from_partial_cache(hcs_with_fg_mask, tmp_path):
         break
 
 
+def _mmap_sharing_child(data_path, cache_dir, result_queue):
+    """Open the shared mmap buffer in a child process (module-level for spawn).
+
+    Defined at module scope so it is picklable under the ``spawn`` start
+    method (Windows/macOS default); a nested closure would fail with
+    ``AttributeError: Can't get local object``.
+    """
+    try:
+        from iohub import open_ome_zarr
+        from tensordict.memmap import MemoryMappedTensor
+
+        with open_ome_zarr(data_path, mode="r") as ds:
+            positions = [pos for _, pos in ds.positions()]
+        arr_shape = positions[0]["0"].shape
+        T, C = arr_shape[0], 2
+        shape = (len(positions) * T, C, *arr_shape[2:])
+        buf = MemoryMappedTensor.from_filename(cache_dir / "data.mmap", dtype=torch.float32, shape=shape)
+        result_queue.put(("ok", tuple(buf.shape)))
+    except Exception as e:
+        result_queue.put(("err", str(e)))
+
+
 def test_mmap_preload_multi_process_sharing(hcs_with_fg_mask, tmp_path):
     """Both parent and child processes can open the mmap buffer after prepare_data."""
-    import multiprocessing
+    import multiprocessing as mp
 
     importorskip("tensordict")
-    from tensordict.memmap import MemoryMappedTensor
+
+    # ``fork`` context because pytest imports tests under
+    # ``--import-mode=importlib``, whose path can't be re-resolved in a spawn
+    # child (``ModuleNotFoundError: 'packages'``). fork is unavailable on
+    # Windows, so skip there — same pattern as test_combined_ddp.py.
+    if "fork" not in mp.get_all_start_methods():
+        skip("fork start_method not available (Windows)")
+    ctx = mp.get_context("fork")
 
     dm = HCSDataModule(
         data_path=hcs_with_fg_mask,
@@ -972,23 +1001,9 @@ def test_mmap_preload_multi_process_sharing(hcs_with_fg_mask, tmp_path):
     dm.prepare_data()
     cache_dir = dm._mmap_cache_dir
 
-    result_queue = multiprocessing.Queue()
-
-    def _child(cache_dir, result_queue):
-        try:
-            from iohub import open_ome_zarr
-
-            with open_ome_zarr(hcs_with_fg_mask, mode="r") as ds:
-                positions = [pos for _, pos in ds.positions()]
-            arr_shape = positions[0]["0"].shape
-            T, C = arr_shape[0], 2
-            shape = (len(positions) * T, C, *arr_shape[2:])
-            buf = MemoryMappedTensor.from_filename(cache_dir / "data.mmap", dtype=torch.float32, shape=shape)
-            result_queue.put(("ok", tuple(buf.shape)))
-        except Exception as e:
-            result_queue.put(("err", str(e)))
+    result_queue = ctx.Queue()
 
-    proc = multiprocessing.Process(target=_child, args=(cache_dir, result_queue))
+    proc = ctx.Process(target=_mmap_sharing_child, args=(hcs_with_fg_mask, cache_dir, result_queue))
     proc.start()
     proc.join(timeout=30)
     status, value = result_queue.get_nowait()
diff --git a/packages/viscy-utils/pyproject.toml b/packages/viscy-utils/pyproject.toml
index 077e92471..5f6ba353a 100644
--- a/packages/viscy-utils/pyproject.toml
+++ b/packages/viscy-utils/pyproject.toml
@@ -30,7 +30,7 @@ classifiers = [
 ]
 dynamic = [ "version" ]
 dependencies = [
-  "iohub>=0.3.5",
+  "iohub>=0.3.6",
   "jsonargparse[signatures]>=4.26",
   "lightning>=2.3",
   "matplotlib>=3.10",
diff --git a/packages/viscy-utils/tests/test_mp_utils.py b/packages/viscy-utils/tests/test_mp_utils.py
index 5a6545881..2e3602a6f 100644
--- a/packages/viscy-utils/tests/test_mp_utils.py
+++ b/packages/viscy-utils/tests/test_mp_utils.py
@@ -4,7 +4,8 @@
 
 
 def test_get_val_stats():
-    values = np.random.default_rng(0).standard_normal(1000)
+    rng = np.random.default_rng(0)
+    values = rng.standard_normal(1000)
     stats = get_val_stats(values)
     assert "mean" in stats
     assert "std" in stats
diff --git a/uv.lock b/uv.lock
index 6b6772bef..f3a8142da 100644
--- a/uv.lock
+++ b/uv.lock
@@ -101,7 +101,7 @@ wheels = [
 
 [[package]]
 name = "aiohttp"
-version = "3.13.5"
+version = "3.14.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohappyeyeballs" },
@@ -110,78 +110,93 @@ dependencies = [
     { name = "frozenlist" },
     { name = "multidict" },
     { name = "propcache" },
+    { name = "typing-extensions", marker = "python_full_version < '3.13'" },
     { name = "yarl" },
 ]
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 [[package]]
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@@ -6412,10 +6470,11 @@ dependencies = [
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 [[package]]
@@ -6668,17 +6727,19 @@ dev = [
     { name = "pytest" },
     { name = "pytest-cov" },
     { name = "tensorstore" },
+    { name = "viscy-transforms" },
 ]
 test = [
     { name = "pytest" },
     { name = "pytest-cov" },
     { name = "tensorstore" },
+    { name = "viscy-transforms" },
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 [package.metadata]
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-    { name = "iohub", specifier = ">=0.3.5" },
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@@ -6706,11 +6767,13 @@ dev = [
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     { name = "pytest-cov", specifier = ">=7" },
     { name = "tensorstore" },
+    { name = "viscy-transforms", editable = "packages/viscy-transforms" },
 ]
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     { name = "pytest-cov", specifier = ">=7" },
     { name = "tensorstore" },
+    { name = "viscy-transforms", editable = "packages/viscy-transforms" },
 ]
 
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@@ -6896,7 +6959,7 @@ requires-dist = [
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     { name = "copairs", marker = "extra == 'all'" },
     { name = "copairs", marker = "extra == 'eval'" },
-    { name = "iohub", specifier = ">=0.3.5" },
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     { name = "lightning", specifier = ">=2.3" },
     { name = "matplotlib", specifier = ">=3.10" },
@@ -6997,8 +7060,8 @@ wheels = [
 
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-source = { git = "https://github.com/mehta-lab/waveorder.git?branch=main#f377a43276535e28ec3f7cdb31a91897a96f5adb" }
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+source = { git = "https://github.com/mehta-lab/waveorder.git?branch=main#22a0f72e6cdf93001a94431f7260d39ef026a819" }
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