diff --git a/.gitignore b/.gitignore
index 811c0a9..f5d31e6 100644
--- a/.gitignore
+++ b/.gitignore
@@ -10,6 +10,9 @@ __pycache__/
 *.py[codz]
 *$py.class
 data/*
+# Optional third-party example assets
+examples/matey/MATEY/
+examples/matey/data/
 # C extensions
 *.so
 
diff --git a/examples/matey/Demo_SOLPS_vit.yaml b/examples/matey/Demo_SOLPS_vit.yaml
new file mode 100644
index 0000000..270c668
--- /dev/null
+++ b/examples/matey/Demo_SOLPS_vit.yaml
@@ -0,0 +1,75 @@
+basic_config: &basic_config
+  # Run settings
+  log_to_wandb: !!bool False #True # Use wandb integration
+  log_to_screen: !!bool True # Log progress to screen.
+  save_checkpoint: !!bool True # Save checkpoints
+  checkpoint_save_interval: 10 # Save every # epochs - also saves "best" according to val loss
+  debug_grad: !!bool True # Compute gradient/step_sizes/ect for debugging
+  true_time: !!bool False # Debugging setting - sets num workers to zero and activates syncs
+  num_data_workers: 2 #6 # Generally pulling 8 cpu per process, so using 6 for DL - not sure if best ratio
+  enable_amp: !!bool False # Use automatic mixed precision - blows up with low variance fields right now
+  compile: !!bool False # Compile model - Does not currently work
+  gradient_checkpointing: !!bool False # Whether to use gradient checkpointing - Slow, but lower memory
+  exp_dir: './Dev_SOLPS' # Output path
+  log_interval: 1 # How often to log - Don't think this is actually implemented
+  pretrained: !!bool False # Whether to load a pretrained model
+  # Training settings
+  drop_path: 0.1
+  batch_size: 64
+  max_epochs: 10
+  scheduler_epochs: -1
+  epoch_size: 20 
+  rescale_gradients: !!bool False # Activate hook that scales block gradients to norm 1
+  optimizer: 'AdamW' # DAdaptAdam 'AdamW' 'SGD'
+  scheduler: 'none' # Only cosine implemented
+  warmup_steps: 0 # Warmup when not using DAdapt
+  learning_rate: 1e-3 # 
+  weight_decay: 1e-3
+  n_states: 29 # Must be >= max field label + 1 in the dataset
+  state_names: ['Pressure', 'Vx', 'Vy', 'Density',  'Vx', 'Vy', 'Density', 'Pressure'] # These are not used now!
+  dt: 1 # Striding of data - Not currently implemented > 1
+  leadtime_max: 10 #prediction lead time range [1, leadtime_max]
+  autoregressive: !!bool True # autoregressive training or one-step prediction
+  supportdata: # Whether to use support data (e.g. input control actuator) as input
+  - input_control_act: !!bool True
+  n_steps: 3 #16 # Length of history to include in input
+  enforce_max_steps: !!bool False # If false and n_steps > dataset steps, use dataset steps. Otherwise, raise Exception.
+  accum_grad: 1  
+  # Model settings
+  model_type:  'vit_all2all' # no need for time_type and space_type inputs
+  #model_type:  'svit' #currently only support time_type=="all2all_time" and space_type=="all2all"
+  #time_type: 'all2all_time' # 
+  #space_type: 'all2all' #
+  #model_type:  'avit' #currently only support space_type=="axial_attention" and time_type=="attention"
+  #time_type: 'attention' # 
+  #space_type: 'axial_attention' #
+  tie_fields: !!bool False # Whether to use 1 embedding per field per data
+  embed_dim: 192 # Dimension of internal representation - 192/384/768/1024 for Ti/S/B/L
+  num_heads: 3 # Number of heads for attention - 3/6/12/16 for Ti/S/B/L
+  processor_blocks: 12 # Number of transformer blocks in the backbone - 12/12/12/24 for Ti/S/B/L
+  ##patch_size: [[1, 2, 2]] #[[1, 40, 40]] #, [32, 32], [64, 64]] #
+  tokenizer_heads:
+  - head_name: "tk-2D"
+    patch_size: [[1, 2, 2]] 
+  sts_model: !!bool False
+  sts_train: !!bool False  #when True, we use loss function with two parts: l_coarse/base + l_total, so that the coarse ViT approximates true solutions directly
+  #gammaref: 0.2 #pick all tokens that with variances larger than gammaref*max_variance to refine
+  #refine_ratio: 0.2 #ratio of coarse tokens picked to be refined
+  bias_type: 'PositionAreaBias'  # Options rel, continuous, none, PositionAreaBias
+  bias_MLP: !!bool True
+  # Data settings
+  #train_val_test: [.6, .2, .2]
+  augmentation: !!bool False # Augmentation not implemented
+  use_all_fields: !!bool True # Prepopulate the field metadata dictionary from dictionary in datasets
+  tie_batches: !!bool False # Force everything in batch to come from one dset
+  extended_names: !!bool False # Whether to use extended names - not currently implemented
+  embedding_offset: 0  # Use when adding extra finetuning fields
+  train_data_paths: [
+              ['examples/matey/data/fusionMT-data/solps/train', 'SOLPS2D', '','tk-2D'],
+              ]
+  valid_data_paths: [
+              ['examples/matey/data/fusionMT-data/solps/valid', 'SOLPS2D', '','tk-2D'],
+              ]
+  append_datasets: [] # List of datasets to append to the input/output projections for finetuning
+
+
diff --git a/examples/matey/README.md b/examples/matey/README.md
new file mode 100644
index 0000000..a4d126e
--- /dev/null
+++ b/examples/matey/README.md
@@ -0,0 +1,91 @@
+# MATEY Example Harness
+
+BaseSim harness for the [MATEY](https://github.com/FusionFM/MATEY) multiscale transformer codebase.
+
+## Setup
+
+1. Install BaseSim (from repo root):
+   ```bash
+   poetry install
+   ```
+
+2. Install the optional MATEY example dependency (pinned commit):
+   ```bash
+   poetry install --extras matey
+   ```
+   This uses GitHub SSH auth for the private MATEY repo, so your SSH key must
+   have access to `FusionFM/MATEY`.
+
+   This extra is pinned to:
+   `4e615bb5c86024632e386153bfbed028b38a8262`
+
+   Equivalent pip command:
+   ```bash
+   pip install "matey @ git+ssh://git@github.com/FusionFM/MATEY.git@4e615bb5c86024632e386153bfbed028b38a8262"
+   ```
+
+3. (Optional) Install heavy/system-dependent packages as needed for your environment:
+   ```bash
+   MAX_JOBS=4 NINJA_STATUS="[%f/%t] " pip install -vv --progress-bar on --no-build-isolation flash-attn
+   pip install dadaptation==3.1                             # for DAdaptAdam optimizer
+   pip install mpi4py                                       # requires MPI C library
+   pip install netCDF4                                      # requires HDF5/netCDF C libs
+   pip install git+https://github.com/sandialabs/exodusii.git # not on PyPI
+   ```
+
+   Alternative: install flash attention without screen output 
+   
+   ``` 
+   MAX_JOBS=4 pip install flash-attn --no-build-isolation  # requires CUDA toolkit + nvcc
+   ```
+## Running
+
+```bash
+poetry run python -m src.main --config examples/matey/matey.toml
+```
+
+Outer-loop drift demo (L2 placeholder model + input-noise stream updates):
+
+```bash
+poetry run python -m src.main --config examples/matey/matey_outer_loop.toml
+# or
+./examples/matey/run_outer_loop.sh
+```
+
+## Configuration
+
+Edit [matey.toml](matey.toml) to adjust training parameters, drift detection, and data paths.
+
+The `[data].path` should point to your local SOLPS dataset root
+(must contain `train/` and `valid/` directories).
+This data is expected to be user-provided and is not tracked in git.
+
+For the SOLPS example, the harness builds a deterministic file-level split of
+`[0.7, 0.15, 0.15]` and materializes staged views under:
+
+```text
+output/matey_split_cache/<fingerprint>/{train,val,test}
+```
+
+The cache is reused when source files, split ratios, and seed are unchanged.
+
+The example TOML is tuned for short smoke runs to make drift-triggered continual
+learning dispatch easier to observe (`detection_interval=5`, `aggregation="last"`,
+`adwin_delta=0.05`, `max_stream_updates=10`).
+
+For the outer-loop harness, use [matey_outer_loop.toml](matey_outer_loop.toml):
+- `data.name = "matey_outer_loop"` selects `model_outer_loop.py`.
+- `data.path` points at `examples/matey/dump/SOLPS2DwION`.
+- `continual_learning.update_mode = "none"` disables parameter updates.
+- `drift_detection.metric_index = 0` monitors the `input_l2` metric.
+
+## Files
+
+| File | Description |
+|---|---|
+| `model.py` | `MATEYHarness` -- adapts MATEY models/data to BaseSim's `BaseModelHarness` interface |
+| `matey.toml` | Experiment config |
+| `model_outer_loop.py` | Outer-loop drift harness with L2 placeholder model and noisy input stream |
+| `matey_outer_loop.toml` | Outer-loop experiment config |
+| `run_outer_loop.sh` | Convenience run script for `matey_outer_loop.toml` |
+| `pyproject.toml` | Optional example dependency manifest |
diff --git a/examples/matey/__init__.py b/examples/matey/__init__.py
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/examples/matey/__init__.py
@@ -0,0 +1 @@
+
diff --git a/examples/matey/matey.toml b/examples/matey/matey.toml
new file mode 100644
index 0000000..a4e507a
--- /dev/null
+++ b/examples/matey/matey.toml
@@ -0,0 +1,46 @@
+seed = 1337
+device = "auto"
+multi_gpu = false
+verbosity = "INFO"
+
+[model]
+name = "matey_vit"
+pretrained_path = ""
+
+[data]
+name = "matey"
+# User-provided SOLPS dataset root containing train/ and valid/ folders.
+# This path is local-only and should not be tracked in git.
+path = "/path/to/fusionMT-data/solps"
+
+[train]
+batch_size = 16
+num_workers = 2
+init_lr = 0.001
+max_iter = 200
+grad_accumulation_steps = 1
+
+[continual_learning]
+update_mode = "base"
+
+[drift_detection]
+detector_name = "ADWINDetector"
+detection_interval = 5
+aggregation = "last"
+metric_index = 0  # MATEY metrics: 0=nrmse, 1=rmse, 2=loss
+reset_after_learning = false
+max_stream_updates = 10
+
+# ADWIN hyperparameters
+adwin_delta = 0.05
+adwin_minor_threshold = 0.3
+adwin_moderate_threshold = 0.6
+
+[logging]
+backend = "wandb" 
+experiment_name = "matey-continual-learning"  # Optional: project/experiment name
+
+[visualization]
+baseline = 0.0
+input = "output/matey.csv"
+output = "output/matey_dashboard.png"
diff --git a/examples/matey/matey_batches.py b/examples/matey/matey_batches.py
new file mode 100644
index 0000000..99b1202
--- /dev/null
+++ b/examples/matey/matey_batches.py
@@ -0,0 +1,13 @@
+from examples.matey.src.matey_batches import (
+    MateyInputBatch,
+    MateyLoaderAdapter,
+    MateyModelAdapter,
+    MateyTargetBatch,
+)
+
+__all__ = [
+    "MateyInputBatch",
+    "MateyTargetBatch",
+    "MateyLoaderAdapter",
+    "MateyModelAdapter",
+]
diff --git a/examples/matey/matey_outer_loop.toml b/examples/matey/matey_outer_loop.toml
new file mode 100644
index 0000000..594b369
--- /dev/null
+++ b/examples/matey/matey_outer_loop.toml
@@ -0,0 +1,44 @@
+seed = 1337
+device = "auto"
+multi_gpu = false
+verbosity = "INFO"
+
+[model]
+name = "matey_outer_loop_l2"
+pretrained_path = ""
+
+[data]
+name = "matey_outer_loop"
+path = "examples/matey/dump/SOLPS2DwION"
+
+[train]
+batch_size = 1
+num_workers = 0
+init_lr = 0.0
+max_iter = 50
+grad_accumulation_steps = 1
+
+[continual_learning]
+update_mode = "none"
+
+[drift_detection]
+detector_name = "ADWINDetector"
+detection_interval = 5
+aggregation = "last"
+metric_index = 0  # outer-loop metrics: 0=input_l2, 1=loss
+reset_after_learning = false
+max_stream_updates = 20
+
+# ADWIN hyperparameters
+adwin_delta = 0.05
+adwin_minor_threshold = 0.3
+adwin_moderate_threshold = 0.6
+
+[logging]
+backend = "wandb"
+experiment_name = "matey-outer-loop-drift"
+
+[visualization]
+baseline = 0.0
+input = "output/matey_outer_loop.csv"
+output = "output/matey_outer_loop_dashboard.png"
diff --git a/examples/matey/model.py b/examples/matey/model.py
new file mode 100644
index 0000000..29bbad1
--- /dev/null
+++ b/examples/matey/model.py
@@ -0,0 +1,605 @@
+from __future__ import annotations
+# mypy: ignore-errors
+
+import copy
+import gc
+import random
+import sys
+from contextlib import contextmanager
+from pathlib import Path
+from typing import Any, Callable, cast
+
+import torch
+from torch import Tensor, nn
+from torch.optim import Optimizer
+
+from config.configuration import Config
+from examples.matey.src.matey_batches import (
+    MateyInputBatch,
+    MateyLoaderAdapter as _MateyLoaderAdapter,
+    MateyModelAdapter as _MateyModelAdapter,
+    MateyTargetBatch,
+)
+from examples.matey.src.solps_split import SolpsStagedSplit, stage_solps_split
+from logger import get_logger
+from model.torch_model_harness import BaseModelHarness
+
+DEFAULT_MATEY_YAML = Path("examples/matey/Demo_SOLPS_vit.yaml")
+DEFAULT_MATEY_PROFILE = "basic_config"
+DEFAULT_MATEY_TRAIN_VAL_TEST = (0.7, 0.15, 0.15)
+DEFAULT_SOLPS_CACHE_ROOT = Path("output/matey_split_cache")
+MATEY_GIT_COMMIT = "4e615bb5c86024632e386153bfbed028b38a8262"
+MATEY_GIT_URL = f"git+ssh://git@github.com/FusionFM/MATEY.git@{MATEY_GIT_COMMIT}"
+SUPPORTED_UPDATE_MODES = {"base", "none"}
+
+
+class MATEYHarness(BaseModelHarness):
+    def __init__(self, cfg: Config):
+        self._assert_supported_update_mode(cfg)
+        self._solps_split: SolpsStagedSplit | None = None
+        self._solps_split_logged = False
+        self._split_seed = int(cfg.seed)
+
+        self._data_root = self._resolve_data_root(cfg)
+        self._validate_data_root(self._data_root)
+
+        modules = self._load_matey_modules()
+        params = self._build_matey_params(cfg, modules["YParams"])
+        self._configure_data_split(params)
+        matey_model = self._build_matey_model(cfg, params, modules)
+
+        self._adapter_model = _MateyModelAdapter(
+            matey_model=matey_model,
+            params=params,
+            forward_options_cls=modules["ForwardOptionsBase"],
+            rearrange_fn=modules["rearrange"],
+            autoregressive_rollout_fn=modules["autoregressive_rollout"],
+            determine_turt_levels_fn=modules["determine_turt_levels"],
+        )
+        super().__init__(cfg=cfg, model=self._adapter_model)
+
+        self._modules = modules
+        self._params = params
+
+        self.task_counter = 0
+        self._cur_train_loader: _MateyLoaderAdapter | None = None
+        self._cur_val_loader: _MateyLoaderAdapter | None = None
+
+        self.eval_metrics = {
+            "nrmse": self._nrmse_metric,
+            "rmse": self._rmse_metric,
+            "loss": self.get_criterion(),
+        }
+        self.higher_is_better = {"nrmse": False, "rmse": False, "loss": False}
+
+    def get_optmizer(self) -> Optimizer:
+        optimizer_name = str(getattr(self._params, "optimizer", "AdamW")).lower()
+        lr = float(getattr(self._params, "learning_rate", self.cfg.train.init_lr))
+        weight_decay = float(getattr(self._params, "weight_decay", 0.0))
+
+        add_weight_decay = self._modules["add_weight_decay"]
+        param_groups = add_weight_decay(self._adapter_model.matey_model, weight_decay)
+
+        if optimizer_name == "dadaptadam":
+            dadapt_cls = self._modules.get("DAdaptAdam")
+            if dadapt_cls is None:
+                raise RuntimeError(
+                    "MATEY optimizer is configured as DAdaptAdam but "
+                    "`dadaptation` is not installed in this environment."
+                )
+            return cast(
+                Optimizer,
+                dadapt_cls(
+                    param_groups, lr=1.0, growth_rate=1.05, log_every=100, decouple=True
+                ),
+            )
+
+        if optimizer_name == "sgd":
+            return torch.optim.SGD(self.model.parameters(), lr=lr, momentum=0.9)
+
+        return torch.optim.AdamW(param_groups, lr=lr, weight_decay=weight_decay)
+
+    def update_data_stream(self) -> None:
+        self._dispose_current_loaders()
+        self._set_stream_seed(self.cfg.seed + self.task_counter)
+        self._log_solps_split_details()
+
+        train_params = self._params_for_loader_split("train")
+        val_params = self._params_for_loader_split("val")
+        train_loader, train_dataset, _ = self._build_loader(train_params, split="train")
+        val_loader, val_dataset, _ = self._build_loader(val_params, split="val")
+
+        self._cur_train_loader = _MateyLoaderAdapter(train_loader, train_dataset)
+        self._cur_val_loader = _MateyLoaderAdapter(val_loader, val_dataset)
+
+        self.task_counter += 1
+
+    def get_cur_data_loaders(self) -> tuple[Any, Any]:
+        if self._cur_train_loader is None or self._cur_val_loader is None:
+            raise RuntimeError(
+                "Matey stream has not been initialized. Call update_data_stream() first."
+            )
+        return self._cur_train_loader, self._cur_val_loader
+
+    def get_hist_data_loaders(self) -> tuple[None, None]:
+        return None, None
+
+    def get_criterion(self):
+        def criterion(y_hat: Tensor, y: MateyTargetBatch) -> Tensor:
+            target = self._select_target_tensor(
+                y, self._adapter_model.last_rollout_steps
+            )
+            return self._compute_nrmse(y_hat, target)
+
+        return criterion
+
+    def _unpack(
+        self, batch: tuple[MateyInputBatch, MateyTargetBatch]
+    ) -> tuple[MateyInputBatch, MateyTargetBatch]:
+        return batch
+
+    @staticmethod
+    def _assert_supported_update_mode(cfg: Config) -> None:
+        if cfg.continual_learning.update_mode not in SUPPORTED_UPDATE_MODES:
+            raise NotImplementedError(
+                "Matey phase-1 harness supports only continual_learning.update_mode "
+                "in {'base', 'none'}. Modes {'jvp_reg', 'ewc_online', 'kfac_online'} "
+                "are not supported yet."
+            )
+
+    @staticmethod
+    def _resolve_data_root(cfg: Config) -> Path:
+        raw = cfg.data.path.strip()
+        if not raw:
+            raise ValueError(
+                "Matey data path is empty. Set [data].path to your local SOLPS "
+                "dataset root containing 'train/' and 'valid/' directories."
+            )
+        path = Path(raw)
+        if not path.is_absolute():
+            path = Path.cwd() / path
+        return path.resolve()
+
+    @staticmethod
+    def _validate_data_root(data_root: Path) -> None:
+        if not data_root.exists():
+            raise FileNotFoundError(
+                f"Matey data root path does not exist: {data_root}. "
+                "Set [data].path to your local SOLPS dataset root path."
+            )
+        if not data_root.is_dir():
+            raise NotADirectoryError(
+                f"Matey data root path is not a directory: {data_root}"
+            )
+
+        if not DEFAULT_MATEY_YAML.exists():
+            raise FileNotFoundError(
+                f"Required Matey YAML config was not found: {DEFAULT_MATEY_YAML}."
+            )
+
+    def _load_matey_modules(self) -> dict[str, Any]:
+        try:
+            # Import netCDF4 before h5py to avoid HDF5 library collision.
+            # Both ship their own libhdf5; whichever loads first wins.
+            import netCDF4 as _netCDF4  # noqa: F401
+
+            from einops import rearrange
+            from matey.data_utils.datasets import get_data_loader
+            from matey.models.avit import build_avit
+            from matey.models.svit import build_svit
+            from matey.models.turbt import build_turbt
+            from matey.models.vit import build_vit
+            from matey.utils.YParams import YParams
+            from matey.utils.distributed_utils import add_weight_decay
+            from matey.utils.distributed_utils import determine_turt_levels
+            from matey.utils.forward_options import ForwardOptionsBase
+            from matey.utils.training_utils import autoregressive_rollout
+        except ModuleNotFoundError as exc:
+            raise RuntimeError(
+                "Matey dependency import failed. Ensure MATEY requirements are "
+                "installed in the active environment (for example: "
+                f"`poetry install --extras matey` or `pip install \"matey @ {MATEY_GIT_URL}\"`) "
+                "and that `[data].path` points to your local SOLPS dataset root."
+            ) from exc
+
+        dadapt = None
+        try:
+            from dadaptation import DAdaptAdam as _DAdaptAdam
+
+            dadapt = _DAdaptAdam
+        except ModuleNotFoundError:
+            dadapt = None
+
+        return {
+            "YParams": YParams,
+            "get_data_loader": get_data_loader,
+            "build_avit": build_avit,
+            "build_svit": build_svit,
+            "build_vit": build_vit,
+            "build_turbt": build_turbt,
+            "add_weight_decay": add_weight_decay,
+            "determine_turt_levels": determine_turt_levels,
+            "ForwardOptionsBase": ForwardOptionsBase,
+            "autoregressive_rollout": autoregressive_rollout,
+            "rearrange": rearrange,
+            "DAdaptAdam": dadapt,
+        }
+
+    def _build_matey_params(self, cfg: Config, yparams_cls: type[Any]) -> Any:
+        yaml_path = DEFAULT_MATEY_YAML
+        params = yparams_cls(str(yaml_path), DEFAULT_MATEY_PROFILE)
+
+        params.use_ddp = False
+        params.use_fsdp = False
+        params.log_to_screen = False
+        params.log_to_wandb = False
+        params.enable_sync = False
+        params.profiling = False
+
+        params.batch_size = max(1, int(cfg.train.batch_size))
+        params.num_data_workers = max(0, int(cfg.train.num_workers))
+        params.learning_rate = float(cfg.train.init_lr)
+
+        if not hasattr(params, "weight_decay"):
+            params.weight_decay = 0.0
+        if not hasattr(params, "optimizer"):
+            params.optimizer = "AdamW"
+        if not hasattr(params, "embedding_offset"):
+            params.embedding_offset = 0
+
+        return params
+
+    @staticmethod
+    def _normalize_path_entry(entry: Any) -> list[Any]:
+        if not isinstance(entry, (list, tuple)) or len(entry) < 2:
+            raise ValueError(
+                "MATEY data path entries must be list/tuple values with at least "
+                "[path, dataset_type, ...]."
+            )
+        return list(entry)
+
+    @staticmethod
+    def _to_abs_path(path_str: str) -> Path:
+        path = Path(path_str)
+        if not path.is_absolute():
+            path = Path.cwd() / path
+        return path.resolve()
+
+    @staticmethod
+    def _as_config_path(path: Path) -> str:
+        try:
+            return str(path.resolve().relative_to(Path.cwd()))
+        except ValueError:
+            return str(path.resolve())
+
+    def _configure_solps_staged_pool(self, params: Any) -> None:
+        train_paths = [
+            self._normalize_path_entry(entry)
+            for entry in getattr(params, "train_data_paths", [])
+        ]
+        val_paths = [
+            self._normalize_path_entry(entry)
+            for entry in getattr(params, "valid_data_paths", [])
+        ]
+
+        if not train_paths or not val_paths:
+            raise ValueError(
+                "MATEY SOLPS split requires non-empty train_data_paths and "
+                "valid_data_paths."
+            )
+
+        train_solps = [entry for entry in train_paths if str(entry[1]) == "SOLPS2D"]
+        val_solps = [entry for entry in val_paths if str(entry[1]) == "SOLPS2D"]
+
+        if not train_solps and not val_solps:
+            return
+
+        if len(train_solps) != len(train_paths) or len(val_solps) != len(val_paths):
+            raise ValueError(
+                "MATEY SOLPS split mode does not support mixing SOLPS2D with "
+                "non-SOLPS datasets in train/valid data paths."
+            )
+
+        signatures = {tuple(entry[1:]) for entry in (train_solps + val_solps)}
+        if len(signatures) != 1:
+            raise ValueError(
+                "MATEY SOLPS split requires matching dataset metadata across "
+                "train_data_paths and valid_data_paths (dataset/include/tk-head)."
+            )
+
+        src_paths = [
+            self._to_abs_path(str(entry[0])) for entry in train_solps + val_solps
+        ]
+        split_view = stage_solps_split(
+            source_roots=src_paths,
+            ratios=DEFAULT_MATEY_TRAIN_VAL_TEST,
+            seed=self._split_seed,
+            cache_root=DEFAULT_SOLPS_CACHE_ROOT,
+        )
+        self._solps_split = split_view
+        signature = list(next(iter(signatures)))
+        train_entry = [self._as_config_path(split_view.train_dir), *signature]
+        val_entry = [self._as_config_path(split_view.val_dir), *signature]
+        params.train_data_paths = [copy.deepcopy(train_entry)]
+        params.valid_data_paths = [copy.deepcopy(val_entry)]
+
+    def _configure_user_data_paths(self, params: Any) -> None:
+        train_dir = self._data_root / "train"
+        val_dir = self._data_root / "valid"
+
+        # Keep compatibility with non-SOLPS test fixtures that mock custom paths.
+        if not train_dir.exists() and not val_dir.exists():
+            return
+
+        if not train_dir.exists() or not val_dir.exists():
+            raise FileNotFoundError(
+                "Matey data root must contain both 'train/' and 'valid/' directories. "
+                f"Missing paths: train={train_dir.exists()}, valid={val_dir.exists()}."
+            )
+
+        params.train_data_paths = [[self._as_config_path(train_dir), "SOLPS2D", "", "tk-2D"]]
+        params.valid_data_paths = [[self._as_config_path(val_dir), "SOLPS2D", "", "tk-2D"]]
+
+    def _configure_data_split(self, params: Any) -> None:
+        self._configure_user_data_paths(params)
+        params.train_val_test = list(DEFAULT_MATEY_TRAIN_VAL_TEST)
+        self._configure_solps_staged_pool(params)
+
+    def _log_solps_split_details(self) -> None:
+        if self._solps_split is None or self._solps_split_logged:
+            return
+
+        logger = get_logger()
+        counts = self._solps_split.counts
+        logger.info("==== MATEY SOLPS staged split ready ====", level=0)
+        logger.info(f"\tCache dir: {self._solps_split.cache_dir}", level=1)
+        logger.info(
+            f"\tSplit counts train/val/test: {counts['train']}/{counts['val']}/{counts['test']}",
+            level=1,
+        )
+        logger.info(
+            f"\tSplit cache reused: {self._solps_split.reused_cache}",
+            level=1,
+        )
+        self._solps_split_logged = True
+
+    def _params_for_loader_split(self, split: str) -> Any:
+        loader_params = copy.deepcopy(self._params)
+        if self._solps_split is None:
+            return loader_params
+
+        if split == "train":
+            loader_params.train_val_test = [1.0, 0.0, 0.0]
+        elif split == "val":
+            loader_params.train_val_test = [0.0, 1.0, 0.0]
+        else:
+            loader_params.train_val_test = [0.0, 0.0, 1.0]
+        return loader_params
+
+    @staticmethod
+    def _build_matey_model(
+        cfg: Config, params: Any, modules: dict[str, Any]
+    ) -> nn.Module:
+        model_type = str(getattr(params, "model_type", "vit_all2all"))
+        if model_type == "avit":
+            model = modules["build_avit"](params)
+        elif model_type == "svit":
+            model = modules["build_svit"](params)
+        elif model_type == "turbt":
+            model = modules["build_turbt"](params)
+        else:
+            model = modules["build_vit"](params)
+
+        MATEYHarness._load_pretrained_weights_if_available(
+            model=model,
+            pretrained_path=cfg.model.pretrained_path,
+        )
+
+        if bool(getattr(params, "compile", False)):
+            model = torch.compile(model)
+
+        return model
+
+    @staticmethod
+    def _load_pretrained_weights_if_available(
+        model: nn.Module, pretrained_path: str
+    ) -> None:
+        raw_path = str(pretrained_path).strip()
+        if not raw_path:
+            return
+
+        checkpoint_path = Path(raw_path).expanduser()
+        if not checkpoint_path.is_absolute():
+            checkpoint_path = Path.cwd() / checkpoint_path
+        checkpoint_path = checkpoint_path.resolve()
+
+        if not checkpoint_path.exists():
+            raise FileNotFoundError(
+                f"MATEY pretrained checkpoint not found: {checkpoint_path}"
+            )
+        if checkpoint_path.is_dir():
+            raise ValueError(
+                "MATEY pretrained checkpoint path must be a file, not a directory: "
+                f"{checkpoint_path}"
+            )
+
+        checkpoint = torch.load(
+            checkpoint_path,
+            map_location="cpu",
+            weights_only=False,
+        )
+        state_dict = MATEYHarness._extract_model_state_dict(checkpoint)
+
+        attempts = [
+            ("raw", state_dict),
+            ("strip_module_prefix", MATEYHarness._strip_prefix(state_dict, "module.")),
+            (
+                "strip_orig_mod_prefix",
+                MATEYHarness._strip_prefix(state_dict, "_orig_mod."),
+            ),
+            (
+                "strip_module_then_orig_mod",
+                MATEYHarness._strip_prefix(
+                    MATEYHarness._strip_prefix(state_dict, "module."),
+                    "_orig_mod.",
+                ),
+            ),
+            (
+                "strip_orig_mod_then_module",
+                MATEYHarness._strip_prefix(
+                    MATEYHarness._strip_prefix(state_dict, "_orig_mod."),
+                    "module.",
+                ),
+            ),
+        ]
+
+        logger = get_logger()
+        last_error: RuntimeError | None = None
+        for transform_name, candidate in attempts:
+            try:
+                model.load_state_dict(candidate)
+                logger.info(
+                    f"Loaded MATEY pretrained weights: {checkpoint_path}",
+                    level=0,
+                )
+                if transform_name != "raw":
+                    logger.info(
+                        f"\tApplied checkpoint key transform: {transform_name}",
+                        level=1,
+                    )
+                return
+            except RuntimeError as exc:
+                last_error = exc
+
+        raise RuntimeError(
+            "Failed to load MATEY pretrained weights from "
+            f"{checkpoint_path}. Last error: {last_error}"
+        )
+
+    @staticmethod
+    def _extract_model_state_dict(checkpoint: Any) -> dict[str, Tensor]:
+        if isinstance(checkpoint, dict):
+            for key in ("model_state", "state_dict", "model_state_dict", "model"):
+                value = checkpoint.get(key)
+                if isinstance(value, dict):
+                    return cast(dict[str, Tensor], value)
+
+            # Raw state_dict case (all tensor-ish values)
+            if checkpoint and all(hasattr(v, "shape") for v in checkpoint.values()):
+                return cast(dict[str, Tensor], checkpoint)
+
+        raise ValueError(
+            "Unsupported MATEY checkpoint format. Expected a state_dict or a dict "
+            "containing one of: model_state, state_dict, model_state_dict, model."
+        )
+
+    @staticmethod
+    def _strip_prefix(state_dict: dict[str, Tensor], prefix: str) -> dict[str, Tensor]:
+        if not prefix:
+            return state_dict
+        plen = len(prefix)
+        return {
+            (key[plen:] if key.startswith(prefix) else key): value
+            for key, value in state_dict.items()
+        }
+
+    def _dispose_current_loaders(self) -> None:
+        if self._cur_train_loader is not None:
+            del self._cur_train_loader
+            self._cur_train_loader = None
+        if self._cur_val_loader is not None:
+            del self._cur_val_loader
+            self._cur_val_loader = None
+        gc.collect()
+
+    @staticmethod
+    def _set_stream_seed(seed: int) -> None:
+        random.seed(seed)
+        torch.manual_seed(seed)
+        if torch.cuda.is_available():
+            torch.cuda.manual_seed_all(seed)
+
+    @contextmanager
+    def _matey_single_worker_loader_patch(self, get_data_loader: Callable[..., Any]):
+        """Patch MATEY's DataLoader symbol to support num_workers=0 safely."""
+        module = sys.modules.get(get_data_loader.__module__)
+        if module is None:
+            yield
+            return
+
+        original_loader = getattr(module, "DataLoader", None)
+        if original_loader is None:
+            yield
+            return
+
+        def _patched_loader(*args: Any, **kwargs: Any):
+            if int(kwargs.get("num_workers", 0)) == 0:
+                kwargs["prefetch_factor"] = None
+                kwargs["persistent_workers"] = False
+            return original_loader(*args, **kwargs)
+
+        setattr(module, "DataLoader", _patched_loader)
+        try:
+            yield
+        finally:
+            setattr(module, "DataLoader", original_loader)
+
+    def _build_loader(self, params: Any, split: str) -> tuple[Any, Any, Any]:
+        get_data_loader = self._modules["get_data_loader"]
+        with self._matey_single_worker_loader_patch(get_data_loader):
+            return get_data_loader(
+                params,
+                params.train_data_paths
+                if split == "train"
+                else params.valid_data_paths,
+                False,
+                split=split,
+                train_offset=getattr(params, "embedding_offset", 0),
+                global_rank=0,
+                num_sp_groups=1,
+                group_size=1,
+            )
+
+    def _select_target_tensor(
+        self, target: MateyTargetBatch | Tensor, rollout_steps: int | None
+    ) -> Tensor:
+        tar = target.target if isinstance(target, MateyTargetBatch) else target
+        if tar.ndim == 6:
+            step = rollout_steps
+            if step is None and isinstance(target, MateyTargetBatch):
+                if target.leadtime is not None and target.leadtime.numel() > 0:
+                    step = int(target.leadtime.min().item())
+            if step is None:
+                step = 1
+            step = max(1, min(int(step), tar.shape[1]))
+            tar = tar[:, step - 1, ...]
+        return tar
+
+    @staticmethod
+    def _compute_nrmse(pred: Tensor, target: Tensor) -> Tensor:
+        eps = 1e-7
+        if pred.ndim == 2:
+            num = (pred - target).pow(2).mean(dim=0)
+            den = target.pow(2).mean(dim=0) + eps
+            return torch.sqrt((num / den).mean())
+
+        spatial_dims = tuple(range(2, pred.ndim))
+        num = (pred - target).pow(2).mean(spatial_dims)
+        den = target.pow(2).mean(spatial_dims) + eps
+        return torch.sqrt((num / den).mean())
+
+    @staticmethod
+    def _compute_rmse(pred: Tensor, target: Tensor) -> Tensor:
+        if pred.ndim == 2:
+            return (pred - target).pow(2).mean(dim=0).sqrt().mean()
+
+        spatial_dims = tuple(range(2, pred.ndim))
+        return (pred - target).pow(2).mean(spatial_dims).sqrt().mean()
+
+    def _nrmse_metric(self, y_hat: Tensor, y: MateyTargetBatch) -> Tensor:
+        target = self._select_target_tensor(y, self._adapter_model.last_rollout_steps)
+        return self._compute_nrmse(y_hat, target)
+
+    def _rmse_metric(self, y_hat: Tensor, y: MateyTargetBatch) -> Tensor:
+        target = self._select_target_tensor(y, self._adapter_model.last_rollout_steps)
+        return self._compute_rmse(y_hat, target)
diff --git a/examples/matey/model_outer_loop.py b/examples/matey/model_outer_loop.py
new file mode 100644
index 0000000..bf46b6f
--- /dev/null
+++ b/examples/matey/model_outer_loop.py
@@ -0,0 +1,417 @@
+from __future__ import annotations
+# mypy: ignore-errors
+
+import copy
+import gc
+import random
+import sys
+import types
+from contextlib import contextmanager
+from dataclasses import replace
+from pathlib import Path
+from typing import Any, Callable
+
+import torch
+from torch import Tensor, nn
+from torch.optim import Optimizer
+
+from config.configuration import Config
+from examples.matey.src.matey_batches import (
+    MateyInputBatch,
+    MateyLoaderAdapter as _MateyLoaderAdapter,
+    MateyTargetBatch,
+)
+from model.torch_model_harness import BaseModelHarness
+
+DEFAULT_MATEY_YAML = Path("examples/matey/Demo_SOLPS_vit.yaml")
+DEFAULT_MATEY_PROFILE = "basic_config"
+DEFAULT_SOLPS2DWION_ROOT = Path("examples/matey/dump/SOLPS2DwION")
+SUPPORTED_UPDATE_MODES = {"base", "none"}
+DEFAULT_NOISE_STD = 0.02
+
+
+class _InputL2MetricModel(nn.Module):
+    """Placeholder outer-loop model: returns per-sample L2 norm of inputs."""
+
+    def __init__(self) -> None:
+        super().__init__()
+
+    def forward(self, batch: MateyInputBatch) -> Tensor:
+        if batch.is_graph:
+            raise NotImplementedError(
+                "Outer-loop placeholder model currently supports tensor inputs only."
+            )
+        if batch.input is None:
+            raise RuntimeError("Matey input batch is missing `input` tensor.")
+
+        # Keep autograd graph valid for `base` updater even without model params.
+        inp = batch.input.float().detach().requires_grad_(True)
+        if inp.ndim == 6 and inp.shape[1] >= 1:
+            # Monitor only the first temporal slice t=0: CxDxHxW.
+            inp_t0 = inp[:, 0, ...]
+        else:
+            inp_t0 = inp
+
+        l2 = torch.linalg.vector_norm(
+            inp_t0.reshape(inp_t0.shape[0], -1), ord=2, dim=1
+        )
+        return l2.unsqueeze(-1)
+
+
+class _NoOpOptimizer:
+    """Optimizer interface shim for parameter-free models."""
+
+    def zero_grad(self, set_to_none: bool = False) -> None:
+        return None
+
+    def step(self, closure: Callable[[], float] | None = None) -> float | None:
+        if closure is None:
+            return None
+        return closure()
+
+
+class _NoisyMateyLoader:
+    """Wrap a Matey adapter and inject Gaussian noise into `input` tensors."""
+
+    def __init__(self, base_loader: _MateyLoaderAdapter, scale: float, seed: int):
+        self._base_loader = base_loader
+        self._scale = float(max(0.0, scale))
+        self._rng = torch.Generator(device="cpu")
+        self._rng.manual_seed(int(seed))
+
+    def __len__(self) -> int:
+        return len(self._base_loader)
+
+    def __iter__(self):
+        for input_batch, target_batch in self._base_loader:
+            if (
+                self._scale <= 0.0
+                or input_batch.is_graph
+                or input_batch.input is None
+            ):
+                yield input_batch, target_batch
+                continue
+
+            noisy_input = input_batch.input.clone()
+            if noisy_input.ndim == 6 and noisy_input.shape[1] >= 1:
+                # Inject white noise only on the T=1 slice (index 0): CxDxHxW.
+                noise = torch.randn(
+                    noisy_input[:, 0, ...].shape,
+                    dtype=noisy_input.dtype,
+                    device=noisy_input.device,
+                    generator=self._rng,
+                ) * self._scale
+                noisy_input[:, 0, ...] = noisy_input[:, 0, ...] + noise
+            else:
+                noise = torch.randn(
+                    noisy_input.shape,
+                    dtype=noisy_input.dtype,
+                    device=noisy_input.device,
+                    generator=self._rng,
+                ) * self._scale
+                noisy_input = noisy_input + noise
+
+            yield replace(input_batch, input=noisy_input), target_batch
+
+
+class MATEYHarness(BaseModelHarness):
+    """Outer-loop Matey harness with synthetic input-noise drift."""
+
+    def __init__(self, cfg: Config):
+        self._assert_supported_update_mode(cfg)
+
+        self._data_root = self._resolve_data_root(cfg)
+        self._validate_data_root(self._data_root)
+
+        modules = self._load_matey_modules()
+        params = self._build_matey_params(cfg, modules["YParams"])
+        self._configure_data_paths(params)
+
+        self._modules = modules
+        self._params = params
+
+        model = _InputL2MetricModel()
+        super().__init__(cfg=cfg, model=model)
+
+        self.task_counter = 0
+        self._cur_train_loader: Any | None = None
+        self._cur_val_loader: Any | None = None
+
+        self.eval_metrics = {
+            "input_l2": self._input_l2_metric,
+            "loss": self.get_criterion(),
+        }
+        self.higher_is_better = {"input_l2": False, "loss": False}
+
+        #self._debug_input_tensor_shapes()
+
+    def get_optmizer(self) -> Optimizer:
+        return _NoOpOptimizer()
+
+    def update_data_stream(self) -> None:
+        self._dispose_current_loaders()
+        stream_seed = int(self.cfg.seed + self.task_counter)
+        self._set_stream_seed(stream_seed)
+
+        train_loader, train_dataset, _ = self._build_loader(self._params, split="train")
+        val_loader, val_dataset, _ = self._build_loader(self._params, split="val")
+
+        scale = self._noise_std_for_stream(self.task_counter)
+
+        self._cur_train_loader = _NoisyMateyLoader(
+            _MateyLoaderAdapter(train_loader, train_dataset),
+            scale=scale,
+            seed=stream_seed,
+        )
+        self._cur_val_loader = _NoisyMateyLoader(
+            _MateyLoaderAdapter(val_loader, val_dataset),
+            scale=scale,
+            seed=stream_seed + 10_000,
+        )
+
+        self.task_counter += 1
+
+    def get_cur_data_loaders(self) -> tuple[Any, Any]:
+        if self._cur_train_loader is None or self._cur_val_loader is None:
+            raise RuntimeError(
+                "Matey stream has not been initialized. Call update_data_stream() first."
+            )
+        return self._cur_train_loader, self._cur_val_loader
+
+    def get_hist_data_loaders(self) -> tuple[None, None]:
+        return None, None
+
+    def get_criterion(self):
+        def criterion(y_hat: Tensor, y: MateyTargetBatch | Tensor) -> Tensor:
+            target_l2 = self._target_l2(y)
+            return torch.mean((y_hat - target_l2) ** 2)
+
+        return criterion
+
+    def _unpack(
+        self, batch: tuple[MateyInputBatch, MateyTargetBatch | Tensor]
+    ) -> tuple[MateyInputBatch, Tensor]:
+        x, y = batch
+        return x, self._as_target_tensor(y)
+
+    @staticmethod
+    def _assert_supported_update_mode(cfg: Config) -> None:
+        if cfg.continual_learning.update_mode not in SUPPORTED_UPDATE_MODES:
+            raise NotImplementedError(
+                "Matey outer-loop harness supports only continual_learning.update_mode "
+                "in {'base', 'none'}."
+            )
+
+    @staticmethod
+    def _resolve_data_root(cfg: Config) -> Path:
+        raw = cfg.data.path.strip()
+        path = Path(raw) if raw else DEFAULT_SOLPS2DWION_ROOT
+        if not path.is_absolute():
+            path = Path.cwd() / path
+        return path.resolve()
+
+    @staticmethod
+    def _validate_data_root(data_root: Path) -> None:
+        if not data_root.exists():
+            raise FileNotFoundError(
+                f"Matey data root path does not exist: {data_root}. "
+                "Set [data].path to a local SOLPS2DwION dataset root, or leave it empty "
+                "to use examples/matey/dump/SOLPS2DwION."
+            )
+        if not data_root.is_dir():
+            raise NotADirectoryError(
+                f"Matey data root path is not a directory: {data_root}"
+            )
+        if not any(data_root.rglob("*.nc")):
+            raise FileNotFoundError(
+                f"No netCDF files were found under Matey data root: {data_root}"
+            )
+
+        if not DEFAULT_MATEY_YAML.exists():
+            raise FileNotFoundError(
+                f"Required Matey YAML config was not found: {DEFAULT_MATEY_YAML}."
+            )
+
+    def _load_matey_modules(self) -> dict[str, Any]:
+        self._install_optional_import_shims()
+        try:
+            # Import netCDF4 before h5py to avoid HDF5 library collision.
+            import netCDF4 as _netCDF4  # noqa: F401
+
+            from matey.data_utils.datasets import get_data_loader
+            from matey.utils.YParams import YParams
+        except ModuleNotFoundError:
+            try:
+                from examples.matey.MATEY.matey.data_utils.datasets import get_data_loader
+                from examples.matey.MATEY.matey.utils.YParams import YParams
+            except ModuleNotFoundError as exc:
+                raise RuntimeError(
+                    "Matey dependency import failed. Install with "
+                    "`poetry install --extras matey` or ensure local MATEY module is importable."
+                ) from exc
+
+        return {
+            "YParams": YParams,
+            "get_data_loader": get_data_loader,
+        }
+
+    @staticmethod
+    def _install_optional_import_shims() -> None:
+        """
+        MATEY's dataset registry imports graph/XGC modules eagerly.
+        For outer-loop SOLPS-only usage, provide a minimal xgc_reader shim so
+        optional graph dependencies do not block import.
+        """
+        if "xgc_reader" in sys.modules:
+            return
+
+        try:
+            import xgc_reader as _xgc_reader  # noqa: F401
+
+            return
+        except ModuleNotFoundError:
+            pass
+
+        shim_pkg = types.ModuleType("xgc_reader")
+        shim_base = types.ModuleType("xgc_reader.base")
+
+        def _missing_xgc1(*_args: Any, **_kwargs: Any) -> None:
+            raise RuntimeError(
+                "xgc_reader is not installed/available. "
+                "It is only required for graph/XGC datasets, not SOLPS outer-loop runs."
+            )
+
+        shim_base.xgc1 = _missing_xgc1
+        shim_pkg.base = shim_base
+        sys.modules["xgc_reader"] = shim_pkg
+        sys.modules["xgc_reader.base"] = shim_base
+
+    def _build_matey_params(self, cfg: Config, yparams_cls: type[Any]) -> Any:
+        params = yparams_cls(str(DEFAULT_MATEY_YAML), DEFAULT_MATEY_PROFILE)
+
+        params.use_ddp = False
+        params.use_fsdp = False
+        params.log_to_screen = False
+        params.log_to_wandb = False
+        params.enable_sync = False
+        params.profiling = False
+
+        params.batch_size = max(1, int(cfg.train.batch_size))
+        params.num_data_workers = max(0, int(cfg.train.num_workers))
+        params.learning_rate = float(cfg.train.init_lr)
+
+        return params
+
+    @staticmethod
+    def _as_config_path(path: Path) -> str:
+        try:
+            return str(path.resolve().relative_to(Path.cwd()))
+        except ValueError:
+            return str(path.resolve())
+
+    def _configure_data_paths(self, params: Any) -> None:
+        entry = [self._as_config_path(self._data_root), "SOLPS2DwION", "", "tk-2D"]
+        params.train_data_paths = [copy.deepcopy(entry)]
+        params.valid_data_paths = [copy.deepcopy(entry)]
+
+    def _debug_input_tensor_shapes(self, max_batches: int = 3) -> None:
+        print("==== Matey outer-loop input shape probe ====", flush=True)
+        try:
+            raw_loader, mixed_dataset, _ = self._build_loader(self._params, split="train")
+            probe_loader = _MateyLoaderAdapter(raw_loader, mixed_dataset)
+            saw_batch = False
+
+            for batch_idx, (input_batch, _) in enumerate(probe_loader):
+                if batch_idx >= max_batches:
+                    break
+                saw_batch = True
+                if input_batch.input is None:
+                    print(
+                        f"\tProbe batch {batch_idx}: no tensor `input` present.",
+                        flush=True,
+                    )
+                else:
+                    print(
+                        f"\tProbe batch {batch_idx}: inps shape={tuple(input_batch.input.shape)}",
+                        flush=True,
+                    )
+
+            if not saw_batch:
+                print("\tProbe loader returned zero batches.", flush=True)
+        except Exception as exc:
+            print(f"Input shape probe failed: {exc}", flush=True)
+        finally:
+            gc.collect()
+
+    def _dispose_current_loaders(self) -> None:
+        if self._cur_train_loader is not None:
+            del self._cur_train_loader
+            self._cur_train_loader = None
+        if self._cur_val_loader is not None:
+            del self._cur_val_loader
+            self._cur_val_loader = None
+        gc.collect()
+
+    @staticmethod
+    def _set_stream_seed(seed: int) -> None:
+        random.seed(seed)
+        torch.manual_seed(seed)
+        if torch.cuda.is_available():
+            torch.cuda.manual_seed_all(seed)
+
+    @staticmethod
+    def _noise_std_for_stream(stream_idx: int) -> float:
+        return DEFAULT_NOISE_STD * float(stream_idx)
+
+    @contextmanager
+    def _matey_single_worker_loader_patch(self, get_data_loader: Callable[..., Any]):
+        """Patch MATEY's DataLoader symbol to support num_workers=0 safely."""
+        module = sys.modules.get(get_data_loader.__module__)
+        if module is None:
+            yield
+            return
+
+        original_loader = getattr(module, "DataLoader", None)
+        if original_loader is None:
+            yield
+            return
+
+        def _patched_loader(*args: Any, **kwargs: Any):
+            if int(kwargs.get("num_workers", 0)) == 0:
+                kwargs["prefetch_factor"] = None
+                kwargs["persistent_workers"] = False
+            return original_loader(*args, **kwargs)
+
+        setattr(module, "DataLoader", _patched_loader)
+        try:
+            yield
+        finally:
+            setattr(module, "DataLoader", original_loader)
+
+    def _build_loader(self, params: Any, split: str) -> tuple[Any, Any, Any]:
+        get_data_loader = self._modules["get_data_loader"]
+        with self._matey_single_worker_loader_patch(get_data_loader):
+            return get_data_loader(
+                params,
+                params.train_data_paths if split == "train" else params.valid_data_paths,
+                False,
+                split=split,
+                train_offset=getattr(params, "embedding_offset", 0),
+                global_rank=0,
+                num_sp_groups=1,
+                group_size=1,
+            )
+
+    @staticmethod
+    def _as_target_tensor(target: MateyTargetBatch | Tensor) -> Tensor:
+        return target.target if isinstance(target, MateyTargetBatch) else target
+
+    @staticmethod
+    def _target_l2(target: MateyTargetBatch | Tensor) -> Tensor:
+        tar = MATEYHarness._as_target_tensor(target).float()
+        l2 = torch.linalg.vector_norm(tar.reshape(tar.shape[0], -1), ord=2, dim=1)
+        return l2.unsqueeze(-1)
+
+    @staticmethod
+    def _input_l2_metric(y_hat: Tensor, _y: MateyTargetBatch | Tensor) -> Tensor:
+        return y_hat.mean()
diff --git a/examples/matey/poetry.lock b/examples/matey/poetry.lock
new file mode 100644
index 0000000..f9f75e8
--- /dev/null
+++ b/examples/matey/poetry.lock
@@ -0,0 +1,2894 @@
+# This file is automatically @generated by Poetry 2.2.1 and should not be changed by hand.
+
+[[package]]
+name = "adios2"
+version = "2.11.0.1012"
+description = "The Adaptable Input Output System version 2"
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
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+]
+
+[package.dependencies]
+numpy = ">=1.22"
+
+[package.extras]
+dev = ["black", "pip (>=21.3)"]
+
+[[package]]
+name = "aiohappyeyeballs"
+version = "2.6.1"
+description = "Happy Eyeballs for asyncio"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
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+multidict = ">=4.0"
+propcache = ">=0.2.1"
+
+[[package]]
+name = "zarr"
+version = "3.1.5"
+description = "An implementation of chunked, compressed, N-dimensional arrays for Python"
+optional = false
+python-versions = ">=3.11"
+groups = ["main"]
+files = [
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+google-crc32c = ">=1.5"
+numcodecs = ">=0.14"
+numpy = ">=1.26"
+packaging = ">=22.0"
+typing-extensions = ">=4.9"
+
+[package.extras]
+cli = ["typer"]
+docs = ["astroid (<4)", "griffe-inherited-docstrings", "markdown-exec[ansi]", "mike (>=2.1.3)", "mkdocs (>=1.6.1)", "mkdocs-material[imaging] (>=9.6.14)", "mkdocs-redirects (>=1.2.0)", "mkdocstrings (>=0.29.1)", "mkdocstrings-python (>=1.16.10)", "numcodecs[msgpack]", "pytest", "rich", "ruff", "s3fs (>=2023.10.0)", "towncrier"]
+gpu = ["cupy-cuda12x"]
+optional = ["rich", "universal-pathlib"]
+remote = ["fsspec (>=2023.10.0)", "obstore (>=0.5.1)"]
+remote-tests = ["botocore", "fsspec (>=2023.10.0)", "moto[s3,server]", "obstore (>=0.5.1)", "requests", "s3fs (>=2023.10.0)"]
+test = ["coverage (>=7.10)", "hypothesis", "mypy", "numpydoc", "packaging", "pytest (<8.4)", "pytest-accept", "pytest-asyncio", "pytest-cov", "pytest-xdist", "rich", "tomlkit", "uv"]
+
+[metadata]
+lock-version = "2.1"
+python-versions = ">=3.12"
+content-hash = "01b5798ff4d3de976ba866dbdbb762b00e3864ab788a84d4d5c62783821b6c70"
diff --git a/examples/matey/pyproject.toml b/examples/matey/pyproject.toml
new file mode 100644
index 0000000..743b4fe
--- /dev/null
+++ b/examples/matey/pyproject.toml
@@ -0,0 +1,36 @@
+[project]
+name = "basesim-matey-example"
+version = "0.1.0"
+description = "MATEY model harness example for the BaseSim framework"
+requires-python = ">=3.12"
+dependencies = [
+    "matey @ git+ssh://git@github.com/FusionFM/MATEY.git@4e615bb5c86024632e386153bfbed028b38a8262",
+    "einops",
+    "h5py",
+    "timm",
+    "torchinfo",
+    "zarr",
+    "ruamel.yaml==0.17.32",
+    "scipy",
+    "scikit-learn",
+    "torch_geometric",
+    "adios2",
+]
+
+[project.optional-dependencies]
+# Heavy / system-dependent deps -- install manually if needed:
+#   pip install dadaptation==3.1
+#   pip install adan-pytorch==0.1.0
+#   MAX_JOBS=4 pip install flash-attn --no-build-isolation  # (requires CUDA toolkit)
+#   pip install mpi4py              # (requires MPI C library)
+#   pip install netCDF4             # (requires HDF5/netCDF C libs)
+#   pip install git+https://github.com/sandialabs/exodusii.git  # (not on PyPI)
+
+[build-system]
+requires = ["setuptools>=68.0"]
+build-backend = "setuptools.build_meta"
+
+[tool.setuptools]
+# This package has no Python modules of its own to install;
+# it exists solely to declare MATEY's pip-installable dependencies.
+py-modules = []
diff --git a/examples/matey/src/__init__.py b/examples/matey/src/__init__.py
new file mode 100644
index 0000000..abca786
--- /dev/null
+++ b/examples/matey/src/__init__.py
@@ -0,0 +1,16 @@
+from examples.matey.src.matey_batches import (
+    MateyInputBatch,
+    MateyLoaderAdapter,
+    MateyModelAdapter,
+    MateyTargetBatch,
+)
+from examples.matey.src.solps_split import SolpsStagedSplit, stage_solps_split
+
+__all__ = [
+    "MateyInputBatch",
+    "MateyTargetBatch",
+    "MateyLoaderAdapter",
+    "MateyModelAdapter",
+    "SolpsStagedSplit",
+    "stage_solps_split",
+]
diff --git a/examples/matey/src/matey_batches.py b/examples/matey/src/matey_batches.py
new file mode 100644
index 0000000..99043bd
--- /dev/null
+++ b/examples/matey/src/matey_batches.py
@@ -0,0 +1,251 @@
+from __future__ import annotations
+
+import copy
+from dataclasses import dataclass
+from typing import Any, Callable, Optional, cast
+
+import torch
+from torch import Tensor, nn
+
+
+def _move_to_device(x: Any, device: str | torch.device) -> Any:
+    if x is None:
+        return None
+    if hasattr(x, "to"):
+        return x.to(device)
+    return x
+
+
+@dataclass(frozen=True)
+class MateyInputBatch:
+    input: Tensor | None = None
+    graph: Any = None
+    field_labels: Tensor | None = None
+    bcs: Tensor | None = None
+    leadtime: Tensor | None = None
+    cond_field_labels: Tensor | None = None
+    cond_fields: Tensor | None = None
+    cond_input: Tensor | None = None
+    field_labels_out: Tensor | None = None
+    tkhead_name: str | None = None
+    blockdict: dict[str, Any] | None = None
+    is_graph: bool = False
+
+    def to(self, device: str | torch.device) -> MateyInputBatch:
+        return MateyInputBatch(
+            input=cast(Optional[Tensor], _move_to_device(self.input, device)),
+            graph=_move_to_device(self.graph, device),
+            field_labels=cast(
+                Optional[Tensor], _move_to_device(self.field_labels, device)
+            ),
+            bcs=cast(Optional[Tensor], _move_to_device(self.bcs, device)),
+            leadtime=cast(Optional[Tensor], _move_to_device(self.leadtime, device)),
+            cond_field_labels=cast(
+                Optional[Tensor], _move_to_device(self.cond_field_labels, device)
+            ),
+            cond_fields=cast(
+                Optional[Tensor], _move_to_device(self.cond_fields, device)
+            ),
+            cond_input=cast(Optional[Tensor], _move_to_device(self.cond_input, device)),
+            field_labels_out=cast(
+                Optional[Tensor], _move_to_device(self.field_labels_out, device)
+            ),
+            tkhead_name=self.tkhead_name,
+            blockdict=copy.deepcopy(self.blockdict),
+            is_graph=self.is_graph,
+        )
+
+
+@dataclass(frozen=True)
+class MateyTargetBatch:
+    target: Tensor
+    leadtime: Tensor | None = None
+    is_graph: bool = False
+
+    def to(self, device: str | torch.device) -> MateyTargetBatch:
+        return MateyTargetBatch(
+            target=cast(Tensor, _move_to_device(self.target, device)),
+            leadtime=cast(Optional[Tensor], _move_to_device(self.leadtime, device)),
+            is_graph=self.is_graph,
+        )
+
+    @property
+    def shape(self) -> torch.Size:
+        return self.target.shape
+
+
+class MateyLoaderAdapter:
+    def __init__(self, raw_loader: Any, mixed_dataset: Any):
+        self._raw_loader = raw_loader
+        self._mixed_dataset = mixed_dataset
+
+    def __len__(self) -> int:
+        return len(self._raw_loader)
+
+    def __iter__(self):
+        for raw_batch in self._raw_loader:
+            yield self._convert_batch(raw_batch)
+
+    def _convert_batch(
+        self, raw_batch: dict[str, Any]
+    ) -> tuple[MateyInputBatch, MateyTargetBatch]:
+        dset_idx_obj = raw_batch.get("dset_idx")
+        if dset_idx_obj is None:
+            raise RuntimeError("Raw batch is missing dset_idx.")
+        if isinstance(dset_idx_obj, torch.Tensor):
+            dset_idx = int(dset_idx_obj.flatten()[0].item())
+        else:
+            dset_idx = int(dset_idx_obj)
+
+        sub_dset = self._mixed_dataset.sub_dsets[dset_idx]
+        tkhead_name = cast(str | None, getattr(sub_dset, "tkhead_name", None))
+        blockdict = copy.deepcopy(getattr(sub_dset, "blockdict", None))
+
+        field_labels = cast(Tensor, raw_batch["field_labels"])
+        bcs = cast(Tensor, raw_batch["bcs"])
+        leadtime = cast(Optional[Tensor], raw_batch.get("leadtime"))
+        cond_field_labels = cast(Optional[Tensor], raw_batch.get("cond_field_labels"))
+        cond_fields = cast(Optional[Tensor], raw_batch.get("cond_fields"))
+        cond_input = cast(Optional[Tensor], raw_batch.get("cond_input"))
+
+        if "graph" in raw_batch:
+            graph = raw_batch["graph"]
+            graph_leadtime = getattr(graph, "leadtime", leadtime)
+            input_batch = MateyInputBatch(
+                graph=graph,
+                field_labels=field_labels,
+                field_labels_out=cast(
+                    Optional[Tensor], raw_batch.get("field_labels_out")
+                ),
+                bcs=bcs,
+                leadtime=graph_leadtime,
+                cond_field_labels=cond_field_labels,
+                cond_fields=cond_fields,
+                cond_input=cond_input,
+                tkhead_name=tkhead_name,
+                blockdict=blockdict,
+                is_graph=True,
+            )
+            target_batch = MateyTargetBatch(
+                target=cast(Tensor, graph.y),
+                leadtime=cast(Optional[Tensor], graph_leadtime),
+                is_graph=True,
+            )
+            return input_batch, target_batch
+
+        input_batch = MateyInputBatch(
+            input=cast(Tensor, raw_batch["input"]),
+            field_labels=field_labels,
+            field_labels_out=field_labels,
+            bcs=bcs,
+            leadtime=leadtime,
+            cond_field_labels=cond_field_labels,
+            cond_fields=cond_fields,
+            cond_input=cond_input,
+            tkhead_name=tkhead_name,
+            blockdict=blockdict,
+            is_graph=False,
+        )
+        target_batch = MateyTargetBatch(
+            target=cast(Tensor, raw_batch["label"]),
+            leadtime=leadtime,
+            is_graph=False,
+        )
+        return input_batch, target_batch
+
+
+class MateyModelAdapter(nn.Module):
+    def __init__(
+        self,
+        matey_model: nn.Module,
+        params: Any,
+        forward_options_cls: type[Any],
+        rearrange_fn: Callable[..., Any],
+        autoregressive_rollout_fn: Callable[..., Any],
+        determine_turt_levels_fn: Callable[..., Any] | None = None,
+    ):
+        super().__init__()
+        self.matey_model = matey_model
+        self.params = params
+        self._forward_options_cls = forward_options_cls
+        self._rearrange = rearrange_fn
+        self._autoregressive_rollout = autoregressive_rollout_fn
+        self._determine_turt_levels = determine_turt_levels_fn
+        self.last_rollout_steps: int | None = None
+
+    def forward(self, batch: MateyInputBatch) -> Tensor:
+        if batch.field_labels is None or batch.bcs is None:
+            raise RuntimeError("Matey input batch is missing required fields.")
+
+        cond_dict: dict[str, Tensor] = {}
+        if batch.cond_field_labels is not None and batch.cond_fields is not None:
+            cond_dict["labels"] = batch.cond_field_labels
+            cond_dict["fields"] = self._rearrange(
+                batch.cond_fields, "b t c d h w -> t b c d h w"
+            )
+
+        leadtime = batch.leadtime
+        if leadtime is None:
+            leadtime = torch.ones(
+                (1, 1), dtype=torch.long, device=batch.field_labels.device
+            )
+
+        imod = 0
+        hierarchical = getattr(self.params, "hierarchical", None)
+        if isinstance(hierarchical, dict):
+            imod = int(hierarchical.get("nlevels", 1) - 1)
+
+        imod_bottom = 0
+        if (
+            not batch.is_graph
+            and imod > 0
+            and self._determine_turt_levels is not None
+            and batch.tkhead_name is not None
+            and batch.input is not None
+        ):
+            tokenizer_heads_params = cast(
+                dict[str, Any], getattr(self.matey_model, "tokenizer_heads_params")
+            )
+            tk_size = tokenizer_heads_params[batch.tkhead_name][-1]
+            imod_bottom = int(
+                self._determine_turt_levels(tk_size, batch.input.shape[-3:], imod)
+            )
+
+        opts = self._forward_options_cls(
+            imod=imod,
+            imod_bottom=imod_bottom,
+            tkhead_name=batch.tkhead_name,
+            sequence_parallel_group=None,
+            leadtime=leadtime,
+            blockdict=copy.deepcopy(batch.blockdict),
+            cond_dict=copy.deepcopy(cond_dict),
+            cond_input=batch.cond_input,
+            isgraph=batch.is_graph,
+            field_labels_out=(
+                batch.field_labels_out
+                if batch.field_labels_out is not None
+                else batch.field_labels
+            ),
+        )
+
+        if batch.is_graph:
+            inp = batch.graph
+        else:
+            if batch.input is None:
+                raise RuntimeError("Matey tensor input is missing.")
+            inp = self._rearrange(batch.input, "b t c d h w -> t b c d h w")
+
+        if bool(getattr(self.params, "autoregressive", False)):
+            output, rollout_steps = self._autoregressive_rollout(
+                self.matey_model,
+                inp,
+                batch.field_labels,
+                batch.bcs,
+                opts,
+                pushforward=True,
+            )
+            self.last_rollout_steps = int(rollout_steps)
+            return output
+
+        self.last_rollout_steps = None
+        return self.matey_model(inp, batch.field_labels, batch.bcs, opts)
diff --git a/examples/matey/src/solps_split.py b/examples/matey/src/solps_split.py
new file mode 100644
index 0000000..fd2a6cb
--- /dev/null
+++ b/examples/matey/src/solps_split.py
@@ -0,0 +1,346 @@
+from __future__ import annotations
+
+import hashlib
+import json
+import os
+import shutil
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Sequence, cast
+
+DEFAULT_SPLIT_RATIOS = (0.7, 0.15, 0.15)
+_SUPPORTED_SUFFIXES = (".nc",)
+_SUPPORTED_EXODUS_SUFFIX = "_sol.exo"
+_MANIFEST_VERSION = 1
+
+
+@dataclass(frozen=True)
+class SolpsStagedSplit:
+    cache_dir: Path
+    train_dir: Path
+    val_dir: Path
+    test_dir: Path
+    common_root: Path
+    fingerprint: str
+    counts: dict[str, int]
+    reused_cache: bool
+
+
+def stage_solps_split(
+    source_roots: Sequence[Path],
+    *,
+    ratios: tuple[float, float, float] = DEFAULT_SPLIT_RATIOS,
+    seed: int = 0,
+    cache_root: Path = Path("output/matey_split_cache"),
+) -> SolpsStagedSplit:
+    roots = _normalize_source_roots(source_roots)
+    if not roots:
+        raise ValueError("SOLPS split staging requires at least one source directory.")
+
+    common_root = _resolve_common_root(roots)
+    pool = _build_pool(roots=roots, common_root=common_root)
+    total_files = len(pool)
+    if total_files == 0:
+        raise ValueError("SOLPS split staging found no '*.nc' or '*_sol.exo' files.")
+    if total_files < 2:
+        raise ValueError(
+            "SOLPS split staging requires at least two files so train and val are both non-empty."
+        )
+
+    ordered_rel_paths = sorted(pool.keys())
+    rel_paths_by_seed = sorted(
+        ordered_rel_paths,
+        key=lambda rel: (_stable_seed_key(seed=seed, rel_path=rel), rel),
+    )
+
+    train_count, val_count, _test_count = _compute_split_counts(
+        total=total_files,
+        ratios=ratios,
+    )
+
+    train_rel = rel_paths_by_seed[:train_count]
+    val_rel = rel_paths_by_seed[train_count : train_count + val_count]
+    test_rel = rel_paths_by_seed[train_count + val_count :]
+    split_counts: dict[str, int] = {
+        "train": len(train_rel),
+        "val": len(val_rel),
+        "test": len(test_rel),
+    }
+
+    entries: list[dict[str, str | int]] = []
+    for split_name, rel_paths in (
+        ("train", train_rel),
+        ("val", val_rel),
+        ("test", test_rel),
+    ):
+        for rel_path in rel_paths:
+            src = pool[rel_path]
+            stat = src.stat()
+            entries.append(
+                {
+                    "split": split_name,
+                    "rel_path": rel_path,
+                    "source": str(src),
+                    "size": int(stat.st_size),
+                    "mtime_ns": int(stat.st_mtime_ns),
+                }
+            )
+
+    spec = {
+        "version": _MANIFEST_VERSION,
+        "seed": int(seed),
+        "ratios": [float(r) for r in ratios],
+        "common_root": str(common_root),
+        "source_roots": [str(path) for path in roots],
+        "entries": entries,
+        "counts": split_counts,
+    }
+
+    fingerprint = hashlib.sha256(
+        json.dumps(spec, sort_keys=True, separators=(",", ":")).encode("utf-8")
+    ).hexdigest()[:16]
+
+    cache_root_abs = (
+        cache_root if cache_root.is_absolute() else (Path.cwd() / cache_root)
+    )
+    cache_dir = cache_root_abs.resolve() / fingerprint
+    manifest_path = cache_dir / "manifest.json"
+
+    if _can_reuse_cache(cache_dir=cache_dir, manifest_path=manifest_path, spec=spec):
+        return SolpsStagedSplit(
+            cache_dir=cache_dir,
+            train_dir=cache_dir / "train",
+            val_dir=cache_dir / "val",
+            test_dir=cache_dir / "test",
+            common_root=common_root,
+            fingerprint=fingerprint,
+            counts=split_counts,
+            reused_cache=True,
+        )
+
+    if cache_dir.exists():
+        shutil.rmtree(cache_dir)
+
+    for split_name in ("train", "val", "test"):
+        (cache_dir / split_name).mkdir(parents=True, exist_ok=True)
+
+    for entry in entries:
+        split_name = str(entry["split"])
+        rel_file = Path(str(entry["rel_path"]))
+        source = Path(str(entry["source"]))
+        destination = cache_dir / split_name / rel_file
+        _materialize_link(source=source, destination=destination)
+
+    manifest_payload = {"fingerprint": fingerprint, "spec": spec}
+    manifest_path.write_text(json.dumps(manifest_payload, indent=2), encoding="utf-8")
+
+    return SolpsStagedSplit(
+        cache_dir=cache_dir,
+        train_dir=cache_dir / "train",
+        val_dir=cache_dir / "val",
+        test_dir=cache_dir / "test",
+        common_root=common_root,
+        fingerprint=fingerprint,
+        counts=split_counts,
+        reused_cache=False,
+    )
+
+
+def _normalize_source_roots(source_roots: Sequence[Path]) -> list[Path]:
+    roots: list[Path] = []
+    for root in source_roots:
+        resolved = root if root.is_absolute() else (Path.cwd() / root)
+        resolved = resolved.resolve()
+        if not resolved.exists() or not resolved.is_dir():
+            raise ValueError(
+                f"SOLPS source root does not exist or is not a directory: {resolved}"
+            )
+        roots.append(resolved)
+    return roots
+
+
+def _resolve_common_root(paths: Sequence[Path]) -> Path:
+    try:
+        from os.path import commonpath
+
+        common = Path(commonpath([str(path) for path in paths])).resolve()
+    except ValueError as exc:
+        raise ValueError(
+            f"Could not derive a shared SOLPS root from: {[str(path) for path in paths]}"
+        ) from exc
+
+    if common == common.parent:
+        raise ValueError(
+            "Shared SOLPS root resolved to filesystem root; please make source paths more specific."
+        )
+
+    return common
+
+
+def _is_hidden(relative_path: Path) -> bool:
+    return any(part.startswith(".") for part in relative_path.parts)
+
+
+def _is_supported_file(path: Path) -> bool:
+    if path.suffix in _SUPPORTED_SUFFIXES:
+        return True
+    return path.name.endswith(_SUPPORTED_EXODUS_SUFFIX)
+
+
+def _build_pool(roots: Sequence[Path], common_root: Path) -> dict[str, Path]:
+    pool: dict[str, Path] = {}
+    for root in roots:
+        for candidate in root.rglob("*"):
+            if not candidate.is_file():
+                continue
+
+            rel_to_root = candidate.relative_to(root)
+            if _is_hidden(rel_to_root):
+                continue
+            if not _is_supported_file(candidate):
+                continue
+
+            rel_to_common = candidate.relative_to(common_root)
+            rel_key = rel_to_common.as_posix()
+            existing = pool.get(rel_key)
+            resolved_candidate = candidate.resolve()
+
+            if existing is not None and existing != resolved_candidate:
+                raise ValueError(
+                    "SOLPS split staging found duplicate relative file paths under the shared "
+                    f"root: {rel_key}"
+                )
+
+            pool[rel_key] = resolved_candidate
+
+    return pool
+
+
+def _stable_seed_key(*, seed: int, rel_path: str) -> str:
+    payload = f"{seed}:{rel_path}".encode("utf-8")
+    return hashlib.sha256(payload).hexdigest()
+
+
+def _compute_split_counts(
+    *, total: int, ratios: tuple[float, float, float]
+) -> tuple[int, int, int]:
+    if len(ratios) != 3:
+        raise ValueError("SOLPS split ratios must contain exactly three values.")
+
+    fractions = [float(v) for v in ratios]
+    if any(v < 0 for v in fractions):
+        raise ValueError("SOLPS split ratios must be non-negative.")
+
+    ratio_sum = sum(fractions)
+    if abs(ratio_sum - 1.0) > 1e-9:
+        raise ValueError(
+            f"SOLPS split ratios must sum to 1.0. Received sum={ratio_sum:.8f}."
+        )
+
+    raw_counts = [value * total for value in fractions]
+    counts = [int(value) for value in raw_counts]
+
+    remaining = total - sum(counts)
+    if remaining > 0:
+        remainders = [raw - int(raw) for raw in raw_counts]
+        for idx in sorted(range(3), key=lambda i: remainders[i], reverse=True):
+            if remaining == 0:
+                break
+            counts[idx] += 1
+            remaining -= 1
+
+    _ensure_non_empty_train_val(counts)
+    return counts[0], counts[1], counts[2]
+
+
+def _ensure_non_empty_train_val(counts: list[int]) -> None:
+    train_idx = 0
+    val_idx = 1
+
+    if counts[train_idx] == 0:
+        donor = _find_donor_split(counts=counts, exclude={train_idx})
+        if donor is None:
+            raise ValueError(
+                "SOLPS split staging could not allocate at least one file to train."
+            )
+        counts[donor] -= 1
+        counts[train_idx] += 1
+
+    if counts[val_idx] == 0:
+        donor = _find_donor_split(counts=counts, exclude={val_idx, train_idx})
+        if donor is None:
+            donor = _find_donor_split(counts=counts, exclude={val_idx})
+        if donor is None:
+            raise ValueError(
+                "SOLPS split staging could not allocate at least one file to val."
+            )
+        counts[donor] -= 1
+        counts[val_idx] += 1
+
+    if counts[train_idx] <= 0 or counts[val_idx] <= 0:
+        raise ValueError(
+            "SOLPS split staging requires non-empty train and val partitions."
+        )
+
+
+def _find_donor_split(counts: Sequence[int], exclude: set[int]) -> int | None:
+    candidates = [
+        idx for idx, value in enumerate(counts) if idx not in exclude and value > 1
+    ]
+    if candidates:
+        return max(candidates, key=lambda idx: counts[idx])
+
+    fallback = [
+        idx for idx, value in enumerate(counts) if idx not in exclude and value > 0
+    ]
+    if fallback:
+        return max(fallback, key=lambda idx: counts[idx])
+
+    return None
+
+
+def _can_reuse_cache(
+    cache_dir: Path, manifest_path: Path, spec: dict[str, object]
+) -> bool:
+    if not cache_dir.exists() or not manifest_path.exists():
+        return False
+
+    try:
+        manifest = json.loads(manifest_path.read_text(encoding="utf-8"))
+    except json.JSONDecodeError:
+        return False
+
+    if manifest.get("spec") != spec:
+        return False
+
+    entries = cast(list[dict[str, str | int]], spec["entries"])
+    for entry in entries:
+        split_name = str(entry["split"])
+        rel_path = Path(str(entry["rel_path"]))
+        destination = cache_dir / split_name / rel_path
+        if not destination.exists() or not destination.is_file():
+            return False
+
+    return True
+
+
+def _materialize_link(*, source: Path, destination: Path) -> None:
+    destination.parent.mkdir(parents=True, exist_ok=True)
+
+    try:
+        if destination.exists() or destination.is_symlink():
+            destination.unlink()
+        os.symlink(source, destination)
+        return
+    except OSError:
+        pass
+
+    try:
+        if destination.exists() or destination.is_symlink():
+            destination.unlink()
+        os.link(source, destination)
+        return
+    except OSError:
+        pass
+
+    shutil.copy2(source, destination)
diff --git a/examples/utils.py b/examples/utils.py
index 0c7ef79..554dab0 100644
--- a/examples/utils.py
+++ b/examples/utils.py
@@ -13,6 +13,14 @@ def get_example(cfg: Config) -> BaseModelHarness:
         return CIFAR_VISION(cfg=cfg)
     elif cfg.data.name == "imagenet":
         return IMAGENET_VISION(cfg=cfg)
+    elif cfg.data.name == "matey":
+        from examples.matey.model import MATEYHarness
+
+        return MATEYHarness(cfg=cfg)
+    elif cfg.data.name == "matey_outer_loop":
+        from examples.matey.model_outer_loop import MATEYHarness
+
+        return MATEYHarness(cfg=cfg)
     else:
         raise NotImplementedError(
             f"Example for dataset {cfg.data.name} is not implemented."
diff --git a/poetry.lock b/poetry.lock
index 426ce3d..1dbbba3 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -59,6 +59,7 @@ files = [
 [package.dependencies]
 idna = ">=2.8"
 sniffio = ">=1.1"
+typing_extensions = {version = ">=4.5", markers = "python_version < \"3.13\""}
 
 [package.extras]
 trio = ["trio (>=0.31.0)"]
@@ -4461,6 +4462,7 @@ files = [
 
 [package.dependencies]
 anyio = ">=3.6.2,<5"
+typing-extensions = {version = ">=4.10.0", markers = "python_version < \"3.13\""}
 
 [package.extras]
 full = ["httpx (>=0.27.0,<0.29.0)", "itsdangerous", "jinja2", "python-multipart (>=0.0.18)", "pyyaml"]
@@ -5532,5 +5534,5 @@ type = ["pytest-mypy"]
 
 [metadata]
 lock-version = "2.1"
-python-versions = ">=3.13,<3.15"
-content-hash = "65839152586ffc3d0b9e954bbb1285f24ba4d4f05efe473ac84e15939f914b16"
+python-versions = ">=3.12,<3.15"
+content-hash = "0dd3a145479f5bd8bd4e73ec292a9f15c82b385b8ec4ada30996b437658252cb"
diff --git a/pyproject.toml b/pyproject.toml
index fa99747..4a8573b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -6,7 +6,7 @@ authors = [
     {name = "Your Name",email = "you@example.com"}
 ]
 readme = "README.md"
-requires-python = ">=3.13,<3.15"
+requires-python = ">=3.12,<3.15"
 dependencies = [
     "torch (>=2.0)",
     "numpy (>=2.3.4,<3.0.0)",
@@ -23,6 +23,11 @@ dependencies = [
     "matplotlib (>=3.10.7,<4.0.0)"
 ]
 
+[project.optional-dependencies]
+matey = [
+    "matey @ git+ssh://git@github.com/FusionFM/MATEY.git@4e615bb5c86024632e386153bfbed028b38a8262",
+]
+
 [dependency-groups]
 dev = [
     "mypy (>=1.18.2,<2.0.0)",
@@ -43,6 +48,7 @@ packages = [
 ]
 
 [tool.ruff]
+extend-exclude = ["examples/matey/MATEY"]
 
 [tool.ruff.lint]
 # Add the codes you want to ignore to the 'ignore' list.
diff --git a/src/driver/continuous_monitor.py b/src/driver/continuous_monitor.py
index 299578c..c637fcb 100644
--- a/src/driver/continuous_monitor.py
+++ b/src/driver/continuous_monitor.py
@@ -84,6 +84,8 @@ def __init__(
         # State tracking
         self.stream_update_count = 0
         self.batch_count = 0
+        self.drift_check_count = 0
+        self.drift_detect_count = 0
         self.drift_event_count = 0
 
         # Metrics accumulation
@@ -121,6 +123,16 @@ def run(self) -> None:
         self.logger.info("==== Continuous Monitoring Complete ====", level=0)
         self.logger.info(f"\tTotal batches processed: {self.batch_count}", level=1)
         self.logger.info(f"\tTotal stream updates: {self.stream_update_count}", level=1)
+        self.logger.info(f"\tTotal drift checks: {self.drift_check_count}", level=1)
+        self.logger.info(
+            f"\tTotal drift detections: {self.drift_detect_count}", level=1
+        )
+        self.logger.info(f"\tTotal CL dispatches: {self.drift_event_count}", level=1)
+        if self.drift_event_count == 0:
+            self.logger.info(
+                "CL dispatch did not run because no drift was detected.",
+                level=0,
+            )
 
     def _process_stream(self) -> None:
         """Process batches from current data stream.
@@ -247,6 +259,19 @@ def _check_drift(self) -> DriftSignal:
         else:
             drift_signal = self.detector.update(agg_metric)
 
+        self.drift_check_count += 1
+        if drift_signal.drift_detected:
+            self.drift_detect_count += 1
+        self.logger.info(
+            (
+                f"\tDrift check #{self.drift_check_count}: "
+                f"metric_{self.metric_idx}={agg_metric:.6f}, "
+                f"detected={drift_signal.drift_detected}, "
+                f"score={drift_signal.drift_score:.4f}"
+            ),
+            level=1,
+        )
+
         # Log drift metrics
         self._log_metrics(drift_signal, agg_metric)
 
diff --git a/tests/test_continuous_monitor.py b/tests/test_continuous_monitor.py
index d84aaaa..0517bce 100644
--- a/tests/test_continuous_monitor.py
+++ b/tests/test_continuous_monitor.py
@@ -26,6 +26,8 @@ def test_initialization(self, default_cfg, dummy_harness):
         mon = ContinuousMonitor(cfg=default_cfg, modelHarness=dummy_harness)
         assert mon.stream_update_count == 0
         assert mon.batch_count == 0
+        assert mon.drift_check_count == 0
+        assert mon.drift_detect_count == 0
         assert mon.drift_event_count == 0
         assert mon.metric_buffer == []
         assert mon.detection_interval == default_cfg.drift_detection.detection_interval
@@ -78,6 +80,11 @@ def test_mean_aggregation(self, default_cfg, dummy_harness):
         agg_value = mock_update.call_args[0][0]
         assert agg_value == pytest.approx(73.33, abs=0.01)
         assert mon.metric_buffer == []
+        assert mon.drift_check_count == 1
+        assert mon.drift_detect_count == 0
+        assert any(
+            "Drift check #1" in call.args[0] for call in mon.logger.info.call_args_list
+        )
         assert signal is mock_signal
 
     def test_last_aggregation(self, default_cfg, dummy_harness):
@@ -99,6 +106,8 @@ def test_last_aggregation(self, default_cfg, dummy_harness):
         agg_value = mock_update.call_args[0][0]
         assert agg_value == 60.0
         assert mon.metric_buffer == []
+        assert mon.drift_check_count == 1
+        assert mon.drift_detect_count == 0
         assert signal is mock_signal
 
     def test_median_aggregation(self, default_cfg, dummy_harness):
@@ -120,8 +129,34 @@ def test_median_aggregation(self, default_cfg, dummy_harness):
         agg_value = mock_update.call_args[0][0]
         assert agg_value == 70.0
         assert mon.metric_buffer == []
+        assert mon.drift_check_count == 1
+        assert mon.drift_detect_count == 0
         assert signal is mock_signal
 
+    def test_drift_detection_counter_increments(self, default_cfg, dummy_harness):
+        mon = ContinuousMonitor(cfg=default_cfg, modelHarness=dummy_harness)
+        mon.metric_buffer = [[0.1], [0.2]]
+        mock_signal = DriftSignal(
+            regime=LearningRegime.CONTINUAL_LEARNING,
+            drift_detected=True,
+            drift_score=0.5,
+        )
+        with patch.object(mon.detector, "update", return_value=mock_signal):
+            _ = mon._check_drift()
+        assert mon.drift_check_count == 1
+        assert mon.drift_detect_count == 1
+
+
+class TestRunSummary:
+    def test_logs_no_drift_summary_message(self, default_cfg, dummy_harness):
+        mon = ContinuousMonitor(cfg=default_cfg, modelHarness=dummy_harness)
+        with patch.object(mon, "_should_stop", side_effect=[True]):
+            mon.run()
+        mon.logger.info.assert_any_call(
+            "CL dispatch did not run because no drift was detected.",
+            level=0,
+        )
+
 
 class TestHandleDrift:
     def test_increments_drift_event_count(self, default_cfg, dummy_harness):
diff --git a/tests/test_matey_pretrained_loading.py b/tests/test_matey_pretrained_loading.py
new file mode 100644
index 0000000..4c39f45
--- /dev/null
+++ b/tests/test_matey_pretrained_loading.py
@@ -0,0 +1,94 @@
+from __future__ import annotations
+
+from pathlib import Path
+import sys
+
+import pytest
+import torch
+
+
+def _import_harness():
+    project_root = Path(__file__).resolve().parents[1]
+    if str(project_root) not in sys.path:
+        sys.path.append(str(project_root))
+
+    from examples.matey.model import MATEYHarness
+
+    return MATEYHarness
+
+
+MATEYHarness = _import_harness()
+
+
+class _TinyModel(torch.nn.Module):
+    def __init__(self) -> None:
+        super().__init__()
+        self.linear = torch.nn.Linear(4, 2)
+
+
+def _clone_state_dict(model: torch.nn.Module) -> dict[str, torch.Tensor]:
+    return {k: v.detach().clone() for k, v in model.state_dict().items()}
+
+
+def _assert_same_params(
+    model: torch.nn.Module, expected: dict[str, torch.Tensor]
+) -> None:
+    current = model.state_dict()
+    assert set(current.keys()) == set(expected.keys())
+    for key in current:
+        assert torch.allclose(current[key], expected[key])
+
+
+def test_load_pretrained_from_raw_state_dict(tmp_path: Path) -> None:
+    source = _TinyModel()
+    expected = _clone_state_dict(source)
+    ckpt = tmp_path / "raw_state.pt"
+    torch.save(expected, ckpt)
+
+    target = _TinyModel()
+    MATEYHarness._load_pretrained_weights_if_available(target, str(ckpt))
+
+    _assert_same_params(target, expected)
+
+
+def test_load_pretrained_from_model_state_key(tmp_path: Path) -> None:
+    source = _TinyModel()
+    expected = _clone_state_dict(source)
+    ckpt = tmp_path / "wrapped_state.pt"
+    torch.save({"model_state": expected, "epoch": 3}, ckpt)
+
+    target = _TinyModel()
+    MATEYHarness._load_pretrained_weights_if_available(target, str(ckpt))
+
+    _assert_same_params(target, expected)
+
+
+def test_load_pretrained_strips_module_prefix(tmp_path: Path) -> None:
+    source = _TinyModel()
+    expected = _clone_state_dict(source)
+    prefixed = {f"module.{k}": v for k, v in expected.items()}
+
+    ckpt = tmp_path / "module_prefixed.pt"
+    torch.save({"model_state": prefixed}, ckpt)
+
+    target = _TinyModel()
+    MATEYHarness._load_pretrained_weights_if_available(target, str(ckpt))
+
+    _assert_same_params(target, expected)
+
+
+def test_missing_pretrained_path_raises(tmp_path: Path) -> None:
+    target = _TinyModel()
+    missing = tmp_path / "missing.pt"
+
+    with pytest.raises(FileNotFoundError, match="pretrained checkpoint not found"):
+        MATEYHarness._load_pretrained_weights_if_available(target, str(missing))
+
+
+def test_unsupported_checkpoint_format_raises(tmp_path: Path) -> None:
+    target = _TinyModel()
+    bad_ckpt = tmp_path / "bad.pt"
+    torch.save({"epoch": 1, "optimizer": {}}, bad_ckpt)
+
+    with pytest.raises(ValueError, match="Unsupported MATEY checkpoint format"):
+        MATEYHarness._load_pretrained_weights_if_available(target, str(bad_ckpt))
diff --git a/tests/test_matey_runsh_harness.py b/tests/test_matey_runsh_harness.py
new file mode 100644
index 0000000..f61a3b1
--- /dev/null
+++ b/tests/test_matey_runsh_harness.py
@@ -0,0 +1,466 @@
+from __future__ import annotations
+
+import copy
+import sys
+from pathlib import Path
+from typing import Any
+
+import pytest
+import torch
+
+from config.configuration import (
+    Config,
+    ContinualLearningCfg,
+    DataCfg,
+    DriftDetectionCfg,
+    ModelCfg,
+    TrainCfg,
+)
+
+
+def _import_matey_symbols():
+    project_root = Path(__file__).resolve().parents[1]
+    if str(project_root) not in sys.path:
+        sys.path.append(str(project_root))
+
+    from examples.matey.matey_batches import MateyInputBatch, MateyTargetBatch
+    from examples.matey.model import MATEYHarness
+
+    return MATEYHarness, MateyInputBatch, MateyTargetBatch
+
+
+def _make_cfg(
+    data_path: str,
+    update_mode: str = "base",
+    num_workers: int = 1,
+) -> Config:
+    return Config(
+        model=ModelCfg(name="matey_vit", pretrained_path=""),
+        data=DataCfg(name="matey", path=data_path),
+        train=TrainCfg(
+            batch_size=2, num_workers=num_workers, init_lr=0.001, max_iter=2
+        ),
+        continual_learning=ContinualLearningCfg(update_mode=update_mode),
+        drift_detection=DriftDetectionCfg(detection_interval=1, max_stream_updates=1),
+        seed=7,
+        device="cpu",
+        multi_gpu=False,
+    )
+
+
+def _make_fake_matey_root(tmp_path: Path) -> Path:
+    matey_root = tmp_path / "MATEY"
+    matey_root.mkdir(parents=True)
+    return matey_root
+
+
+def _make_fake_modules(
+    *,
+    solps_root: Path | None = None,
+    train_data_paths: list[list[Any]] | None = None,
+    valid_data_paths: list[list[Any]] | None = None,
+    loader_calls: list[dict[str, Any]] | None = None,
+    graph_batches: bool = False,
+):
+    class DummyYParams:
+        def __init__(self, yaml_filename: str, config_name: str):
+            self.yaml_filename = yaml_filename
+            self.config_name = config_name
+            self.model_type = "vit_all2all"
+            self.optimizer = "AdamW"
+            self.weight_decay = 0.0
+            self.learning_rate = 0.001
+            self.embedding_offset = 0
+            self.autoregressive = False
+            self.compile = False
+
+            if train_data_paths is not None and valid_data_paths is not None:
+                self.train_data_paths = copy.deepcopy(train_data_paths)
+                self.valid_data_paths = copy.deepcopy(valid_data_paths)
+            elif solps_root is None:
+                self.train_data_paths = [["unused", "incompNS", "", "tk-2D"]]
+                self.valid_data_paths = [["unused", "incompNS", "", "tk-2D"]]
+            else:
+                self.train_data_paths = [
+                    [str(solps_root / "train"), "SOLPS2D", "", "tk-2D"]
+                ]
+                self.valid_data_paths = [
+                    [str(solps_root / "valid"), "SOLPS2D", "", "tk-2D"]
+                ]
+
+    class DummyForwardOptionsBase:
+        def __init__(self, **kwargs: Any):
+            self.__dict__.update(kwargs)
+
+    class DummySubDataset:
+        tkhead_name = "tk-2D"
+        type = "dummy"
+        blockdict = None
+
+    class DummyMixedDataset:
+        sub_dsets = [DummySubDataset()]
+
+    class DummyRawLoader:
+        def __init__(self, batch: dict[str, Any]):
+            self._batch = batch
+
+        def __len__(self) -> int:
+            return 1
+
+        def __iter__(self):
+            yield self._batch
+
+    class DummyMateyCore(torch.nn.Module):
+        def __init__(self) -> None:
+            super().__init__()
+            self._weight = torch.nn.Parameter(torch.tensor(1.0))
+
+        def forward(self, inp, field_labels, bcs, opts):
+            if isinstance(inp, torch.Tensor):
+                return inp[-1].float() * self._weight
+            if hasattr(inp, "y"):
+                return inp.y.float() * self._weight
+            raise RuntimeError("Dummy core only supports tensor inputs for this test.")
+
+    def fake_rearrange(x: torch.Tensor, pattern: str) -> torch.Tensor:
+        assert pattern == "b t c d h w -> t b c d h w"
+        return x.permute(1, 0, 2, 3, 4, 5).contiguous()
+
+    def fake_get_data_loader(*args, **kwargs):
+        if loader_calls is not None:
+            params = args[0]
+            paths = args[1]
+            loader_calls.append(
+                {
+                    "split": kwargs.get("split"),
+                    "paths": copy.deepcopy(paths),
+                    "train_val_test": list(getattr(params, "train_val_test", [])),
+                }
+            )
+
+        if graph_batches:
+            class DummyGraph:
+                def __init__(self) -> None:
+                    self.y = torch.randn(2, 4, 2, 2, 2)
+                    self.leadtime = torch.ones(2, 1, dtype=torch.long)
+
+            batch = {
+                "graph": DummyGraph(),
+                "bcs": torch.zeros(2, 1),
+                "field_labels": torch.tensor([[0, 1, 2, 3], [0, 1, 2, 3]]),
+                "dset_idx": torch.tensor([0, 0], dtype=torch.long),
+            }
+            return DummyRawLoader(batch), DummyMixedDataset(), None
+
+        batch = {
+            "input": torch.randn(2, 3, 4, 2, 2, 2),
+            "label": torch.randn(2, 3, 4, 2, 2, 2),
+            "bcs": torch.zeros(2, 1),
+            "leadtime": torch.ones(2, 1, dtype=torch.long),
+            "field_labels": torch.tensor(
+                [[0, 1, 2, 3], [0, 1, 2, 3]], dtype=torch.long
+            ),
+            "dset_idx": torch.tensor([0, 0], dtype=torch.long),
+        }
+        return DummyRawLoader(batch), DummyMixedDataset(), None
+
+    return {
+        "YParams": DummyYParams,
+        "get_data_loader": fake_get_data_loader,
+        "build_avit": lambda p: DummyMateyCore(),
+        "build_svit": lambda p: DummyMateyCore(),
+        "build_vit": lambda p: DummyMateyCore(),
+        "build_turbt": lambda p: DummyMateyCore(),
+        "add_weight_decay": lambda model, wd: model.parameters(),
+        "determine_turt_levels": lambda tk_size, shape, imod: 0,
+        "ForwardOptionsBase": DummyForwardOptionsBase,
+        "autoregressive_rollout": (
+            lambda model, inp, labels, bcs, opts, pushforward=True: (
+                model(inp, labels, bcs, opts),
+                1,
+            )
+        ),
+        "rearrange": fake_rearrange,
+        "DAdaptAdam": None,
+    }
+
+
+def _write_solps_samples(solps_root: Path) -> None:
+    (solps_root / "train").mkdir(parents=True, exist_ok=True)
+    (solps_root / "valid").mkdir(parents=True, exist_ok=True)
+    for filename in (
+        solps_root / "train" / "sample-a.nc",
+        solps_root / "train" / "sample-b.nc",
+        solps_root / "valid" / "sample-c.nc",
+    ):
+        filename.write_text("stub", encoding="utf-8")
+
+
+def test_unsupported_updater_mode_raises_before_loading() -> None:
+    MATEYHarness, _, _ = _import_matey_symbols()
+    cfg = _make_cfg(data_path="MATEY", update_mode="jvp_reg")
+    with pytest.raises(NotImplementedError, match="supports only"):
+        MATEYHarness(cfg)
+
+
+def test_missing_matey_root_path_raises(tmp_path: Path) -> None:
+    MATEYHarness, _, _ = _import_matey_symbols()
+    cfg = _make_cfg(data_path=str(tmp_path / "missing_matey_root"))
+    with pytest.raises(FileNotFoundError, match="Matey data root path does not exist"):
+        MATEYHarness(cfg)
+
+
+def test_data_root_requires_train_and_valid_when_one_exists(
+    tmp_path: Path, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    MATEYHarness, _, _ = _import_matey_symbols()
+    data_root = tmp_path / "solps"
+    (data_root / "train").mkdir(parents=True)
+
+    fake_modules = _make_fake_modules()
+    monkeypatch.setattr(
+        MATEYHarness, "_load_matey_modules", lambda self: fake_modules
+    )
+
+    cfg = _make_cfg(data_path=str(data_root))
+    with pytest.raises(FileNotFoundError, match="must contain both 'train/' and 'valid/'"):
+        MATEYHarness(cfg)
+
+
+def test_zero_workers_are_supported(
+    tmp_path: Path, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    MATEYHarness, _, _ = _import_matey_symbols()
+    matey_root = _make_fake_matey_root(tmp_path)
+    fake_modules = _make_fake_modules()
+    monkeypatch.setattr(
+        MATEYHarness, "_load_matey_modules", lambda self: fake_modules
+    )
+
+    cfg = _make_cfg(data_path=str(matey_root), num_workers=0)
+    harness = MATEYHarness(cfg)
+    assert harness._params.num_data_workers == 0
+
+
+def test_harness_builds_stream_and_loss_with_mocked_matey(
+    tmp_path: Path, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    MATEYHarness, MateyInputBatch, MateyTargetBatch = _import_matey_symbols()
+    matey_root = _make_fake_matey_root(tmp_path)
+    fake_modules = _make_fake_modules()
+
+    monkeypatch.setattr(
+        MATEYHarness,
+        "_load_matey_modules",
+        lambda self: fake_modules,
+    )
+
+    cfg = _make_cfg(data_path=str(matey_root), update_mode="base")
+    harness = MATEYHarness(cfg)
+
+    harness.update_data_stream()
+    train_loader, val_loader = harness.get_cur_data_loaders()
+
+    train_batch = next(iter(train_loader))
+    assert isinstance(train_batch[0], MateyInputBatch)
+    assert isinstance(train_batch[1], MateyTargetBatch)
+    assert train_batch[1].shape[0] == 2
+
+    x, y = train_batch[0].to("cpu"), train_batch[1].to("cpu")
+    y_hat = harness.model(x)
+    loss = harness.get_criterion()(y_hat, y)
+    assert torch.isfinite(loss)
+
+    nrmse = harness.eval_metrics["nrmse"](y_hat, y)
+    rmse = harness.eval_metrics["rmse"](y_hat, y)
+    assert torch.isfinite(nrmse)
+    assert torch.isfinite(rmse)
+
+    _ = next(iter(val_loader))
+    harness.update_data_stream()
+    assert harness.task_counter == 2
+
+
+def test_default_split_applied_for_matey(
+    tmp_path: Path, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    MATEYHarness, _, _ = _import_matey_symbols()
+    monkeypatch.setattr(
+        "examples.matey.model.DEFAULT_SOLPS_CACHE_ROOT", tmp_path / "cache"
+    )
+    matey_root = _make_fake_matey_root(tmp_path)
+    solps_root = tmp_path / "solps"
+    _write_solps_samples(solps_root)
+
+    fake_modules = _make_fake_modules(solps_root=solps_root)
+    monkeypatch.setattr(
+        MATEYHarness,
+        "_load_matey_modules",
+        lambda self: fake_modules,
+    )
+
+    cfg = _make_cfg(data_path=str(matey_root))
+    harness = MATEYHarness(cfg)
+
+    assert harness._params.train_val_test == [0.7, 0.15, 0.15]
+    train_root = Path(harness._params.train_data_paths[0][0]).resolve()
+    val_root = Path(harness._params.valid_data_paths[0][0]).resolve()
+    assert train_root.name == "train"
+    assert val_root.name == "val"
+    assert train_root.parent == val_root.parent
+    train_files = [path for path in train_root.rglob("*") if path.is_file()]
+    val_files = [path for path in val_root.rglob("*") if path.is_file()]
+    assert train_files
+    assert val_files
+    staged_names = {path.name for path in (train_files + val_files)}
+    assert {"sample-a.nc", "sample-b.nc", "sample-c.nc"} <= staged_names
+
+
+def test_cfg_data_path_overrides_yaml_paths(
+    tmp_path: Path, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    MATEYHarness, _, _ = _import_matey_symbols()
+    monkeypatch.setattr(
+        "examples.matey.model.DEFAULT_SOLPS_CACHE_ROOT", tmp_path / "cache"
+    )
+    solps_root = tmp_path / "user-solps"
+    _write_solps_samples(solps_root)
+
+    fake_modules = _make_fake_modules()
+    monkeypatch.setattr(
+        MATEYHarness,
+        "_load_matey_modules",
+        lambda self: fake_modules,
+    )
+
+    cfg = _make_cfg(data_path=str(solps_root))
+    harness = MATEYHarness(cfg)
+
+    train_root = Path(harness._params.train_data_paths[0][0]).resolve()
+    val_root = Path(harness._params.valid_data_paths[0][0]).resolve()
+    assert train_root.name == "train"
+    assert val_root.name == "val"
+    assert train_root.parent == val_root.parent
+
+
+def test_train_and_val_loaders_use_staged_paths(
+    tmp_path: Path, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    MATEYHarness, _, _ = _import_matey_symbols()
+    monkeypatch.setattr(
+        "examples.matey.model.DEFAULT_SOLPS_CACHE_ROOT", tmp_path / "cache"
+    )
+    matey_root = _make_fake_matey_root(tmp_path)
+    solps_root = tmp_path / "solps"
+    _write_solps_samples(solps_root)
+
+    loader_calls: list[dict[str, Any]] = []
+    fake_modules = _make_fake_modules(
+        solps_root=solps_root,
+        loader_calls=loader_calls,
+    )
+    monkeypatch.setattr(
+        MATEYHarness,
+        "_load_matey_modules",
+        lambda self: fake_modules,
+    )
+
+    cfg = _make_cfg(data_path=str(matey_root))
+    harness = MATEYHarness(cfg)
+    harness.update_data_stream()
+
+    assert [call["split"] for call in loader_calls] == ["train", "val"]
+    assert loader_calls[0]["train_val_test"] == [1.0, 0.0, 0.0]
+    assert loader_calls[1]["train_val_test"] == [0.0, 1.0, 0.0]
+
+    train_loader_path = Path(loader_calls[0]["paths"][0][0]).resolve()
+    val_loader_path = Path(loader_calls[1]["paths"][0][0]).resolve()
+    assert train_loader_path.name == "train"
+    assert val_loader_path.name == "val"
+    assert train_loader_path.parent == val_loader_path.parent
+
+
+def test_graph_batch_is_supported(
+    tmp_path: Path, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    MATEYHarness, MateyInputBatch, MateyTargetBatch = _import_matey_symbols()
+    monkeypatch.setattr(
+        "examples.matey.model.DEFAULT_SOLPS_CACHE_ROOT", tmp_path / "cache"
+    )
+    matey_root = _make_fake_matey_root(tmp_path)
+    solps_root = tmp_path / "solps"
+    _write_solps_samples(solps_root)
+
+    fake_modules = _make_fake_modules(
+        solps_root=solps_root,
+        graph_batches=True,
+    )
+    monkeypatch.setattr(
+        MATEYHarness,
+        "_load_matey_modules",
+        lambda self: fake_modules,
+    )
+
+    cfg = _make_cfg(data_path=str(matey_root))
+    harness = MATEYHarness(cfg)
+    harness.update_data_stream()
+    train_loader, _ = harness.get_cur_data_loaders()
+
+    x, y = next(iter(train_loader))
+    assert isinstance(x, MateyInputBatch)
+    assert isinstance(y, MateyTargetBatch)
+    assert x.is_graph
+    assert y.is_graph
+
+    y_hat = harness.model(x.to("cpu"))
+    loss = harness.get_criterion()(y_hat, y.to("cpu"))
+    assert torch.isfinite(loss)
+
+
+def test_mixed_solps_and_non_solps_raises(
+    tmp_path: Path, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    MATEYHarness, _, _ = _import_matey_symbols()
+    monkeypatch.setattr(
+        "examples.matey.model.DEFAULT_SOLPS_CACHE_ROOT", tmp_path / "cache"
+    )
+    matey_root = _make_fake_matey_root(tmp_path)
+    solps_root = tmp_path / "solps"
+    _write_solps_samples(solps_root)
+
+    train_paths = [
+        [str(solps_root / "train"), "SOLPS2D", "", "tk-2D"],
+        [str(tmp_path / "other"), "incompNS", "", "tk-2D"],
+    ]
+    valid_paths = [[str(solps_root / "valid"), "SOLPS2D", "", "tk-2D"]]
+
+    fake_modules = _make_fake_modules(
+        train_data_paths=train_paths,
+        valid_data_paths=valid_paths,
+    )
+    monkeypatch.setattr(
+        MATEYHarness,
+        "_load_matey_modules",
+        lambda self: fake_modules,
+    )
+
+    cfg = _make_cfg(data_path=str(matey_root))
+    with pytest.raises(
+        ValueError, match="does not support mixing SOLPS2D with non-SOLPS datasets"
+    ):
+        MATEYHarness(cfg)
+
+
+def test_examples_factory_dispatch_for_matey(monkeypatch: pytest.MonkeyPatch) -> None:
+    _import_matey_symbols()
+    from examples.utils import get_example
+
+    class DummyHarness:
+        def __init__(self, cfg):
+            self.cfg = cfg
+
+    monkeypatch.setattr("examples.matey.model.MATEYHarness", DummyHarness)
+
+    cfg = _make_cfg(data_path="MATEY")
+    harness = get_example(cfg)
+    assert isinstance(harness, DummyHarness)
diff --git a/tests/test_matey_solps_split.py b/tests/test_matey_solps_split.py
new file mode 100644
index 0000000..c3d4585
--- /dev/null
+++ b/tests/test_matey_solps_split.py
@@ -0,0 +1,104 @@
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+import pytest
+
+
+def _load_stage_fn():
+    project_root = Path(__file__).resolve().parents[1]
+    if str(project_root) not in sys.path:
+        sys.path.append(str(project_root))
+
+    from examples.matey.src.solps_split import stage_solps_split
+
+    return stage_solps_split
+
+
+stage_solps_split = _load_stage_fn()
+
+
+def _write_file(path: Path) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    path.write_text("x", encoding="utf-8")
+
+
+def test_staged_split_counts_are_deterministic(tmp_path: Path) -> None:
+    source_root = tmp_path / "solps"
+    train_root = source_root / "train"
+    valid_root = source_root / "valid"
+
+    # 10 valid files across train/valid roots.
+    for idx in range(7):
+        _write_file(train_root / f"train_{idx}.nc")
+    for idx in range(2):
+        _write_file(valid_root / f"valid_{idx}.nc")
+    _write_file(valid_root / "holdout_sol.exo")
+
+    # Hidden artifacts should be ignored.
+    _write_file(train_root / ".cache" / "ignored.nc")
+
+    result = stage_solps_split(
+        [train_root, valid_root],
+        ratios=(0.7, 0.15, 0.15),
+        seed=7,
+        cache_root=tmp_path / "cache",
+    )
+
+    assert result.reused_cache is False
+    assert result.counts == {"train": 7, "val": 2, "test": 1}
+    assert result.train_dir.exists()
+    assert result.val_dir.exists()
+    assert result.test_dir.exists()
+
+    train_files = [path for path in result.train_dir.rglob("*") if path.is_file()]
+    val_files = [path for path in result.val_dir.rglob("*") if path.is_file()]
+    test_files = [path for path in result.test_dir.rglob("*") if path.is_file()]
+    assert len(train_files) == 7
+    assert len(val_files) == 2
+    assert len(test_files) == 1
+    assert all(
+        ".cache" not in path.as_posix()
+        for path in (train_files + val_files + test_files)
+    )
+
+
+def test_cache_reused_for_identical_inputs(tmp_path: Path) -> None:
+    source_root = tmp_path / "solps"
+    train_root = source_root / "train"
+    valid_root = source_root / "valid"
+    _write_file(train_root / "sample_1.nc")
+    _write_file(valid_root / "sample_2.nc")
+
+    first = stage_solps_split(
+        [train_root, valid_root],
+        seed=42,
+        cache_root=tmp_path / "cache",
+    )
+    second = stage_solps_split(
+        [train_root, valid_root],
+        seed=42,
+        cache_root=tmp_path / "cache",
+    )
+
+    assert first.cache_dir == second.cache_dir
+    assert first.fingerprint == second.fingerprint
+    assert first.reused_cache is False
+    assert second.reused_cache is True
+
+
+def test_missing_source_root_raises(tmp_path: Path) -> None:
+    with pytest.raises(ValueError, match="does not exist"):
+        stage_solps_split([tmp_path / "missing"], cache_root=tmp_path / "cache")
+
+
+def test_requires_enough_files_for_non_empty_train_val(tmp_path: Path) -> None:
+    root = tmp_path / "solps"
+    train_root = root / "train"
+    valid_root = root / "valid"
+    valid_root.mkdir(parents=True, exist_ok=True)
+    _write_file(train_root / "only_file.nc")
+
+    with pytest.raises(ValueError, match="at least two files"):
+        stage_solps_split([train_root, valid_root], cache_root=tmp_path / "cache")