diff --git a/README.md b/README.md
index d0d02b63..a295501f 100644
--- a/README.md
+++ b/README.md
@@ -55,19 +55,6 @@ curl -LsSf https://astral.sh/uv/install.sh | sh
 uv self update
 ```
 
-### Install STAMP in a Virtual Environment:
-
-```bash
-uv venv --python=3.12
-source .venv/bin/activate
-
-# For a GPU (CUDA) installation:
-uv pip install "git+https://github.com/KatherLab/STAMP.git[gpu]"
-
-# For a CPU-only installation:
-uv pip install "git+https://github.com/KatherLab/STAMP.git[cpu]" --torch-backend=cpu
-```
-
 ### Install STAMP from the Repository:
 
 ```bash
@@ -210,11 +197,13 @@ uv cache clean causal_conv1d
 
 # Now it should re-build the packages with the correct torch version
 
-# With uv pip install
-uv pip install "git+https://github.com/KatherLab/STAMP.git[build]"
-uv pip install "git+https://github.com/KatherLab/STAMP.git[build,gpu] --no-build-isolation"
-
 # With uv sync in the cloned repository
 uv sync --extra build
 uv sync --extra build --extra gpu
 ```
+
+## Reproducibility
+
+We use a central `Seed` utility to set seeds for PyTorch, NumPy, and Python’s `random`. This makes data loading and model initialization reproducible. Always call `Seed.set(seed)` once at startup.
+
+We do not enable [`torch.use_deterministic_algorithms()`](https://pytorch.org/docs/stable/notes/randomness.html#reproducibility) because it can cause large performance drops. Expect runs with the same seed to follow the same training trajectory, but not bit-for-bit identical low-level kernels.
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
index 4c06a0c5..05fe7c39 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -56,7 +56,7 @@ build = [
     "ninja"
 ]
 flash-attention = [
-    "flash-attn>=2.8.2",
+    "flash-attn>=2.8.3",
     ]
 conch = [
     "huggingface-hub>=0.26.2",
@@ -99,12 +99,13 @@ virchow2 = [
 ]
 cobra = [
     "stamp[flash-attention]",
-    "causal-conv1d @ git+https://github.com/KatherLab/causal-conv1d.git@b73d1ca0e0726ba6520c38d342bd411bb5850064",
-    "mamba-ssm @ git+https://github.com/KatherLab/mamba.git@d0d4192621889b26f9669ea4a8e6fe79cc84e8d9",
-    "cobra @ git+http://github.com/KatherLab/COBRA.git@c8aa71ce691e1279f2bb797f536355d6be47b6ac",
+    "causal-conv1d",
+    "mamba-ssm",
+    # "causal-conv1d @ git+https://github.com/KatherLab/causal-conv1d.git@dededae18d0258ccec833ab950d45279f1616fd1",
+    # "mamba-ssm @ git+https://github.com/KatherLab/mamba.git@3dad301098b721ee5c93d9ad16aafbbc1dc42cfd",
+    "cobra @ git+http://github.com/KatherLab/COBRA.git@73712e9ffa4d1bdecf9be9826d66094bd2b17534",
     "jinja2>=3.1.4", 
     "triton"
-    # "triton==3.2.0", # Fix triton to 3.2.0 (also makes torch==2.6.0) until this is solved: https://github.com/pytorch/pytorch/issues/153737
 ]
 prism_cpu = [
     "sacremoses==0.1.1",
@@ -179,6 +180,16 @@ conflicts = [
     ]
 ]
 
+[tool.uv.sources]
+torch = { index = "pytorch-cu128" }
+torchvision = { index = "pytorch-cu128" }
+
+[[tool.uv.index]]
+name = "pytorch-cu128"
+url = "https://download.pytorch.org/whl/cu128"
+explicit = true
+
+
 [[tool.uv.dependency-metadata]]
 name = "uni"
 version = "v0.1.0"
@@ -197,7 +208,6 @@ requires-dist = [
 
 [[tool.uv.dependency-metadata]]
 name = "flash-attn"
-version = "2.8.2"
 requires-dist = [
     "torch",
     "einops",
@@ -206,14 +216,12 @@ requires-dist = [
 
 [[tool.uv.dependency-metadata]]
 name = "mamba-ssm"
-version = "v2.2.4"
 requires-dist = [
     "setuptools",
 ]
 
 [[tool.uv.dependency-metadata]]
 name = "causal-conv1d"
-version = "v1.5.0.post8"
 requires-dist = [
     "setuptools",
 ]
diff --git a/src/stamp/__main__.py b/src/stamp/__main__.py
old mode 100755
new mode 100644
index b11089d8..362ea5cf
--- a/src/stamp/__main__.py
+++ b/src/stamp/__main__.py
@@ -13,6 +13,7 @@
     ModelParams,
     VitModelParams,
 )
+from stamp.seed import Seed
 
 STAMP_FACTORY_SETTINGS = Path(__file__).with_name("config.yaml")
 
@@ -49,6 +50,16 @@ def _run_cli(args: argparse.Namespace) -> None:
     with open(args.config_file_path, "r") as config_yaml:
         config = StampConfig.model_validate(yaml.safe_load(config_yaml))
 
+    # use default advanced config in case none is provided
+    if config.advanced_config is None:
+        config.advanced_config = AdvancedConfig(
+            model_params=ModelParams(vit=VitModelParams(), mlp=MlpModelParams())
+        )
+
+    # Set global random seed
+    if config.advanced_config.seed is not None:
+        Seed.set(config.advanced_config.seed)
+
     match args.command:
         case "init":
             raise RuntimeError("this case should be handled above")
@@ -67,7 +78,8 @@ def _run_cli(args: argparse.Namespace) -> None:
                 "using the following configuration:\n"
                 f"{yaml.dump(config.preprocessing.model_dump(mode='json'))}"
             )
-            extract_(
+            _logger.info("Starting preprocessing...")
+            summary = extract_(
                 output_dir=config.preprocessing.output_dir,
                 wsi_dir=config.preprocessing.wsi_dir,
                 wsi_list=config.preprocessing.wsi_list,
@@ -83,6 +95,11 @@ def _run_cli(args: argparse.Namespace) -> None:
                 cache_tiles_ext=config.preprocessing.cache_tiles_ext,
                 generate_hash=config.preprocessing.generate_hash,
             )
+            _logger.info("preprocessing finished.")
+            _logger.info(
+                f"Slides processed: {summary['processed']}, "
+                f"failed: {summary['failed']}, skipped: {summary['skipped']}"
+            )
 
         case "encode_slides":
             from stamp.encoding import init_slide_encoder_
@@ -95,7 +112,8 @@ def _run_cli(args: argparse.Namespace) -> None:
                 "using the following configuration:\n"
                 f"{yaml.dump(config.slide_encoding.model_dump(mode='json'))}"
             )
-            init_slide_encoder_(
+            _logger.info("Starting slide encoding...")
+            summary = init_slide_encoder_(
                 encoder=config.slide_encoding.encoder,
                 output_dir=config.slide_encoding.output_dir,
                 feat_dir=config.slide_encoding.feat_dir,
@@ -103,6 +121,11 @@ def _run_cli(args: argparse.Namespace) -> None:
                 agg_feat_dir=config.slide_encoding.agg_feat_dir,
                 generate_hash=config.slide_encoding.generate_hash,
             )
+            _logger.info("slide encoding finished.")
+            _logger.info(
+                f"Slides processed: {summary['processed']}, "
+                f"failed: {summary['failed']}, skipped: {summary['skipped']}"
+            )
 
         case "encode_patients":
             from stamp.encoding import init_patient_encoder_
@@ -115,7 +138,8 @@ def _run_cli(args: argparse.Namespace) -> None:
                 "using the following configuration:\n"
                 f"{yaml.dump(config.patient_encoding.model_dump(mode='json'))}"
             )
-            init_patient_encoder_(
+            _logger.info("Starting patient encoding...")
+            summary = init_patient_encoder_(
                 encoder=config.patient_encoding.encoder,
                 output_dir=config.patient_encoding.output_dir,
                 feat_dir=config.patient_encoding.feat_dir,
@@ -126,6 +150,11 @@ def _run_cli(args: argparse.Namespace) -> None:
                 agg_feat_dir=config.patient_encoding.agg_feat_dir,
                 generate_hash=config.patient_encoding.generate_hash,
             )
+            _logger.info("patient encoding finished.")
+            _logger.info(
+                f"Patients processed: {summary['processed']}, "
+                f"failed: {summary['failed']}, skipped: {summary['skipped']}"
+            )
 
         case "train":
             from stamp.modeling.train import train_categorical_model_
@@ -133,21 +162,17 @@ def _run_cli(args: argparse.Namespace) -> None:
             if config.training is None:
                 raise ValueError("no training configuration supplied")
 
-            # use default advanced config in case none is provided
-            if config.advanced_config is None:
-                config.advanced_config = AdvancedConfig(
-                    model_params=ModelParams(vit=VitModelParams(), mlp=MlpModelParams())
-                )
-
             _add_file_handle_(_logger, output_dir=config.training.output_dir)
             _logger.info(
                 "using the following configuration:\n"
                 f"{yaml.dump(config.training.model_dump(mode='json'))}"
             )
+            _logger.info("Starting training...")
 
             train_categorical_model_(
                 config=config.training, advanced=config.advanced_config
             )
+            _logger.info("Training finished.")
 
         case "deploy":
             from stamp.modeling.deploy import deploy_categorical_model_
@@ -160,6 +185,7 @@ def _run_cli(args: argparse.Namespace) -> None:
                 "using the following configuration:\n"
                 f"{yaml.dump(config.deployment.model_dump(mode='json'))}"
             )
+            _logger.info("Starting deployment...")
             deploy_categorical_model_(
                 output_dir=config.deployment.output_dir,
                 checkpoint_paths=config.deployment.checkpoint_paths,
@@ -172,6 +198,7 @@ def _run_cli(args: argparse.Namespace) -> None:
                 num_workers=config.deployment.num_workers,
                 accelerator=config.deployment.accelerator,
             )
+            _logger.info("Deployment finished...")
 
         case "crossval":
             from stamp.modeling.crossval import categorical_crossval_
@@ -184,17 +211,13 @@ def _run_cli(args: argparse.Namespace) -> None:
                 "using the following configuration:\n"
                 f"{yaml.dump(config.crossval.model_dump(mode='json'))}"
             )
-
-            # use default advanced config in case none is provided
-            if config.advanced_config is None:
-                config.advanced_config = AdvancedConfig(
-                    model_params=ModelParams(vit=VitModelParams(), mlp=MlpModelParams())
-                )
-
+            
+            _logger.info("Starting crossval...")
             categorical_crossval_(
                 config=config.crossval,
                 advanced=config.advanced_config,
             )
+            _logger.info("Crossval finished...")
 
         case "statistics":
             from stamp.statistics import compute_stats_
@@ -225,6 +248,7 @@ def _run_cli(args: argparse.Namespace) -> None:
                 "using the following configuration:\n"
                 f"{yaml.dump(config.heatmaps.model_dump(mode='json'))}"
             )
+            _logger.info("Starting heatmap generation...")
             heatmaps_(
                 feature_dir=config.heatmaps.feature_dir,
                 wsi_dir=config.heatmaps.wsi_dir,
@@ -237,6 +261,7 @@ def _run_cli(args: argparse.Namespace) -> None:
                 default_slide_mpp=config.heatmaps.default_slide_mpp,
                 opacity=config.heatmaps.opacity,
             )
+            _logger.info("Heatmaps finished...")
 
         case _:
             raise RuntimeError(
diff --git a/src/stamp/config.yaml b/src/stamp/config.yaml
index 23fcec79..0fa34a4d 100644
--- a/src/stamp/config.yaml
+++ b/src/stamp/config.yaml
@@ -277,6 +277,8 @@ patient_encoding:
 
 
 advanced_config:
+  # Optional random seed
+  # seed: 42
   max_epochs: 32
   patience: 16
   batch_size: 64
diff --git a/src/stamp/encoding/encoder/__init__.py b/src/stamp/encoding/encoder/__init__.py
index d9035f7a..f196c160 100644
--- a/src/stamp/encoding/encoder/__init__.py
+++ b/src/stamp/encoding/encoder/__init__.py
@@ -44,9 +44,12 @@ def encode_slides_(
         device: DeviceLikeType,
         generate_hash: bool,
         **kwargs,
-    ) -> None:
+    ) -> dict:
         """General method for encoding slide-level features. Called by init_slide_encoder_.
         Override this function if coords are required. See init_slide_encoder_ for full description"""
+        processed = 0
+        failed = 0
+        skipped = 0
         # generate the name for the folder containing the feats
         if generate_hash:
             encode_dir = f"{self.identifier}-slide-{get_processing_code_hash(Path(__file__))[:8]}"
@@ -71,12 +74,14 @@ def encode_slides_(
                 _logger.info(
                     f"skipping {str(slide_name)} because {output_path} already exists"
                 )
+                skipped += 1
                 continue
 
             try:
                 feats, coords = self._validate_and_read_features(h5_path)
             except ValueError as e:
                 tqdm.write(s=str(e))
+                failed += 1
                 continue
 
             slide_embedding = self._generate_slide_embedding(
@@ -85,6 +90,8 @@ def encode_slides_(
             self._save_features_(
                 output_path=output_path, feats=slide_embedding, feat_type="slide"
             )
+            processed += 1
+        return {"processed": processed, "failed": failed, "skipped": skipped}
 
     def encode_patients_(
         self,
@@ -96,9 +103,12 @@ def encode_patients_(
         device: DeviceLikeType,
         generate_hash: bool,
         **kwargs,
-    ) -> None:
+    ) -> dict:
         """General method for encoding patient-level features. Called by init_patient_encoder_.
         Override this function if coords are required. See init_patient_encoder_ for full description"""
+        processed = 0
+        failed = 0
+        skipped = 0
         # generate the name for the folder containing the feats
         if generate_hash:
             encode_dir = (
@@ -126,27 +136,34 @@ def encode_patients_(
                 _logger.info(
                     f"skipping {str(patient_id)} because {output_path} already exists"
                 )
+                skipped += 1
                 continue
 
             feats_list = []
-
-            for _, row in group.iterrows():
-                slide_filename = row[filename_label]
-                h5_path = os.path.join(feat_dir, slide_filename)
-                feats, _ = self._validate_and_read_features(h5_path)
-                feats_list.append(feats)
-
-            if not feats_list:
-                tqdm.write(f"No features found for patient {patient_id}, skipping.")
+            try: 
+                for _, row in group.iterrows():
+                    slide_filename = row[filename_label]
+                    h5_path = os.path.join(feat_dir, slide_filename)
+                    feats, _ = self._validate_and_read_features(h5_path)
+                    feats_list.append(feats)
+
+                if not feats_list:
+                    tqdm.write(f"No features found for patient {patient_id}, skipping.")
+                    skipped += 1
+                    continue
+
+                patient_embedding = self._generate_patient_embedding(
+                    feats_list, device, **kwargs
+                )
+                self._save_features_(
+                    output_path=output_path, feats=patient_embedding, feat_type="patient"
+                )
+                processed += 1
+            except Exception as e:
+                tqdm.write(f"Failed to process patient {patient_id}: {e}")
+                failed += 1
                 continue
 
-            patient_embedding = self._generate_patient_embedding(
-                feats_list, device, **kwargs
-            )
-            self._save_features_(
-                output_path=output_path, feats=patient_embedding, feat_type="patient"
-            )
-
     @abstractmethod
     def _generate_slide_embedding(
         self, feats: torch.Tensor, device, **kwargs
diff --git a/src/stamp/heatmaps/__init__.py b/src/stamp/heatmaps/__init__.py
index c1c52900..48337150 100644
--- a/src/stamp/heatmaps/__init__.py
+++ b/src/stamp/heatmaps/__init__.py
@@ -12,6 +12,7 @@
 from matplotlib.axes import Axes
 from matplotlib.figure import Figure
 from matplotlib.patches import Patch
+from packaging.version import Version
 from PIL import Image
 from torch import Tensor
 from torch.func import jacrev  # pyright: ignore[reportPrivateImportUsage]
@@ -22,8 +23,6 @@
 from stamp.preprocessing import supported_extensions
 from stamp.preprocessing.tiling import get_slide_mpp_
 from stamp.types import DeviceLikeType, Microns, SlideMPP, TilePixels
-from packaging.version import Version
-
 
 _logger = logging.getLogger("stamp")
 
@@ -33,25 +32,26 @@ def _gradcam_per_category(
     feats: Float[Tensor, "tile feat"],
     coords: Float[Tensor, "tile 2"],
 ) -> Float[Tensor, "tile category"]:
-    feat = -1  # feats dimension
+    feat_dim = -1
 
-    return (
+    cam = (
         (
             feats
             * jacrev(
-                lambda bags: torch.softmax(
-                    model.forward(
-                        bags=bags.unsqueeze(0),
-                        coords=coords.unsqueeze(0),
-                        mask=None,
-                    ),
-                    dim=1,
+                lambda bags: model.forward(
+                    bags=bags.unsqueeze(0),
+                    coords=coords.unsqueeze(0),
+                    mask=None,
                 ).squeeze(0)
             )(feats)
         )
-        .mean(feat)  # type: ignore
+        .mean(feat_dim)  # type: ignore
         .abs()
-    ).permute(-1, -2)
+    )
+
+    cam = torch.softmax(cam, dim=-1)
+
+    return cam.permute(-1, -2)
 
 
 def _vals_to_im(
diff --git a/src/stamp/modeling/config.py b/src/stamp/modeling/config.py
index d8239be0..ac9f116f 100644
--- a/src/stamp/modeling/config.py
+++ b/src/stamp/modeling/config.py
@@ -97,4 +97,5 @@ class AdvancedConfig(BaseModel):
         default=None,
         description='Optional: "vit" or "mlp". Defaults based on feature type.',
     )
-    model_params: ModelParams
+    model_params: ModelParams | None
+    seed: int | None = None
diff --git a/src/stamp/modeling/data.py b/src/stamp/modeling/data.py
index 33a8c4c7..a8837cc3 100755
--- a/src/stamp/modeling/data.py
+++ b/src/stamp/modeling/data.py
@@ -5,7 +5,7 @@
 from dataclasses import KW_ONLY, dataclass
 from itertools import groupby
 from pathlib import Path
-from typing import IO, BinaryIO, Generic, TextIO, TypeAlias, cast, Union
+from typing import IO, BinaryIO, Generic, TextIO, TypeAlias, Union, cast
 
 import h5py
 import numpy as np
@@ -17,6 +17,7 @@
 from torch.utils.data import DataLoader, Dataset
 
 import stamp
+from stamp.seed import Seed
 from stamp.types import (
     Bags,
     BagSize,
@@ -100,6 +101,8 @@ def tile_bag_dataloader(
                 shuffle=shuffle,
                 num_workers=num_workers,
                 collate_fn=_collate_to_tuple,
+                worker_init_fn=Seed.get_loader_worker_init(),
+                generator=Seed.get_torch_generator(),
             ),
         ),
         list(categories),
diff --git a/src/stamp/preprocessing/__init__.py b/src/stamp/preprocessing/__init__.py
old mode 100755
new mode 100644
index 23a8e0c3..2c8be0a3
--- a/src/stamp/preprocessing/__init__.py
+++ b/src/stamp/preprocessing/__init__.py
@@ -131,7 +131,7 @@ def extract_(
     brightness_cutoff: int | None,
     canny_cutoff: float | None,
     generate_hash: bool,
-) -> None:
+) -> dict[str, int]:
     """
     Extracts features from slides.
     Build in a fail-safe way, i.e. slides for which feature extraction triggers an exception
@@ -142,6 +142,9 @@ def extract_(
             If not `None`, ignore the slide metadata MPP, instead replacing it with this value.
             Useful for slides without metadata.
     """
+    processed = 0
+    skipped = 0
+    failed = 0
     match extractor:
         case ExtractorName.CTRANSPATH:
             from stamp.preprocessing.extractor.ctranspath import ctranspath
@@ -274,6 +277,7 @@ def extract_(
             _logger.debug(
                 f"skipping {slide_path} because {feature_output_path} already exists"
             )
+            skipped += 1
             continue
 
         feature_output_path.parent.mkdir(parents=True, exist_ok=True)
@@ -306,13 +310,16 @@ def extract_(
                 "failed to extract MPP from slide. "
                 "You can try manually setting it by adding `preprocessing.default_slide_mpp = <MPP>` "
             )
+            failed += 1
             continue
         except Exception:
             _logger.exception(f"error while extracting features from {slide_path}")
+            failed += 1
             continue
 
         if len(feats) == 0:
             _logger.info(f"no tiles found in {slide_path}, skipping")
+            skipped += 1
             continue
 
         coords = torch.stack([torch.concat(xs_um), torch.concat(ys_um)], dim=1).numpy()
@@ -341,6 +348,7 @@ def extract_(
 
             Path(tmp_h5_file.name).rename(feature_output_path)
             _logger.debug(f"saved features to {feature_output_path}")
+            processed += 1
 
         # Save rejection thumbnail
         thumbnail_path = feat_output_dir / slide_path.relative_to(wsi_dir).with_suffix(
@@ -355,6 +363,8 @@ def extract_(
             default_slide_mpp=default_slide_mpp,
         ).convert("RGB").save(thumbnail_path)
 
+    return {"processed": processed, "failed": failed, "skipped": skipped}
+
 
 def _get_rejection_thumb(
     slide: openslide.AbstractSlide,
diff --git a/src/stamp/seed.py b/src/stamp/seed.py
new file mode 100644
index 00000000..41374bd9
--- /dev/null
+++ b/src/stamp/seed.py
@@ -0,0 +1,60 @@
+import random
+from typing import Callable, ClassVar
+
+import numpy as np
+import torch
+from torch import Generator
+
+
+def _seed_worker(worker_id: int) -> None:
+    worker_seed = torch.initial_seed() % 2**32
+    np.random.seed(worker_seed)
+    random.seed(worker_seed)
+
+
+class Seed:
+    seed: ClassVar[int | None] = None
+
+    @classmethod
+    def torch(cls, seed: int) -> None:
+        torch.manual_seed(seed)
+        torch.cuda.manual_seed_all(seed)
+
+    @classmethod
+    def python(cls, seed: int) -> None:
+        random.seed(seed)
+
+    @classmethod
+    def numpy(cls, seed: int) -> None:
+        np.random.seed(seed)
+
+    @classmethod
+    def set(cls, seed: int) -> None:
+        cls.torch(seed)
+        cls.python(seed)
+        cls.numpy(seed)
+        cls.seed = seed
+
+    @classmethod
+    def _is_set(cls) -> bool:
+        return cls.seed is not None
+
+    @classmethod
+    def get_loader_worker_init(cls) -> Callable[[int], None]:
+        if cls._is_set():
+            return _seed_worker
+        else:
+            raise RuntimeError(
+                "Seed has not been set. Call Seed.set(seed) before using a DataLoader."
+            )
+
+    @classmethod
+    def get_torch_generator(cls, device="cpu") -> Generator:
+        seed = cls.seed
+        if seed is None:
+            raise RuntimeError(
+                "Seed has not been set. Call Seed.set(seed) before requesting a generator."
+            )
+        g = torch.Generator(device)
+        g.manual_seed(seed)
+        return g
diff --git a/tests/random_data.py b/tests/random_data.py
index c2c3e046..63180999 100644
--- a/tests/random_data.py
+++ b/tests/random_data.py
@@ -23,13 +23,6 @@
 FeatureDir: TypeAlias = Path
 
 
-def seed_rng(seed: int) -> None:
-    """Seeds all the random number generators"""
-    random.seed(seed)
-    torch.manual_seed(seed)
-    np.random.seed(seed)
-
-
 def create_random_dataset(
     *,
     dir: Path,
diff --git a/tests/test_crossval.py b/tests/test_crossval.py
index 342e39fc..ebafd281 100644
--- a/tests/test_crossval.py
+++ b/tests/test_crossval.py
@@ -1,8 +1,6 @@
 import os
-import random
 from pathlib import Path
 
-import numpy as np
 import pytest
 import torch
 from random_data import create_random_dataset, create_random_patient_level_dataset
@@ -15,6 +13,7 @@
     VitModelParams,
 )
 from stamp.modeling.crossval import categorical_crossval_
+from stamp.seed import Seed
 
 
 @pytest.mark.slow
@@ -32,9 +31,7 @@ def test_crossval_integration(
     use_alibi: bool = False,
     use_vary_precision_transform: bool = False,
 ) -> None:
-    random.seed(0)
-    torch.manual_seed(0)
-    np.random.seed(0)
+    Seed.set(42)
 
     if feature_type == "tile":
         clini_path, slide_path, feature_dir, categories = create_random_dataset(
diff --git a/tests/test_data.py b/tests/test_data.py
index 5ded4f01..74d1b16e 100644
--- a/tests/test_data.py
+++ b/tests/test_data.py
@@ -6,11 +6,11 @@
 import pytest
 import torch
 from random_data import (
+    create_good_and_bad_slide_tables,
     create_random_patient_level_feature_file,
+    create_random_slide_tables,
     make_feature_file,
     make_old_feature_file,
-    create_good_and_bad_slide_tables,
-    create_random_slide_tables,
 )
 from torch.utils.data import DataLoader
 
@@ -23,6 +23,7 @@
     get_coords,
     slide_to_patient_from_slide_table_,
 )
+from stamp.seed import Seed
 from stamp.types import (
     BagSize,
     FeaturePath,
@@ -94,6 +95,7 @@ def test_bag_dataset(
     dim_feats: int = 34,
     batch_size: int = 2,
 ) -> None:
+    Seed.set(1234)
     ds = BagDataset(
         bags=[
             [
@@ -126,7 +128,14 @@ def test_bag_dataset(
     assert item_bag_size <= bag_size
 
     # Test batching
-    dl = DataLoader(ds, batch_size=batch_size, shuffle=False)
+    dl = DataLoader(
+        ds,
+        batch_size=batch_size,
+        shuffle=False,
+        num_workers=1,
+        worker_init_fn=Seed.get_loader_worker_init() if Seed._is_set() else None,
+        generator=Seed.get_torch_generator() if Seed._is_set() else None,
+    )
     bag, coords, bag_sizes, _ = next(iter(dl))
     assert bag.shape == (batch_size, bag_size, dim_feats)
     assert coords.shape == (batch_size, bag_size, 2)
@@ -136,6 +145,7 @@ def test_bag_dataset(
 def test_patient_feature_dataset(
     tmp_path: Path, dim_feats: int = 16, batch_size: int = 2
 ) -> None:
+    Seed.set(1234)
     # Create 3 random patient-level feature files on disk
     files = [
         create_random_patient_level_feature_file(tmp_path=tmp_path, feat_dim=dim_feats)
@@ -153,7 +163,15 @@ def test_patient_feature_dataset(
     assert torch.allclose(label, labels[0])
 
     # Test batching
-    dl = DataLoader(ds, batch_size=batch_size, shuffle=False)
+    dl = DataLoader(
+        ds,
+        batch_size=batch_size,
+        shuffle=False,
+        num_workers=1,
+        worker_init_fn=Seed.get_loader_worker_init() if Seed._is_set() else None,
+        generator=Seed.get_torch_generator() if Seed._is_set() else None,
+    )
+
     feats_batch, labels_batch = next(iter(dl))
     assert feats_batch.shape == (batch_size, dim_feats)
     assert labels_batch.shape == (batch_size, 4)
@@ -309,3 +327,42 @@ def test_slide_table_h5_validation_random(
             patient_label="PATIENT",
             filename_label="FILENAME",
         )
+
+
+def test_two_dataloaders_diverge():
+    # Global seed so dataset construction is deterministic
+    Seed.set(1234)
+
+    ds = BagDataset(
+        bags=[
+            [make_feature_file(feats=torch.rand((12, 8)), coords=torch.rand(12, 2))],
+            [make_feature_file(feats=torch.rand((12, 8)), coords=torch.rand(12, 2))],
+        ],
+        bag_size=BagSize(5),  # triggers per-sample random subsampling
+        ground_truths=torch.rand(2, 3) > 0.5,
+        transform=None,
+    )
+
+    base = 98765
+    g2 = torch.Generator().manual_seed(base)  # loader 1 generator
+
+    # Loader A: no workers ⇒ __getitem__ randomness runs in main process
+    dl1 = DataLoader(ds, batch_size=1, shuffle=True, num_workers=0)
+
+    # Loader B: 1 worker ⇒ worker RNG is derived from DataLoader's base_seed
+    dl2 = DataLoader(
+        ds,
+        batch_size=1,
+        shuffle=True,
+        num_workers=1,
+        worker_init_fn=Seed.get_loader_worker_init(),
+        generator=g2,
+    )
+
+    # With same seed, shuffle order tends to match, but per-sample RNG differs
+    diffs = 0
+    for (f1, *_), (f2, *_) in zip(dl1, dl2):
+        if not torch.equal(f1, f2):
+            diffs += 1
+
+    assert diffs >= 1, "Expected at least one batch to differ despite same seed"
diff --git a/tests/test_deployment.py b/tests/test_deployment.py
index 033924d4..41b859bd 100644
--- a/tests/test_deployment.py
+++ b/tests/test_deployment.py
@@ -13,6 +13,7 @@
 from stamp.modeling.deploy import _predict, _to_prediction_df
 from stamp.modeling.lightning_model import LitVisionTransformer
 from stamp.modeling.mlp_classifier import LitMLPClassifier
+from stamp.seed import Seed
 from stamp.types import GroundTruth, PatientId
 
 
@@ -25,6 +26,7 @@ def test_predict(
     n_heads: int = 7,
     dim_input: int = 12,
 ) -> None:
+    Seed.set(42)
     model = LitVisionTransformer(
         categories=list(categories),
         category_weights=torch.rand(len(categories)),
diff --git a/tests/test_deployment_backward_compatibility.py b/tests/test_deployment_backward_compatibility.py
index b62a0923..bfaf8095 100644
--- a/tests/test_deployment_backward_compatibility.py
+++ b/tests/test_deployment_backward_compatibility.py
@@ -5,6 +5,7 @@
 from stamp.modeling.data import PatientData, tile_bag_dataloader
 from stamp.modeling.deploy import _predict
 from stamp.modeling.lightning_model import LitVisionTransformer
+from stamp.seed import Seed
 from stamp.types import FeaturePath, PatientId
 
 
@@ -12,6 +13,7 @@
     "ignore:The 'predict_dataloader' does not have many workers"
 )
 def test_backwards_compatibility() -> None:
+    Seed.set(42)
     example_checkpoint_path = download_file(
         url="https://github.com/KatherLab/STAMP/releases/download/2.2.0/example-model-v2_3_0.ckpt",
         file_name="example-modelv2_3_0.ckpt",
diff --git a/tests/test_train_deploy.py b/tests/test_train_deploy.py
index 03d48c48..0b58cd10 100644
--- a/tests/test_train_deploy.py
+++ b/tests/test_train_deploy.py
@@ -16,6 +16,7 @@
 )
 from stamp.modeling.deploy import deploy_categorical_model_
 from stamp.modeling.train import train_categorical_model_
+from stamp.seed import Seed
 
 
 @pytest.mark.slow
@@ -35,9 +36,7 @@ def test_train_deploy_integration(
     use_alibi: bool,
     use_vary_precision_transform: bool,
 ) -> None:
-    random.seed(0)
-    torch.manual_seed(0)
-    np.random.seed(0)
+    Seed.set(42)
 
     (tmp_path / "train").mkdir()
     (tmp_path / "deploy").mkdir()
diff --git a/uv.lock b/uv.lock
index 7f1fe21c..738f2a7a 100644
--- a/uv.lock
+++ b/uv.lock
@@ -18,17 +18,14 @@ conflicts = [[
 
 [[manifest.dependency-metadata]]
 name = "causal-conv1d"
-version = "1.5.0.post8"
 requires-dist = ["setuptools"]
 
 [[manifest.dependency-metadata]]
 name = "flash-attn"
-version = "2.8.2"
 requires-dist = ["torch", "einops"]
 
 [[manifest.dependency-metadata]]
 name = "mamba-ssm"
-version = "2.2.4"
 requires-dist = ["setuptools"]
 
 [[manifest.dependency-metadata]]
@@ -243,11 +240,12 @@ wheels = [
 
 [[package]]
 name = "causal-conv1d"
-version = "1.5.0.post8"
-source = { git = "https://github.com/KatherLab/causal-conv1d.git?rev=b73d1ca0e0726ba6520c38d342bd411bb5850064#b73d1ca0e0726ba6520c38d342bd411bb5850064" }
+version = "1.5.2"
+source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "setuptools" },
 ]
+sdist = { url = "https://files.pythonhosted.org/packages/03/e5/2d2b2e067234c0022ff491ff8e574ca0c67094b2deb61249a2be21789cbb/causal_conv1d-1.5.2.tar.gz", hash = "sha256:9b7d8ec8d07e3590a1dfa010e4e87d1442635c3f96d665a3c1ce3025d8cc4b84", size = 23883, upload-time = "2025-07-18T22:15:36.988Z" }
 
 [[package]]
 name = "certifi"
@@ -366,7 +364,7 @@ wheels = [
 [[package]]
 name = "cobra"
 version = "0.1.0"
-source = { git = "http://github.com/KatherLab/COBRA.git?rev=c8aa71ce691e1279f2bb797f536355d6be47b6ac#c8aa71ce691e1279f2bb797f536355d6be47b6ac" }
+source = { git = "http://github.com/KatherLab/COBRA.git?rev=73712e9ffa4d1bdecf9be9826d66094bd2b17534#73712e9ffa4d1bdecf9be9826d66094bd2b17534" }
 dependencies = [
     { name = "causal-conv1d" },
     { name = "einops" },
@@ -767,13 +765,13 @@ wheels = [
 
 [[package]]
 name = "flash-attn"
-version = "2.8.2"
+version = "2.8.3"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "einops" },
     { name = "torch" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/63/91/91bcbd7424877fa8da4e7cc04b8a777247a33037b9c1fd976a5ad426c047/flash_attn-2.8.2.tar.gz", hash = "sha256:740a5370f406cbe16155cc6d078ec543a97a137501f32cba89198295e6b80e54", size = 8167111, upload-time = "2025-07-24T16:06:45.953Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/3b/b2/8d76c41ad7974ee264754709c22963447f7f8134613fd9ce80984ed0dab7/flash_attn-2.8.3.tar.gz", hash = "sha256:1e71dd64a9e0280e0447b8a0c2541bad4bf6ac65bdeaa2f90e51a9e57de0370d", size = 8447812, upload-time = "2025-08-15T08:28:12.911Z" }
 
 [[package]]
 name = "fonttools"
@@ -1540,11 +1538,12 @@ dependencies = [
 
 [[package]]
 name = "mamba-ssm"
-version = "2.2.4"
-source = { git = "https://github.com/KatherLab/mamba.git?rev=d0d4192621889b26f9669ea4a8e6fe79cc84e8d9#d0d4192621889b26f9669ea4a8e6fe79cc84e8d9" }
+version = "2.2.5"
+source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "setuptools" },
 ]
+sdist = { url = "https://files.pythonhosted.org/packages/ba/2d/fbd909f6e6d48c491a9ed7ae68e8a890d8409aba4a6356741e2a9c6adad5/mamba_ssm-2.2.5.tar.gz", hash = "sha256:5055c8e631a22bb3cfc9958828a7a57ea14deef2ab2a98dcf425a27c87dcf6a7", size = 113753, upload-time = "2025-07-19T06:16:06.875Z" }
 
 [[package]]
 name = "markdown-it-py"
@@ -1981,168 +1980,154 @@ wheels = [
 
 [[package]]
 name = "nvidia-cublas-cu12"
-version = "12.6.4.1"
+version = "12.8.4.1"
 source = { registry = "https://pypi.org/simple" }
 wheels = [
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-    { url = "https://files.pythonhosted.org/packages/97/0d/f1f0cadbf69d5b9ef2e4f744c9466cb0a850741d08350736dfdb4aa89569/nvidia_cublas_cu12-12.6.4.1-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:235f728d6e2a409eddf1df58d5b0921cf80cfa9e72b9f2775ccb7b4a87984668", size = 390794615, upload-time = "2024-11-20T17:39:52.715Z" },
-    { url = "https://files.pythonhosted.org/packages/84/f7/985e9bdbe3e0ac9298fcc8cfa51a392862a46a0ffaccbbd56939b62a9c83/nvidia_cublas_cu12-12.6.4.1-py3-none-win_amd64.whl", hash = "sha256:9e4fa264f4d8a4eb0cdbd34beadc029f453b3bafae02401e999cf3d5a5af75f8", size = 434535301, upload-time = "2024-11-20T17:50:41.681Z" },
+    { url = "https://files.pythonhosted.org/packages/29/99/db44d685f0e257ff0e213ade1964fc459b4a690a73293220e98feb3307cf/nvidia_cublas_cu12-12.8.4.1-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:b86f6dd8935884615a0683b663891d43781b819ac4f2ba2b0c9604676af346d0", size = 590537124, upload-time = "2025-03-07T01:43:53.556Z" },
+    { url = "https://files.pythonhosted.org/packages/dc/61/e24b560ab2e2eaeb3c839129175fb330dfcfc29e5203196e5541a4c44682/nvidia_cublas_cu12-12.8.4.1-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:8ac4e771d5a348c551b2a426eda6193c19aa630236b418086020df5ba9667142", size = 594346921, upload-time = "2025-03-07T01:44:31.254Z" },
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