diff --git a/tests/unit/utilities/test_typed_module_list.py b/tests/unit/utilities/test_typed_module_list.py
new file mode 100644
index 000000000..64a95f4f6
--- /dev/null
+++ b/tests/unit/utilities/test_typed_module_list.py
@@ -0,0 +1,105 @@
+"""Unit tests for TypedModuleList.
+
+TypedModuleList is a generic, type-preserving wrapper around nn.ModuleList. These
+tests pin down its runtime behaviour: it must stay a drop-in nn.ModuleList (same
+iteration, indexing, slicing, and submodule registration). The static typing
+guarantee (iteration/indexing yield the element type) is exercised separately by
+mypy over the rest of the package.
+"""
+
+import pytest
+import torch.nn as nn
+
+from transformer_lens.utilities import TypedModuleList
+from transformer_lens.utilities.typed_module_list import (
+    TypedModuleList as TypedModuleListDirect,
+)
+
+
+def _linears(n: int) -> list[nn.Linear]:
+    return [nn.Linear(3, 3) for _ in range(n)]
+
+
+class TestTypedModuleList:
+    """Runtime behaviour of TypedModuleList."""
+
+    def test_exported_from_utilities_package(self):
+        """It is re-exported from transformer_lens.utilities."""
+        assert TypedModuleList is TypedModuleListDirect
+
+    def test_is_an_nn_module_list(self):
+        """It must remain a genuine nn.ModuleList subclass."""
+        assert isinstance(TypedModuleList(_linears(2)), nn.ModuleList)
+
+    @pytest.mark.parametrize("n", [0, 1, 3])
+    def test_construction_and_len(self, n: int):
+        """len() matches the number of modules passed in (including empty)."""
+        assert len(TypedModuleList(_linears(n))) == n
+
+    def test_construction_with_no_arguments(self):
+        """It can be constructed empty, like nn.ModuleList()."""
+        empty = TypedModuleList()
+        assert len(empty) == 0
+        assert list(empty) == []
+
+    def test_iteration_preserves_order_and_identity(self):
+        """Iterating yields the exact module objects, in order."""
+        layers = _linears(3)
+        assert [block for block in TypedModuleList(layers)] == layers
+
+    def test_integer_indexing_returns_the_element(self):
+        """Integer indexing (including negative) returns the stored module."""
+        layers = _linears(3)
+        tml = TypedModuleList(layers)
+        assert tml[0] is layers[0]
+        assert tml[-1] is layers[-1]
+
+    def test_slicing_returns_a_typed_module_list(self):
+        """Slicing returns a TypedModuleList (not a bare nn.ModuleList) with the right modules."""
+        layers = _linears(4)
+        sliced = TypedModuleList(layers)[1:3]
+        assert isinstance(sliced, TypedModuleList)
+        assert list(sliced) == layers[1:3]
+
+    def test_modules_are_registered_as_submodules(self):
+        """Child modules are registered, so parameters() / named_children() work."""
+        tml = TypedModuleList(_linears(2))
+        # Two Linear(3, 3) layers => 2 * (weight + bias) == 4 parameter tensors.
+        assert len(list(tml.parameters())) == 4
+        assert [name for name, _ in tml.named_children()] == ["0", "1"]
+
+    def test_registers_correctly_when_nested_in_a_module(self):
+        """Used as a submodule, its parameters surface on the parent under the attribute name."""
+
+        class Net(nn.Module):
+            def __init__(self) -> None:
+                super().__init__()
+                self.blocks = TypedModuleList(_linears(2))
+
+        net = Net()
+        param_names = [name for name, _ in net.named_parameters()]
+        assert len(param_names) == 4
+        assert all(name.startswith("blocks.") for name in param_names)
+
+    def test_append_mutates_and_returns_self(self):
+        """append adds the module and returns the same list (for chaining), like nn.ModuleList."""
+        tml: TypedModuleList[nn.Linear] = TypedModuleList()
+        layer = nn.Linear(3, 3)
+        returned = tml.append(layer)
+        assert returned is tml
+        assert isinstance(returned, TypedModuleList)
+        assert len(tml) == 1
+        assert tml[0] is layer
+
+    def test_setitem_replaces_element(self):
+        """__setitem__ replaces the module at an existing index and re-registers it."""
+        layers = _linears(2)
+        tml = TypedModuleList(layers)
+        replacement = nn.Linear(3, 3)
+        tml[0] = replacement
+        assert tml[0] is replacement
+        assert tml[1] is layers[1]
+        assert len(tml) == 2
+        # The replacement must be registered (old child gone, new child present).
+        assert replacement in tml.children()
+        assert layers[0] not in tml.children()
diff --git a/transformer_lens/ActivationCache.py b/transformer_lens/ActivationCache.py
index e820b64d3..7d392b6da 100644
--- a/transformer_lens/ActivationCache.py
+++ b/transformer_lens/ActivationCache.py
@@ -36,7 +36,6 @@ class first, including the examples, and then skimming the available methods. Yo
 from transformer_lens.utilities import Slice, SliceInput, warn_if_mps
 
 if TYPE_CHECKING:
-    from transformer_lens.components import TransformerBlock
     from transformer_lens.HookedTransformer import HookedTransformer
 
 
@@ -748,8 +747,7 @@ def compute_head_results(
             )
 
             # Element-wise multiplication of z and W_O (with shape [head_index, d_head, d_model])
-            # nn.ModuleList[T][i] is typed Tensor|Module upstream; cast restores T.
-            block = cast("TransformerBlock", self.model.blocks[layer])
+            block = self.model.blocks[layer]
             result = z * block.attn.W_O
 
             # Sum over d_head to get the contribution of each head to the residual stream
@@ -906,8 +904,7 @@ def get_neuron_results(
             pos_slice = Slice(pos_slice)
 
         neuron_acts = self[("post", layer, "mlp")]
-        # ModuleList[T] indexing is typed `Tensor | Module` upstream; cast restores T.
-        block = cast("TransformerBlock", self.model.blocks[layer])
+        block = self.model.blocks[layer]
         W_out = block.mlp.W_out
         if pos_slice is not None:
             # Note - order is important, as Slice.apply *may* collapse a dimension, so this ensures
@@ -974,8 +971,7 @@ def _stack_neuron_results_apply_ln_projected(
 
         components: list = []
         for l in range(layer):
-            # nn.ModuleList[T][i] is typed Tensor|Module upstream; cast restores T.
-            block = cast("TransformerBlock", self.model.blocks[l])
+            block = self.model.blocks[l]
             W_out_l = block.mlp.W_out  # [d_mlp, d_model]
             W_out_l_sliced = neuron_slice.apply(W_out_l, dim=0)
             W_proj_l = W_out_l_sliced @ project_2d  # [d_mlp, n_outs]
diff --git a/transformer_lens/HookedAudioEncoder.py b/transformer_lens/HookedAudioEncoder.py
index 472425ec5..a1cbd47eb 100644
--- a/transformer_lens/HookedAudioEncoder.py
+++ b/transformer_lens/HookedAudioEncoder.py
@@ -7,11 +7,10 @@
 from __future__ import annotations
 
 import logging
-from typing import Any, Dict, List, Optional, Tuple, TypeVar, Union, overload
+from typing import Any, Dict, List, Optional, Tuple, TypeVar, Union, cast, overload
 
 import numpy as np
 import torch
-import torch.nn as nn
 from einops import repeat
 from jaxtyping import Float, Int
 from transformers import AutoFeatureExtractor, HubertModel, Wav2Vec2Model
@@ -19,11 +18,11 @@
 
 from transformer_lens import loading_from_pretrained as loading
 from transformer_lens.ActivationCache import ActivationCache
-from transformer_lens.components import MLP, Attention, BertBlock
+from transformer_lens.components import MLP, BertBlock
 from transformer_lens.config.hooked_transformer_config import HookedTransformerConfig
 from transformer_lens.FactoredMatrix import FactoredMatrix
 from transformer_lens.HookedRootModule import HookedRootModule
-from transformer_lens.utilities import devices
+from transformer_lens.utilities import TypedModuleList, devices
 
 T = TypeVar("T", bound="HookedAudioEncoder")
 
@@ -41,6 +40,7 @@ class HookedAudioEncoder(HookedRootModule):
 
     processor: Any  # AutoFeatureExtractor — HF auto class, not typed as callable in stubs
     hubert_model: Union[HubertModel, Wav2Vec2Model]
+    blocks: TypedModuleList[BertBlock]
 
     def __init__(
         self,
@@ -60,7 +60,7 @@ def __init__(
 
         assert self.cfg.n_devices == 1, "Multiple devices not supported for HookedEncoder"
 
-        self.blocks = nn.ModuleList([BertBlock(self.cfg) for _ in range(self.cfg.n_layers)])
+        self.blocks = TypedModuleList([BertBlock(self.cfg) for _ in range(self.cfg.n_layers)])
 
         if move_to_device:
             if self.cfg.device is None:
@@ -426,86 +426,62 @@ def from_pretrained(
     @property
     def W_K(self) -> Float[torch.Tensor, "n_layers n_heads d_model d_head"]:
         """Stacks the key weights across all layers"""
-        for block in self.blocks:
-            assert isinstance(block.attn, Attention)
         return torch.stack([block.attn.W_K for block in self.blocks], dim=0)
 
     @property
     def W_Q(self) -> Float[torch.Tensor, "n_layers n_heads d_model d_head"]:
         """Stacks the query weights across all layers"""
-        for block in self.blocks:
-            assert isinstance(block.attn, Attention)
         return torch.stack([block.attn.W_Q for block in self.blocks], dim=0)
 
     @property
     def W_V(self) -> Float[torch.Tensor, "n_layers n_heads d_model d_head"]:
         """Stacks the value weights across all layers"""
-        for block in self.blocks:
-            assert isinstance(block.attn, Attention)
         return torch.stack([block.attn.W_V for block in self.blocks], dim=0)
 
     @property
     def W_O(self) -> Float[torch.Tensor, "n_layers n_heads d_head d_model"]:
         """Stacks the attn output weights across all layers"""
-        for block in self.blocks:
-            assert isinstance(block.attn, Attention)
         return torch.stack([block.attn.W_O for block in self.blocks], dim=0)
 
     @property
     def W_in(self) -> Float[torch.Tensor, "n_layers d_model d_mlp"]:
         """Stacks the MLP input weights across all layers"""
-        for block in self.blocks:
-            assert isinstance(block.mlp, MLP)
         return torch.stack([block.mlp.W_in for block in self.blocks], dim=0)
 
     @property
     def W_out(self) -> Float[torch.Tensor, "n_layers d_mlp d_model"]:
         """Stacks the MLP output weights across all layers"""
-        for block in self.blocks:
-            assert isinstance(block.mlp, MLP)
         return torch.stack([block.mlp.W_out for block in self.blocks], dim=0)
 
     @property
     def b_K(self) -> Float[torch.Tensor, "n_layers n_heads d_head"]:
         """Stacks the key biases across all layers"""
-        for block in self.blocks:
-            assert isinstance(block.attn, Attention)
         return torch.stack([block.attn.b_K for block in self.blocks], dim=0)
 
     @property
     def b_Q(self) -> Float[torch.Tensor, "n_layers n_heads d_head"]:
         """Stacks the query biases across all layers"""
-        for block in self.blocks:
-            assert isinstance(block.attn, Attention)
         return torch.stack([block.attn.b_Q for block in self.blocks], dim=0)
 
     @property
     def b_V(self) -> Float[torch.Tensor, "n_layers n_heads d_head"]:
         """Stacks the value biases across all layers"""
-        for block in self.blocks:
-            assert isinstance(block.attn, Attention)
         return torch.stack([block.attn.b_V for block in self.blocks], dim=0)
 
     @property
     def b_O(self) -> Float[torch.Tensor, "n_layers d_model"]:
         """Stacks the attn output biases across all layers"""
-        for block in self.blocks:
-            assert isinstance(block.attn, Attention)
         return torch.stack([block.attn.b_O for block in self.blocks], dim=0)
 
     @property
     def b_in(self) -> Float[torch.Tensor, "n_layers d_mlp"]:
         """Stacks the MLP input biases across all layers"""
-        for block in self.blocks:
-            assert isinstance(block.mlp, MLP)
-        return torch.stack([block.mlp.b_in for block in self.blocks], dim=0)
+        return torch.stack([cast(MLP, block.mlp).b_in for block in self.blocks], dim=0)
 
     @property
     def b_out(self) -> Float[torch.Tensor, "n_layers d_model"]:
         """Stacks the MLP output biases across all layers"""
-        for block in self.blocks:
-            assert isinstance(block.mlp, MLP)
-        return torch.stack([block.mlp.b_out for block in self.blocks], dim=0)
+        return torch.stack([cast(MLP, block.mlp).b_out for block in self.blocks], dim=0)
 
     @property
     def QK(self) -> FactoredMatrix:  # [n_layers, n_heads, d_model, d_model]
diff --git a/transformer_lens/HookedEncoder.py b/transformer_lens/HookedEncoder.py
index 9dffc07e4..f751fafbf 100644
--- a/transformer_lens/HookedEncoder.py
+++ b/transformer_lens/HookedEncoder.py
@@ -11,7 +11,6 @@
 from typing import Any, Dict, List, Optional, Tuple, TypeVar, Union, cast, overload
 
 import torch
-import torch.nn as nn
 from einops import repeat
 from jaxtyping import Float, Int
 from transformers.models.auto.tokenization_auto import AutoTokenizer
@@ -33,7 +32,7 @@
 from transformer_lens.FactoredMatrix import FactoredMatrix
 from transformer_lens.hook_points import HookPoint
 from transformer_lens.HookedRootModule import HookedRootModule
-from transformer_lens.utilities import devices
+from transformer_lens.utilities import TypedModuleList, devices
 
 T = TypeVar("T", bound="HookedEncoder")
 
@@ -49,11 +48,7 @@ class HookedEncoder(HookedRootModule):
         - There is no preprocessing (e.g. LayerNorm folding) when loading a pretrained model
     """
 
-    blocks: nn.ModuleList[BertBlock]  # type: ignore[type-arg]
-
-    def _get_blocks(self) -> list[BertBlock]:
-        """Helper to get blocks with proper typing."""
-        return [cast(BertBlock, block) for block in self.blocks]
+    blocks: TypedModuleList[BertBlock]
 
     def __init__(
         self,
@@ -91,7 +86,7 @@ def __init__(
             self.cfg.d_vocab_out = self.cfg.d_vocab
 
         self.embed = BertEmbed(self.cfg)
-        self.blocks = nn.ModuleList([BertBlock(self.cfg) for _ in range(self.cfg.n_layers)])
+        self.blocks = TypedModuleList([BertBlock(self.cfg) for _ in range(self.cfg.n_layers)])
         self.mlm_head = BertMLMHead(self.cfg)
         self.unembed = Unembed(self.cfg)
         self.nsp_head = BertNSPHead(self.cfg)
@@ -471,69 +466,69 @@ def W_E_pos(self) -> Float[torch.Tensor, "d_vocab+n_ctx d_model"]:
     @property
     def W_K(self) -> Float[torch.Tensor, "n_layers n_heads d_model d_head"]:
         """Stacks the key weights across all layers"""
-        return torch.stack([block.attn.W_K for block in self._get_blocks()], dim=0)
+        return torch.stack([block.attn.W_K for block in self.blocks], dim=0)
 
     @property
     def W_Q(self) -> Float[torch.Tensor, "n_layers n_heads d_model d_head"]:
         """Stacks the query weights across all layers"""
-        return torch.stack([block.attn.W_Q for block in self._get_blocks()], dim=0)
+        return torch.stack([block.attn.W_Q for block in self.blocks], dim=0)
 
     @property
     def W_V(self) -> Float[torch.Tensor, "n_layers n_heads d_model d_head"]:
         """Stacks the value weights across all layers"""
-        return torch.stack([block.attn.W_V for block in self._get_blocks()], dim=0)
+        return torch.stack([block.attn.W_V for block in self.blocks], dim=0)
 
     @property
     def W_O(self) -> Float[torch.Tensor, "n_layers n_heads d_head d_model"]:
         """Stacks the attn output weights across all layers"""
-        return torch.stack([block.attn.W_O for block in self._get_blocks()], dim=0)
+        return torch.stack([block.attn.W_O for block in self.blocks], dim=0)
 
     @property
     def W_in(self) -> Float[torch.Tensor, "n_layers d_model d_mlp"]:
         """Stacks the MLP input weights across all layers"""
         return torch.stack(
-            [cast(Union[MLP, GatedMLP], block.mlp).W_in for block in self._get_blocks()], dim=0
+            [cast(Union[MLP, GatedMLP], block.mlp).W_in for block in self.blocks], dim=0
         )
 
     @property
     def W_out(self) -> Float[torch.Tensor, "n_layers d_mlp d_model"]:
         """Stacks the MLP output weights across all layers"""
         return torch.stack(
-            [cast(Union[MLP, GatedMLP], block.mlp).W_out for block in self._get_blocks()], dim=0
+            [cast(Union[MLP, GatedMLP], block.mlp).W_out for block in self.blocks], dim=0
         )
 
     @property
     def b_K(self) -> Float[torch.Tensor, "n_layers n_heads d_head"]:
         """Stacks the key biases across all layers"""
-        return torch.stack([block.attn.b_K for block in self._get_blocks()], dim=0)
+        return torch.stack([block.attn.b_K for block in self.blocks], dim=0)
 
     @property
     def b_Q(self) -> Float[torch.Tensor, "n_layers n_heads d_head"]:
         """Stacks the query biases across all layers"""
-        return torch.stack([block.attn.b_Q for block in self._get_blocks()], dim=0)
+        return torch.stack([block.attn.b_Q for block in self.blocks], dim=0)
 
     @property
     def b_V(self) -> Float[torch.Tensor, "n_layers n_heads d_head"]:
         """Stacks the value biases across all layers"""
-        return torch.stack([block.attn.b_V for block in self._get_blocks()], dim=0)
+        return torch.stack([block.attn.b_V for block in self.blocks], dim=0)
 
     @property
     def b_O(self) -> Float[torch.Tensor, "n_layers d_model"]:
         """Stacks the attn output biases across all layers"""
-        return torch.stack([block.attn.b_O for block in self._get_blocks()], dim=0)
+        return torch.stack([block.attn.b_O for block in self.blocks], dim=0)
 
     @property
     def b_in(self) -> Float[torch.Tensor, "n_layers d_mlp"]:
         """Stacks the MLP input biases across all layers"""
         return torch.stack(
-            [cast(Union[MLP, GatedMLP], block.mlp).b_in for block in self._get_blocks()], dim=0
+            [cast(Union[MLP, GatedMLP], block.mlp).b_in for block in self.blocks], dim=0
         )
 
     @property
     def b_out(self) -> Float[torch.Tensor, "n_layers d_model"]:
         """Stacks the MLP output biases across all layers"""
         return torch.stack(
-            [cast(Union[MLP, GatedMLP], block.mlp).b_out for block in self._get_blocks()], dim=0
+            [cast(Union[MLP, GatedMLP], block.mlp).b_out for block in self.blocks], dim=0
         )
 
     @property
diff --git a/transformer_lens/HookedEncoderDecoder.py b/transformer_lens/HookedEncoderDecoder.py
index 7bc56df9e..df466aaa0 100644
--- a/transformer_lens/HookedEncoderDecoder.py
+++ b/transformer_lens/HookedEncoderDecoder.py
@@ -27,7 +27,6 @@
 import tqdm
 from einops import repeat
 from jaxtyping import Float, Int
-from torch import nn
 from transformers import AutoTokenizer, PreTrainedTokenizerBase
 from typing_extensions import Literal
 
@@ -38,7 +37,7 @@
 from transformer_lens.FactoredMatrix import FactoredMatrix
 from transformer_lens.hook_points import HookPoint
 from transformer_lens.HookedRootModule import HookedRootModule
-from transformer_lens.utilities import sample_logits, warn_if_mps
+from transformer_lens.utilities import TypedModuleList, sample_logits, warn_if_mps
 from transformer_lens.utilities.multi_gpu import get_device_for_block_index
 
 T = TypeVar("T", bound="HookedEncoderDecoder")
@@ -57,6 +56,8 @@ class HookedEncoderDecoder(HookedRootModule):
     """
 
     tokenizer: Optional[PreTrainedTokenizerBase]
+    encoder: TypedModuleList[T5Block]
+    decoder: TypedModuleList[T5Block]
 
     def __init__(
         self,
@@ -97,14 +98,14 @@ def __init__(
             self.cfg.d_vocab_out = self.cfg.d_vocab
 
         self.embed = Embed(self.cfg)
-        self.encoder = nn.ModuleList(
+        self.encoder = TypedModuleList(
             [
                 T5Block(self.cfg, num_layer, is_decoder=False)
                 for num_layer in range(self.cfg.n_layers)
             ]
         )
         self.encoder_final_ln = RMSNorm(self.cfg)
-        self.decoder = nn.ModuleList(
+        self.decoder = TypedModuleList(
             [
                 T5Block(self.cfg, num_layer, is_decoder=True)
                 for num_layer in range(self.cfg.n_layers)
@@ -258,9 +259,9 @@ def forward(
 
         query_len = key_len = tokens.shape[1]
 
-        encoder_positional_bias = cast(
-            T5Block, self.encoder[0]
-        ).attn.compute_relative_attention_bias(query_len, key_len, device=self.cfg.device)
+        encoder_positional_bias = self.encoder[0].attn.compute_relative_attention_bias(
+            query_len, key_len, device=self.cfg.device
+        )
 
         for encoder_block in self.encoder:
             resid = encoder_block(
@@ -275,9 +276,7 @@ def forward(
             raise ValueError("decoder_input cannot be None when input is not a string")
         decoder_resid = self.embed(decoder_input)
         decoder_query_len = decoder_key_len = decoder_input.shape[1]
-        decoder_positional_bias = cast(
-            T5Block, self.decoder[0]
-        ).attn.compute_relative_attention_bias(
+        decoder_positional_bias = self.decoder[0].attn.compute_relative_attention_bias(
             decoder_query_len, decoder_key_len, device=self.cfg.device
         )
 
@@ -638,7 +637,7 @@ def W_pos(self) -> None:
     def W_K(self) -> Float[torch.Tensor, "n_layers n_heads d_model d_head"]:
         """Stacks the key weights across all layers"""
         return torch.stack(
-            [cast(T5Block, block).attn.W_K for block in chain(self.encoder, self.decoder)],
+            [block.attn.W_K for block in chain(self.encoder, self.decoder)],
             dim=0,
         )
 
@@ -646,7 +645,7 @@ def W_K(self) -> Float[torch.Tensor, "n_layers n_heads d_model d_head"]:
     def W_Q(self) -> Float[torch.Tensor, "n_layers n_heads d_model d_head"]:
         """Stacks the query weights across all layers"""
         return torch.stack(
-            [cast(T5Block, block).attn.W_Q for block in chain(self.encoder, self.decoder)],
+            [block.attn.W_Q for block in chain(self.encoder, self.decoder)],
             dim=0,
         )
 
@@ -654,7 +653,7 @@ def W_Q(self) -> Float[torch.Tensor, "n_layers n_heads d_model d_head"]:
     def W_V(self) -> Float[torch.Tensor, "n_layers n_heads d_model d_head"]:
         """Stacks the value weights across all layers"""
         return torch.stack(
-            [cast(T5Block, block).attn.W_V for block in chain(self.encoder, self.decoder)],
+            [block.attn.W_V for block in chain(self.encoder, self.decoder)],
             dim=0,
         )
 
@@ -662,7 +661,7 @@ def W_V(self) -> Float[torch.Tensor, "n_layers n_heads d_model d_head"]:
     def W_O(self) -> Float[torch.Tensor, "n_layers n_heads d_head d_model"]:
         """Stacks the attn output weights across all layers"""
         return torch.stack(
-            [cast(T5Block, block).attn.W_O for block in chain(self.encoder, self.decoder)],
+            [block.attn.W_O for block in chain(self.encoder, self.decoder)],
             dim=0,
         )
 
@@ -671,7 +670,7 @@ def W_in(self) -> Float[torch.Tensor, "n_layers d_model d_mlp"]:
         """Stacks the MLP input weights across all layers"""
         weights: List[torch.Tensor] = []
         for block in chain(self.encoder, self.decoder):
-            mlp = cast(T5Block, block).mlp
+            mlp = block.mlp
             if isinstance(mlp, (MLP, GatedMLP)):
                 weights.append(mlp.W_in)
             else:
@@ -685,7 +684,7 @@ def W_out(self) -> Float[torch.Tensor, "n_layers d_mlp d_model"]:
         """Stacks the MLP output weights across all layers"""
         weights: List[torch.Tensor] = []
         for block in chain(self.encoder, self.decoder):
-            mlp = cast(T5Block, block).mlp
+            mlp = block.mlp
             if isinstance(mlp, (MLP, GatedMLP)):
                 weights.append(mlp.W_out)
             else:
@@ -698,7 +697,7 @@ def W_out(self) -> Float[torch.Tensor, "n_layers d_mlp d_model"]:
     def b_K(self) -> Float[torch.Tensor, "n_layers n_heads d_head"]:
         """Stacks the key biases across all layers"""
         return torch.stack(
-            [cast(T5Block, block).attn.b_K for block in chain(self.encoder, self.decoder)],
+            [block.attn.b_K for block in chain(self.encoder, self.decoder)],
             dim=0,
         )
 
@@ -706,7 +705,7 @@ def b_K(self) -> Float[torch.Tensor, "n_layers n_heads d_head"]:
     def b_Q(self) -> Float[torch.Tensor, "n_layers n_heads d_head"]:
         """Stacks the query biases across all layers"""
         return torch.stack(
-            [cast(T5Block, block).attn.b_Q for block in chain(self.encoder, self.decoder)],
+            [block.attn.b_Q for block in chain(self.encoder, self.decoder)],
             dim=0,
         )
 
@@ -714,7 +713,7 @@ def b_Q(self) -> Float[torch.Tensor, "n_layers n_heads d_head"]:
     def b_V(self) -> Float[torch.Tensor, "n_layers n_heads d_head"]:
         """Stacks the value biases across all layers"""
         return torch.stack(
-            [cast(T5Block, block).attn.b_V for block in chain(self.encoder, self.decoder)],
+            [block.attn.b_V for block in chain(self.encoder, self.decoder)],
             dim=0,
         )
 
@@ -722,7 +721,7 @@ def b_V(self) -> Float[torch.Tensor, "n_layers n_heads d_head"]:
     def b_O(self) -> Float[torch.Tensor, "n_layers d_model"]:
         """Stacks the attn output biases across all layers"""
         return torch.stack(
-            [cast(T5Block, block).attn.b_O for block in chain(self.encoder, self.decoder)],
+            [block.attn.b_O for block in chain(self.encoder, self.decoder)],
             dim=0,
         )
 
@@ -731,7 +730,7 @@ def b_in(self) -> Float[torch.Tensor, "n_layers d_mlp"]:
         """Stacks the MLP input biases across all layers"""
         biases: List[torch.Tensor] = []
         for block in chain(self.encoder, self.decoder):
-            mlp = cast(T5Block, block).mlp
+            mlp = block.mlp
             if isinstance(mlp, (MLP, GatedMLP)):
                 biases.append(mlp.b_in)
             else:
@@ -745,7 +744,7 @@ def b_out(self) -> Float[torch.Tensor, "n_layers d_model"]:
         """Stacks the MLP output biases across all layers"""
         biases: List[torch.Tensor] = []
         for block in chain(self.encoder, self.decoder):
-            mlp = cast(T5Block, block).mlp
+            mlp = block.mlp
             if isinstance(mlp, (MLP, GatedMLP)):
                 biases.append(mlp.b_out)
             else:
diff --git a/transformer_lens/HookedTransformer.py b/transformer_lens/HookedTransformer.py
index 8370bbf8a..7e9e780b1 100644
--- a/transformer_lens/HookedTransformer.py
+++ b/transformer_lens/HookedTransformer.py
@@ -66,6 +66,7 @@
 from transformer_lens.loading_from_pretrained import NON_HF_HOSTED_MODEL_NAMES
 from transformer_lens.utilities import (
     USE_DEFAULT_VALUE,
+    TypedModuleList,
     get_best_available_device,
     get_device_for_block_index,
     init_kaiming_normal_,
@@ -138,7 +139,7 @@ class HookedTransformer(HookedRootModule):
 
     ln_final: nn.Module
     tokenizer: Optional[PreTrainedTokenizerBase]
-    blocks: nn.ModuleList[TransformerBlock]  # type: ignore[type-arg]
+    blocks: TypedModuleList[TransformerBlock]
 
     def __init__(
         self,
@@ -224,7 +225,7 @@ def __init__(
         if self.cfg.use_hook_tokens:
             self.hook_tokens = HookPoint()  # [batch, pos]
 
-        self.blocks = nn.ModuleList(
+        self.blocks = TypedModuleList(
             [TransformerBlock(self.cfg, block_index) for block_index in range(self.cfg.n_layers)]
         )
 
@@ -640,7 +641,7 @@ def forward(
             # Eg: start_at_layer==None + stop_at_layer==0 means to only run the embed.
             # Eg: start_at_layer==3 + stop_at_layer==-1 means to run from layer 3 until the end of the PENULTIMATE layer
             blocks_and_idxs = list(zip(range(self.cfg.n_layers), self.blocks))
-            for i, block in blocks_and_idxs[start_at_layer:stop_at_layer]:  # type: ignore
+            for i, block in blocks_and_idxs[start_at_layer:stop_at_layer]:
                 # Note that each block includes skip connections, so we don't need
                 # residual + block(residual)
                 # If we're using multiple GPUs, we need to send the residual and shortformer_pos_embed to the correct GPU
@@ -2550,35 +2551,31 @@ def W_E_pos(self) -> Float[torch.Tensor, "d_vocab+n_ctx d_model"]:
     # we want to do analysis on weights across all layers. If GPU memory is a bottleneck, don't use
     # these properties!
 
-    def _get_blocks(self) -> list[TransformerBlock]:
-        """Helper to get blocks with proper typing."""
-        return [cast(TransformerBlock, block) for block in self.blocks]
-
     @property
     def W_K(self) -> Float[torch.Tensor, "n_layers n_heads d_model d_head"]:
         """Stack the key weights across all layers."""
-        return torch.stack([block.attn.W_K for block in self._get_blocks()], dim=0)
+        return torch.stack([block.attn.W_K for block in self.blocks], dim=0)
 
     @property
     def W_Q(self) -> Float[torch.Tensor, "n_layers n_heads d_model d_head"]:
         """Stack the query weights across all layers."""
-        return torch.stack([block.attn.W_Q for block in self._get_blocks()], dim=0)
+        return torch.stack([block.attn.W_Q for block in self.blocks], dim=0)
 
     @property
     def W_V(self) -> Float[torch.Tensor, "n_layers n_heads d_model d_head"]:
         """Stack the value weights across all layers."""
-        return torch.stack([block.attn.W_V for block in self._get_blocks()], dim=0)
+        return torch.stack([block.attn.W_V for block in self.blocks], dim=0)
 
     @property
     def W_O(self) -> Float[torch.Tensor, "n_layers n_heads d_head d_model"]:
         """Stack the attn output weights across all layers."""
-        return torch.stack([block.attn.W_O for block in self._get_blocks()], dim=0)
+        return torch.stack([block.attn.W_O for block in self.blocks], dim=0)
 
     @property
     def W_in(self) -> Float[torch.Tensor, "n_layers d_model d_mlp"]:
         """Stack the MLP input weights across all layers."""
         return torch.stack(
-            [cast(Union[MLP, GatedMLP], block.mlp).W_in for block in self._get_blocks()], dim=0
+            [cast(Union[MLP, GatedMLP], block.mlp).W_in for block in self.blocks], dim=0
         )
 
     @property
@@ -2588,9 +2585,7 @@ def W_gate(self) -> Union[Float[torch.Tensor, "n_layers d_model d_mlp"], None]:
         Only works for models with gated MLPs.
         """
         if self.cfg.gated_mlp:
-            return torch.stack(
-                [cast(GatedMLP, block.mlp).W_gate for block in self._get_blocks()], dim=0
-            )
+            return torch.stack([cast(GatedMLP, block.mlp).W_gate for block in self.blocks], dim=0)
         else:
             return None
 
@@ -2598,41 +2593,41 @@ def W_gate(self) -> Union[Float[torch.Tensor, "n_layers d_model d_mlp"], None]:
     def W_out(self) -> Float[torch.Tensor, "n_layers d_mlp d_model"]:
         """Stack the MLP output weights across all layers."""
         return torch.stack(
-            [cast(Union[MLP, GatedMLP], block.mlp).W_out for block in self._get_blocks()], dim=0
+            [cast(Union[MLP, GatedMLP], block.mlp).W_out for block in self.blocks], dim=0
         )
 
     @property
     def b_K(self) -> Float[torch.Tensor, "n_layers n_heads d_head"]:
         """Stack the key biases across all layers."""
-        return torch.stack([block.attn.b_K for block in self._get_blocks()], dim=0)
+        return torch.stack([block.attn.b_K for block in self.blocks], dim=0)
 
     @property
     def b_Q(self) -> Float[torch.Tensor, "n_layers n_heads d_head"]:
         """Stack the query biases across all layers."""
-        return torch.stack([block.attn.b_Q for block in self._get_blocks()], dim=0)
+        return torch.stack([block.attn.b_Q for block in self.blocks], dim=0)
 
     @property
     def b_V(self) -> Float[torch.Tensor, "n_layers n_heads d_head"]:
         """Stack the value biases across all layers."""
-        return torch.stack([block.attn.b_V for block in self._get_blocks()], dim=0)
+        return torch.stack([block.attn.b_V for block in self.blocks], dim=0)
 
     @property
     def b_O(self) -> Float[torch.Tensor, "n_layers d_model"]:
         """Stack the attn output biases across all layers."""
-        return torch.stack([block.attn.b_O for block in self._get_blocks()], dim=0)
+        return torch.stack([block.attn.b_O for block in self.blocks], dim=0)
 
     @property
     def b_in(self) -> Float[torch.Tensor, "n_layers d_mlp"]:
         """Stack the MLP input biases across all layers."""
         return torch.stack(
-            [cast(Union[MLP, GatedMLP], block.mlp).b_in for block in self._get_blocks()], dim=0
+            [cast(Union[MLP, GatedMLP], block.mlp).b_in for block in self.blocks], dim=0
         )
 
     @property
     def b_out(self) -> Float[torch.Tensor, "n_layers d_model"]:
         """Stack the MLP output biases across all layers."""
         return torch.stack(
-            [cast(Union[MLP, GatedMLP], block.mlp).b_out for block in self._get_blocks()], dim=0
+            [cast(Union[MLP, GatedMLP], block.mlp).b_out for block in self.blocks], dim=0
         )
 
     @property
@@ -2668,13 +2663,13 @@ def accumulated_bias(
         accumulated_bias = torch.zeros(self.cfg.d_model, device=self.cfg.device)
 
         for i in range(layer):
-            block = cast(TransformerBlock, self.blocks[i])
+            block = self.blocks[i]
             accumulated_bias += cast(torch.Tensor, block.attn.b_O)
             if include_mlp_biases:
                 accumulated_bias += cast(torch.Tensor, block.mlp.b_out)
         if mlp_input:
             assert layer < self.cfg.n_layers, "Cannot include attn_bias from beyond the final layer"
-            block = cast(TransformerBlock, self.blocks[layer])
+            block = self.blocks[layer]
             accumulated_bias += cast(torch.Tensor, block.attn.b_O)
         return accumulated_bias
 
diff --git a/transformer_lens/benchmarks/weight_processing.py b/transformer_lens/benchmarks/weight_processing.py
index 62e561b25..9ffe1c08f 100644
--- a/transformer_lens/benchmarks/weight_processing.py
+++ b/transformer_lens/benchmarks/weight_processing.py
@@ -77,11 +77,11 @@ def benchmark_weight_processing(
                 )
             _mlp_idx, mlp_block = mlp_blocks[0]
             bridge_w_out = mlp_block.mlp.W_out
-            reference_w_out = reference_model.blocks[_mlp_idx].mlp.W_out  # type: ignore[union-attr]
+            reference_w_out = reference_model.blocks[_mlp_idx].mlp.W_out
 
             bridge_mean = torch.mean(torch.abs(torch.mean(bridge_w_out, dim=-1, keepdim=True)))
             reference_mean = torch.mean(
-                torch.abs(torch.mean(reference_w_out, dim=-1, keepdim=True))  # type: ignore[arg-type]
+                torch.abs(torch.mean(reference_w_out, dim=-1, keepdim=True))
             )
 
             if bridge_mean.item() > 1e-3:
@@ -161,7 +161,7 @@ def benchmark_weight_sharing(
             # Verify weights are identical before modification
             bridge_W_V = torch.clone(cast(torch.Tensor, bridge_attn_block.attn.W_V))
             reference_W_V = torch.clone(
-                cast(torch.Tensor, reference_model.blocks[bridge_attn_idx].attn.W_V)  # type: ignore[union-attr]
+                cast(torch.Tensor, reference_model.blocks[bridge_attn_idx].attn.W_V)
             )
 
             # Check if models have GQA (different head counts for K/V vs Q)
@@ -206,7 +206,7 @@ def benchmark_weight_sharing(
             # Modify weights in both models
             with torch.no_grad():
                 bridge_attn_block.attn.W_V[0, :, :] = 0  # type: ignore[union-attr,operator]
-                reference_model.blocks[bridge_attn_idx].attn.W_V[0, :, :] = 0  # type: ignore[union-attr,operator]
+                reference_model.blocks[bridge_attn_idx].attn.W_V[0, :, :] = 0
 
             # Test modified losses
             bridge_modified = bridge(test_text, return_type="loss")
@@ -218,7 +218,7 @@ def benchmark_weight_sharing(
             # Restore weights
             with torch.no_grad():
                 bridge_attn_block.attn.W_V.copy_(bridge_W_V)  # type: ignore[union-attr,operator,arg-type]
-                reference_model.blocks[bridge_attn_idx].attn.W_V.copy_(reference_W_V)  # type: ignore[union-attr,operator,arg-type]
+                reference_model.blocks[bridge_attn_idx].attn.W_V.copy_(reference_W_V)
 
             diff = abs(bridge_change - reference_change)
             if diff < atol:
diff --git a/transformer_lens/model_bridge/sources/native/model.py b/transformer_lens/model_bridge/sources/native/model.py
index 2035ade42..1a6fa2e8b 100644
--- a/transformer_lens/model_bridge/sources/native/model.py
+++ b/transformer_lens/model_bridge/sources/native/model.py
@@ -16,6 +16,7 @@
 import torch.nn.functional as F
 
 from transformer_lens.config import TransformerBridgeConfig
+from transformer_lens.utilities import TypedModuleList
 
 # gelu_new = the tanh-approximation HF GPT-2 / HT use; F.gelu(approximate="tanh")
 # is the exact same formula.
@@ -404,7 +405,7 @@ def __init__(self, cfg: TransformerBridgeConfig):
                 f"NativeModel supports 'standard' and 'rotary'."
             )
 
-        self.layers = nn.ModuleList(
+        self.layers = TypedModuleList(
             [NativeBlock(cfg, rotary=self.rotary) for _ in range(cfg.n_layers)]
         )
         # final_rms forces RMS on the final norm regardless of block-norm choice
diff --git a/transformer_lens/utilities/__init__.py b/transformer_lens/utilities/__init__.py
index 3da03052c..e242e951c 100644
--- a/transformer_lens/utilities/__init__.py
+++ b/transformer_lens/utilities/__init__.py
@@ -80,3 +80,4 @@
     get_tokens_with_bos_removed,
     tokenize_and_concatenate,
 )
+from .typed_module_list import TypedModuleList
diff --git a/transformer_lens/utilities/bridge_components.py b/transformer_lens/utilities/bridge_components.py
index 317289b52..8c0418fb6 100644
--- a/transformer_lens/utilities/bridge_components.py
+++ b/transformer_lens/utilities/bridge_components.py
@@ -1,6 +1,6 @@
 """Utilities for traversing and applying functions to every component in a TransformerBridge model."""
 
-from typing import Any, Callable
+from typing import Any, Callable, cast
 
 import torch.nn as nn
 
@@ -73,8 +73,13 @@ def collect_components_of_block_bridge(
     # Make sure the remote component is a ModuleList
     if isinstance(remote_module_list, nn.ModuleList):
         for block in remote_module_list:
-            components[block.name] = block
-            components = collect_all_submodules_of_component(model, block, components, block.name)
+            block_component = cast(GeneralizedComponent, block)
+            block_name = block_component.name
+            assert block_name is not None, "Block bridge component must have a name"
+            components[block_name] = block_component
+            components = collect_all_submodules_of_component(
+                model, block_component, components, block_name
+            )
     return components
 
 
diff --git a/transformer_lens/utilities/typed_module_list.py b/transformer_lens/utilities/typed_module_list.py
new file mode 100644
index 000000000..f5e0d4b0c
--- /dev/null
+++ b/transformer_lens/utilities/typed_module_list.py
@@ -0,0 +1,27 @@
+"""Typed Module List.
+
+A thin generic wrapper around :class:`torch.nn.ModuleList`, inspired by
+https://github.com/pytorch/pytorch/issues/80821#issuecomment-3188314929.
+
+``nn.ModuleList`` is not generic, so iterating or indexing it yields a bare
+``nn.Module`` (or ``Any`` on torch < 2.8). ``TypedModuleList[T]`` preserves the
+element type ``T`` through ` iteration and other methods while remaining a drop-in
+``nn.ModuleList`` at runtime (children are still registered as submodules).
+
+The element-type-preserving signatures live in a companion stub
+``typed_module_list.pyi`` for technical reasons related to jaxtyping/beartype.
+"""
+
+from typing import Generic, TypeVar
+
+from torch import nn
+
+T = TypeVar("T", bound=nn.Module)
+
+
+class TypedModuleList(nn.ModuleList, Generic[T]):
+    """An ``nn.ModuleList`` that remembers the type ``T`` of the modules it holds.
+
+    Runtime behaviour is exactly ``nn.ModuleList``'s; the typed API is declared in
+    ``typed_module_list.pyi``.
+    """
diff --git a/transformer_lens/utilities/typed_module_list.pyi b/transformer_lens/utilities/typed_module_list.pyi
new file mode 100644
index 000000000..dc997041a
--- /dev/null
+++ b/transformer_lens/utilities/typed_module_list.pyi
@@ -0,0 +1,21 @@
+"""Typed Module List type annotations
+
+The element-type-preserving signatures live here rather than in
+``typed_module_list.py`` for technical reasons related to jaxtyping/beartype.
+"""
+
+from typing import Generic, Iterable, Iterator, Optional, TypeVar, overload
+
+from torch import nn
+
+T = TypeVar("T", bound=nn.Module)
+
+class TypedModuleList(nn.ModuleList, Generic[T]):
+    def __init__(self, modules: Optional[Iterable[T]] = ...) -> None: ...
+    def __iter__(self) -> Iterator[T]: ...
+    @overload
+    def __getitem__(self, idx: slice) -> TypedModuleList[T]: ...
+    @overload
+    def __getitem__(self, idx: int) -> T: ...
+    def append(self, module: T) -> TypedModuleList[T]: ...  # type: ignore[override]
+    def __setitem__(self, idx: int, module: T) -> None: ...  # type: ignore[override]
diff --git a/uv.lock b/uv.lock
index 3f03b3e29..f4b1b0809 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1047,6 +1047,31 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/20/2a/1b016902351a523aa2bd446b50a5bc1175d7a7d1cf90fe2ef904f9b84ebc/cryptography-46.0.7-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:258514877e15963bd43b558917bc9f54cf7cf866c38aa576ebf47a77ddbc43a4", size = 3412829, upload-time = "2026-04-08T01:57:48.874Z" },
 ]
 
+[[package]]
+name = "cuda-bindings"
+version = "12.9.4"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "cuda-pathfinder", marker = "sys_platform == 'linux'" },
+]
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/7a/d8/b546104b8da3f562c1ff8ab36d130c8fe1dd6a045ced80b4f6ad74f7d4e1/cuda_bindings-12.9.4-cp310-cp310-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4d3c842c2a4303b2a580fe955018e31aea30278be19795ae05226235268032e5", size = 12148218, upload-time = "2025-10-21T14:51:28.855Z" },
+    { url = "https://files.pythonhosted.org/packages/45/e7/b47792cc2d01c7e1d37c32402182524774dadd2d26339bd224e0e913832e/cuda_bindings-12.9.4-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c912a3d9e6b6651853eed8eed96d6800d69c08e94052c292fec3f282c5a817c9", size = 12210593, upload-time = "2025-10-21T14:51:36.574Z" },
+    { url = "https://files.pythonhosted.org/packages/a9/c1/dabe88f52c3e3760d861401bb994df08f672ec893b8f7592dc91626adcf3/cuda_bindings-12.9.4-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:fda147a344e8eaeca0c6ff113d2851ffca8f7dfc0a6c932374ee5c47caa649c8", size = 12151019, upload-time = "2025-10-21T14:51:43.167Z" },
+    { url = "https://files.pythonhosted.org/packages/63/56/e465c31dc9111be3441a9ba7df1941fe98f4aa6e71e8788a3fb4534ce24d/cuda_bindings-12.9.4-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:32bdc5a76906be4c61eb98f546a6786c5773a881f3b166486449b5d141e4a39f", size = 11906628, upload-time = "2025-10-21T14:51:49.905Z" },
+    { url = "https://files.pythonhosted.org/packages/a3/84/1e6be415e37478070aeeee5884c2022713c1ecc735e6d82d744de0252eee/cuda_bindings-12.9.4-cp313-cp313t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:56e0043c457a99ac473ddc926fe0dc4046694d99caef633e92601ab52cbe17eb", size = 11925991, upload-time = "2025-10-21T14:51:56.535Z" },
+    { url = "https://files.pythonhosted.org/packages/d1/af/6dfd8f2ed90b1d4719bc053ff8940e494640fe4212dc3dd72f383e4992da/cuda_bindings-12.9.4-cp314-cp314-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8b72ee72a9cc1b531db31eebaaee5c69a8ec3500e32c6933f2d3b15297b53686", size = 11922703, upload-time = "2025-10-21T14:52:03.585Z" },
+    { url = "https://files.pythonhosted.org/packages/6c/19/90ac264acc00f6df8a49378eedec9fd2db3061bf9263bf9f39fd3d8377c3/cuda_bindings-12.9.4-cp314-cp314t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d80bffc357df9988dca279734bc9674c3934a654cab10cadeed27ce17d8635ee", size = 11924658, upload-time = "2025-10-21T14:52:10.411Z" },
+]
+
+[[package]]
+name = "cuda-pathfinder"
+version = "1.5.5"
+source = { registry = "https://pypi.org/simple" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/11/c8/26f2e4aae92f11522a96043892ba39a90eac610d5242523aa863212bc1c7/cuda_pathfinder-1.5.5-py3-none-any.whl", hash = "sha256:0228c023f95d1480f143ef5c8922d27a2ab052087a942e81dc289c9eb8f91689", size = 51671, upload-time = "2026-05-27T01:21:25.413Z" },
+]
+
 [[package]]
 name = "cycler"
 version = "0.12.1"
@@ -3661,81 +3686,77 @@ wheels = [
 
 [[package]]
 name = "nvidia-cublas-cu12"
-version = "12.6.4.1"
+version = "12.8.4.1"
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