diff --git a/.ai-instructions b/.ai-instructions
index 609ac4a8d..afbb98140 160000
--- a/.ai-instructions
+++ b/.ai-instructions
@@ -1 +1 @@
-Subproject commit 609ac4a8d9262f93594f36ea382d30cd94ea07a4
+Subproject commit afbb98140f3f47724eec2f689ea78953ddcd60f3
diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml
index 80b272c16..796f8dd51 100644
--- a/.github/workflows/main.yml
+++ b/.github/workflows/main.yml
@@ -28,7 +28,7 @@ jobs:
       - uses: actions/checkout@v6
       - uses: prefix-dev/setup-pixi@v0.9.5
         with:
-          pixi-version: v0.67.2
+          pixi-version: v0.68.1
           cache: true
           cache-write: ${{ github.event_name == 'push' && github.ref_name == 'main' }}
           environments: tests-cpu
@@ -59,7 +59,7 @@ jobs:
       - uses: actions/checkout@v6
       - uses: prefix-dev/setup-pixi@v0.9.5
         with:
-          pixi-version: v0.67.2
+          pixi-version: v0.68.1
           cache: true
           cache-write: ${{ github.event_name == 'push' && github.ref_name == 'main' }}
           environments: type-checking
@@ -82,7 +82,7 @@ jobs:
       - uses: actions/checkout@v6
       - uses: prefix-dev/setup-pixi@v0.9.5
         with:
-          pixi-version: v0.67.2
+          pixi-version: v0.68.1
           cache: true
           cache-write: ${{ github.event_name == 'push' && github.ref_name == 'main' }}
           environments: tests-cuda12
@@ -101,7 +101,7 @@ jobs:
       - uses: actions/checkout@v6
       - uses: prefix-dev/setup-pixi@v0.9.5
         with:
-          pixi-version: v0.67.2
+          pixi-version: v0.68.1
           cache: true
           cache-write: ${{ github.event_name == 'push' && github.ref_name == 'main' }}
           environments: tests-cuda12
@@ -116,7 +116,7 @@ jobs:
   #     - uses: actions/checkout@v6
   #     - uses: prefix-dev/setup-pixi@v0.9.5
   #       with:
-  #         pixi-version: v0.67.2
+  #         pixi-version: v0.68.1
   #         cache: true
   #         cache-write: ${{ github.event_name == 'push' && github.ref_name == 'main' }}
   #         environments: tests-cpu
diff --git a/AGENTS.md b/AGENTS.md
index b311ee198..21e9fcc86 100644
--- a/AGENTS.md
+++ b/AGENTS.md
@@ -275,13 +275,13 @@ loaded = SimulationResult.from_pickle("path/to/file.pkl")
 
 ### Initial Conditions Format
 
-Initial conditions use a flat dictionary with state names plus `"regime"`:
+Initial conditions use a flat dictionary with state names plus `"regime_id"`:
 
 ```python
 initial_conditions = {
     "wealth": jnp.array([1.0, 2.0, 3.0]),
     "health": jnp.array([0.5, 0.8, 0.3]),
-    "regime": jnp.array([RegimeId.working, RegimeId.working, RegimeId.retired]),
+    "regime_id": jnp.array([RegimeId.working, RegimeId.working, RegimeId.retired]),
 }
 ```
 
@@ -527,11 +527,16 @@ Code structure should be self-evident from function names and ordering.
 - **Helper function names follow `{verb}_{qualifier}_noun` patterns.** E.g.,
   `get_irreg_coordinate`, `find_irreg_coordinate`, `get_linspace_coordinate` — not
   `get_coordinate_irreg`.
-- **Use `@overload` when a function accepts both scalar and array inputs.** When a
-  function works with both `ScalarFloat` and `Array`, add overload declarations so the
-  type checker can track `(ScalarFloat) -> ScalarFloat` and `(Array) -> Array`
-  separately. Concrete subclass methods need their own overloads too (not just the
-  abstract base).
+- **Pick the single narrowest jaxtyping alias — never scalar/array `@overload` pairs,
+  never `ScalarX | XND` unions.** ty erases jaxtyping shape annotations: `ScalarFloat`,
+  `Float1D`, and `FloatND` all reveal as `Array`, so scalar/array `@overload` pairs and
+  `ScalarFloat | FloatND`-style unions add zero static precision — they are pure noise.
+  At runtime, beartype treats a 0-d float array as satisfying both `ScalarFloat` and
+  `FloatND`, so `ScalarFloat ⊆ FloatND` and the union is redundant. Annotate each slot
+  with the one alias that matches its genuine rank: `ScalarFloat`/`ScalarInt` for
+  fixed-0-d, `Float1D`/`Int1D` for fixed-1-d, `FloatND`/`IntND` for genuinely rank-
+  polymorphic. Never use a bare `Array` annotation — always reach for the narrowest
+  `lcm.typing` alias.
 - **`func` for callable abbreviations** — use `func`, `func_name`, `func_params` (never
   `fn`). Full word `function(s)` in dataclass field names and public method names.
 - **Singular `state_names` / `action_names`** — not `states_names` / `actions_names`.
diff --git a/benchmarks/_gpu_mem.py b/benchmarks/_gpu_mem.py
index 45e328216..591b6ba18 100644
--- a/benchmarks/_gpu_mem.py
+++ b/benchmarks/_gpu_mem.py
@@ -26,6 +26,11 @@ class MahlerYumGpuPeakMem(GpuPeakMem):
 # Project root: the directory containing the benchmarks/ package.
 _PROJECT_ROOT = Path(__file__).resolve().parent.parent
 
+# Marks the peak-memory line on the subprocess's stdout. The subprocess imports
+# lcm, whose beartype claw can emit diagnostics to stdout, so the parent locates
+# this line instead of parsing stdout wholesale.
+_PEAK_MARKER = "__PEAK_BYTES_IN_USE__"
+
 
 def measure_gpu_peak(bench_module: str, bench_class: str) -> int:
     """Run a benchmark in a subprocess and return peak GPU bytes.
@@ -58,7 +63,15 @@ def measure_gpu_peak(bench_module: str, bench_class: str) -> int:
             f"stderr: {result.stderr!r}"
         )
         raise RuntimeError(msg)
-    return int(result.stdout.strip())
+    for line in result.stdout.splitlines():
+        if line.startswith(_PEAK_MARKER):
+            return int(line.removeprefix(_PEAK_MARKER).strip())
+    msg = (
+        "GPU memory subprocess produced no peak-bytes line.\n"
+        f"stdout: {result.stdout!r}\n"
+        f"stderr: {result.stderr!r}"
+    )
+    raise RuntimeError(msg)
 
 
 def _track_gpu_peak_mem(self):
@@ -104,4 +117,4 @@ def setup(self):
     import jax
 
     stats = jax.local_devices()[0].memory_stats()
-    print(stats["peak_bytes_in_use"])
+    print(f"{_PEAK_MARKER} {stats['peak_bytes_in_use']}")
diff --git a/benchmarks/bench_mahler_yum.py b/benchmarks/bench_mahler_yum.py
index fa8e3f6ba..046757a82 100644
--- a/benchmarks/bench_mahler_yum.py
+++ b/benchmarks/bench_mahler_yum.py
@@ -29,7 +29,7 @@ def _build(self):
         self.model_params = {"alive": common_params}
         self.initial_conditions = {
             **initial_states,
-            "regime": jnp.full(
+            "regime_id": jnp.full(
                 _N_SUBJECTS,
                 self.model.regime_names_to_ids["alive"],
                 dtype=jnp.int32,
diff --git a/benchmarks/bench_precautionary_savings.py b/benchmarks/bench_precautionary_savings.py
index 5f02671bb..822d3747d 100644
--- a/benchmarks/bench_precautionary_savings.py
+++ b/benchmarks/bench_precautionary_savings.py
@@ -33,7 +33,7 @@ def _make_initial_conditions(n_subjects):
         "age": jnp.full(n_subjects, 20.0),
         "wealth": jnp.full(n_subjects, 5.0),
         "income": jnp.full(n_subjects, 0.0),
-        "regime": jnp.zeros(n_subjects, dtype=jnp.int32),
+        "regime_id": jnp.zeros(n_subjects, dtype=jnp.int32),
     }
 
 
diff --git a/docs/development/benchmarking.md b/docs/development/benchmarking.md
index 0d660e08a..8e6cc5748 100644
--- a/docs/development/benchmarking.md
+++ b/docs/development/benchmarking.md
@@ -129,7 +129,7 @@ class TimeMyModel:
         self.model_params = my_model.get_params()
         self.initial_conditions = {
             "wealth": jnp.full(1_000, 5.0),
-            "regime": jnp.zeros(1_000, dtype=jnp.int32),
+            "regime_id": jnp.zeros(1_000, dtype=jnp.int32),
         }
 
         # JIT warmup (timed separately)
diff --git a/docs/examples/mahler_yum_2024.md b/docs/examples/mahler_yum_2024.md
index 13cabbe99..4ce0df0a8 100644
--- a/docs/examples/mahler_yum_2024.md
+++ b/docs/examples/mahler_yum_2024.md
@@ -40,7 +40,7 @@ result = MAHLER_YUM_MODEL.simulate(
     params={"alive": params},
     initial_conditions={
         **initial_states,
-        "regime": jnp.full(
+        "regime_id": jnp.full(
             n_subjects,
             MAHLER_YUM_MODEL.regime_names_to_ids["alive"],
         ),
diff --git a/docs/examples/mortality.md b/docs/examples/mortality.md
index 8dcdf9c9b..687fb528b 100644
--- a/docs/examples/mortality.md
+++ b/docs/examples/mortality.md
@@ -26,7 +26,7 @@ result = model.simulate(
     initial_conditions={
         "age": jnp.full(100, model.ages.values[0]),
         "wealth": jnp.linspace(1, 100, 100),
-        "regime": jnp.full(100, model.regime_names_to_ids["working_life"]),
+        "regime_id": jnp.full(100, model.regime_names_to_ids["working_life"]),
     },
     period_to_regime_to_V_arr=None,
     seed=1234,
diff --git a/docs/examples/precautionary_savings.md b/docs/examples/precautionary_savings.md
index 4fe1c1568..6e92d327a 100644
--- a/docs/examples/precautionary_savings.md
+++ b/docs/examples/precautionary_savings.md
@@ -27,7 +27,7 @@ result = model.simulate(
         "age": jnp.full(100, model.ages.values[0]),
         "wealth": jnp.linspace(1, 10, 100),
         "income": jnp.zeros(100),
-        "regime": jnp.full(100, model.regime_names_to_ids["alive"]),
+        "regime_id": jnp.full(100, model.regime_names_to_ids["alive"]),
     },
     period_to_regime_to_V_arr=None,
 )
diff --git a/docs/examples/precautionary_savings_health.md b/docs/examples/precautionary_savings_health.md
index 7b4cf8b4d..38a244125 100644
--- a/docs/examples/precautionary_savings_health.md
+++ b/docs/examples/precautionary_savings_health.md
@@ -32,7 +32,7 @@ result = model.simulate(
         "age": jnp.full(1_000, model.ages.values[0]),
         "wealth": jnp.full(1_000, 1.0),
         "health": jnp.full(1_000, 1.0),
-        "regime": jnp.full(1_000, model.regime_names_to_ids["working_life"]),
+        "regime_id": jnp.full(1_000, model.regime_names_to_ids["working_life"]),
     },
     period_to_regime_to_V_arr=None,
 )
diff --git a/docs/examples/tiny.md b/docs/examples/tiny.md
index 24d64fb76..aa943d395 100644
--- a/docs/examples/tiny.md
+++ b/docs/examples/tiny.md
@@ -25,7 +25,7 @@ params = get_params()
 
 initial_df = pd.DataFrame(
     {
-        "regime": "working_life",
+        "regime_name": "working_life",
         "age": model.ages.values[0],
         "wealth": np.linspace(1, 20, 100),
     }
diff --git a/docs/user_guide/benchmarking.md b/docs/user_guide/benchmarking.md
index 85de82ddb..0140cfe97 100644
--- a/docs/user_guide/benchmarking.md
+++ b/docs/user_guide/benchmarking.md
@@ -91,7 +91,7 @@ class TimeSolveSimulate:
         self.initial_conditions = {
             "age": jnp.full(500, 25.0),
             "wealth": jnp.full(500, 5.0),
-            "regime": jnp.zeros(500, dtype=jnp.int32),
+            "regime_id": jnp.zeros(500, dtype=jnp.int32),
         }
 
         # --- JAX warmup --------------------------------------------------
diff --git a/docs/user_guide/pandas_interop.md b/docs/user_guide/pandas_interop.md
index 5a04d8a2c..1bca397ab 100644
--- a/docs/user_guide/pandas_interop.md
+++ b/docs/user_guide/pandas_interop.md
@@ -12,12 +12,12 @@ is DataFrame in, DataFrame out.
 ## Initial Conditions as a DataFrame
 
 Pass a pandas DataFrame directly to `simulate()` as `initial_conditions`. One row per
-agent, one column per state variable, plus a `"regime"` column:
+agent, one column per state variable, plus a `"regime_name"` column:
 
 ```python
 df = pd.DataFrame(
     {
-        "regime": ["working", "working", "retired"],
+        "regime_name": ["working", "working", "retired"],
         "wealth": [10.0, 50.0, 30.0],
         "health": ["good", "bad", "good"],
         "age": [25.0, 25.0, 25.0],
@@ -31,7 +31,7 @@ result = model.simulate(
 )
 ```
 
-- `"regime"` column is required. Use regime names as strings (e.g., `"working"`).
+- `"regime_name"` column is required. Use regime names as strings (e.g., `"working"`).
 - Discrete states use string labels from the model's categorical classes (e.g., `"good"`
   instead of `0`). Labels are validated and mapped to integer codes automatically.
 - Continuous states pass through as-is.
diff --git a/docs/user_guide/solving_and_simulating.md b/docs/user_guide/solving_and_simulating.md
index 873974fb1..e85d5aaac 100644
--- a/docs/user_guide/solving_and_simulating.md
+++ b/docs/user_guide/solving_and_simulating.md
@@ -74,7 +74,7 @@ import pandas as pd
 
 df = pd.DataFrame(
     {
-        "regime": ["working_life", "working_life", "retirement", "working_life"],
+        "regime_name": ["working_life", "working_life", "retirement", "working_life"],
         "age": [25.0, 25.0, 25.0, 25.0],
         "wealth": [1.0, 5.0, 10.0, 20.0],
         "health": ["good", "bad", "bad", "good"],  # string labels, auto-converted
@@ -102,7 +102,7 @@ initial_conditions = {
     "age": jnp.array([25.0, 25.0, 25.0, 25.0]),
     "wealth": jnp.array([1.0, 5.0, 10.0, 20.0]),
     "health": jnp.array([0, 1, 1, 0]),  # integer codes for discrete states
-    "regime": jnp.array(
+    "regime_id": jnp.array(
         [
             RegimeId.working_life,
             RegimeId.working_life,
@@ -114,7 +114,7 @@ initial_conditions = {
 ```
 
 - Every non-shock state must have an entry.
-- `"regime"` must be included, with integer codes from the `regime_id_class`.
+- `"regime_id"` must be included, with integer codes from the `regime_id_class`.
 - All arrays must have the same length (= number of agents).
 - Shock states are drawn automatically.
 
@@ -140,7 +140,7 @@ Subjects can start at different ages:
 initial_conditions = {
     "age": jnp.array([40.0, 60.0]),
     "wealth": jnp.array([50.0, 50.0]),
-    "regime": jnp.array(
+    "regime_id": jnp.array(
         [
             model.regime_names_to_ids["working_life"],
             model.regime_names_to_ids["working_life"],
@@ -160,7 +160,7 @@ earlier periods are omitted, not filled with placeholders.
 df = result.to_dataframe()
 ```
 
-Returns a pandas DataFrame with columns: `subject_id`, `period`, `age`, `regime`,
+Returns a pandas DataFrame with columns: `subject_id`, `period`, `age`, `regime_name`,
 `value`, plus all states and actions. Discrete variables are pandas Categorical with
 string labels.
 
@@ -240,7 +240,7 @@ params = {
 
 # 3. Prepare initial conditions as a DataFrame
 initial_df = pd.DataFrame({
-    "regime": "working_life",
+    "regime_name": "working_life",
     "age": model.ages.values[0],
     "wealth": np.linspace(1, 50, 100),
 })
diff --git a/pixi.lock b/pixi.lock
index a73e96fba..d103c693d 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -20,26 +20,26 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/asv_runner-0.2.1-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/async-lru-2.3.0-pyhcf101f3_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/attrs-26.1.0-pyhcf101f3_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-auth-0.10.1-h2d2dd48_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-auth-0.10.1-ha62d5e7_3.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-cal-0.9.13-h2c9d079_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-common-0.12.6-hb03c661_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-compression-0.3.2-h8b1a151_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-event-stream-0.6.0-h9b893ba_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-http-0.10.12-h4bacb7b_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-io-0.26.3-hc87160b_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-mqtt-0.15.2-he9ea9c5_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-s3-0.11.5-h6d69fc9_5.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-event-stream-0.7.0-h9b893ba_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-http-0.10.13-h4bacb7b_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-io-0.26.3-h692f434_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-mqtt-0.15.2-hc1936db_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-s3-0.12.2-he6ee468_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-sdkutils-0.2.4-h8b1a151_4.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-checksums-0.2.10-h8b1a151_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-crt-cpp-0.37.4-h4c8aef7_3.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-sdk-cpp-1.11.747-hc3785e1_3.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-crt-cpp-0.38.3-h745e52d_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-sdk-cpp-1.11.747-h41c0014_4.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-core-cpp-1.16.2-h206d751_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-identity-cpp-1.13.3-hed0cdb0_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-blobs-cpp-12.16.0-hdd73cc9_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-common-cpp-12.12.0-ha7a2c86_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-files-datalake-cpp-12.14.0-h52c5a47_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-blobs-cpp-12.16.0-hf824e48_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-common-cpp-12.13.0-ha7a2c86_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-files-datalake-cpp-12.14.0-h539c000_2.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/babel-2.18.0-pyhcf101f3_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/backports.zstd-1.3.0-py314h680f03e_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/backports.zstd-1.5.0-py314h680f03e_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/beautifulsoup4-4.14.3-pyha770c72_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.45.1-default_hfdba357_102.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_linux-64-2.45.1-default_h4852527_102.conda
@@ -77,23 +77,23 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-nvvm-tools-12.9.86-h4bc722e_2.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-version-12.9-h4f385c5_3.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/debugpy-1.8.20-py314h42812f9_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/decorator-5.2.1-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/decorator-5.3.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/defusedxml-0.7.1-pyhd8ed1ab_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/distlib-0.4.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.1-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/executing-2.2.1-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/filelock-3.29.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/fqdn-1.5.1-pyhd8ed1ab_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_impl_linux-64-14.3.0-hbdf3cc3_18.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_linux-64-14.3.0-h298d278_23.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_impl_linux-64-14.3.0-h235f0fe_19.conda
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   license: Apache-2.0
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   license: Apache-2.0
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   license: Apache-2.0
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   license: Apache-2.0
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-  - liblapacke 3.11.0   6*_mkl
-  - liblapack  3.11.0   6*_mkl
-  - libcblas   3.11.0   6*_mkl
+  - blas 2.307   mkl
+  - libcblas   3.11.0   7*_mkl
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   license: BSD-3-Clause
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 - conda: https://conda.anaconda.org/conda-forge/linux-64/libbrotlicommon-1.2.0-hb03c661_1.conda
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-  - liblapack  3.11.0   6*_openblas
-  - liblapacke 3.11.0   6*_openblas
+  - liblapacke 3.11.0   7*_openblas
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   license_family: Apache
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   license_family: Apache
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-  md5: 9d978822b57bafe72ebd3f8b527bba71
+- conda: https://conda.anaconda.org/conda-forge/linux-64/s2n-1.7.2-hc5a330e_1.conda
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+  md5: 3f578c7d2b0bb52469340e4060d48d94
   depends:
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   - libgcc >=14
-  - openssl >=3.5.5,<4.0a0
+  - openssl >=3.5.6,<4.0a0
   license: Apache-2.0
   license_family: Apache
   purls: []
-  size: 395083
-  timestamp: 1773251675551
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 - conda: https://conda.anaconda.org/conda-forge/linux-64/scipy-1.17.1-py314hf07bd8e_0.conda
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   md5: d0510124f87c75403090e220db1e9d41
@@ -14843,7 +15016,7 @@ packages:
   license: BSD-3-Clause
   license_family: BSD
   purls:
-  - pkg:pypi/scipy?source=compressed-mapping
+  - pkg:pypi/scipy?source=hash-mapping
   size: 17225275
   timestamp: 1771880751368
 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/scipy-1.17.1-py314hfc1f868_0.conda
@@ -14954,7 +15127,7 @@ packages:
   license: MIT
   license_family: MIT
   purls:
-  - pkg:pypi/setuptools?source=compressed-mapping
+  - pkg:pypi/setuptools?source=hash-mapping
   size: 639697
   timestamp: 1773074868565
 - conda: https://conda.anaconda.org/conda-forge/noarch/six-1.17.0-pyhe01879c_1.conda
@@ -15119,10 +15292,10 @@ packages:
   purls: []
   size: 24008591
   timestamp: 1765578833462
-- pypi: https://files.pythonhosted.org/packages/65/44/bb509c3d2c0b5a87e7a5af1d5917a402a32ff026f777a6d7cb6990746cbb/tabcompleter-1.4.0-py3-none-any.whl
+- pypi: https://files.pythonhosted.org/packages/cb/fc/8c82be70b8f96d09943360f34cfb2ecdd3035294c51bce4131eeabe56645/tabcompleter-1.4.1-py3-none-any.whl
   name: tabcompleter
-  version: 1.4.0
-  sha256: d744aa735b49c0a6cc2fb8fcd40077fec47425e4388301010b14e6ce3311368b
+  version: 1.4.1
+  sha256: 26b5cf330a48f32625b00e1664aa589f67c8e98275b6d9c2b85d19917dac1601
   requires_dist:
   - pyreadline3 ; sys_platform == 'win32'
   requires_python: '>=3.8'
@@ -15138,19 +15311,18 @@ packages:
   - pkg:pypi/tabulate?source=hash-mapping
   size: 43964
   timestamp: 1772732795746
-- conda: https://conda.anaconda.org/conda-forge/win-64/tbb-2022.3.0-h3155e25_2.conda
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-  md5: 0f9817ffbe25f9e69ceba5ea70c52606
+- conda: https://conda.anaconda.org/conda-forge/win-64/tbb-2023.0.0-hd3d4ead_2.conda
+  sha256: 8a4053839b8e997a5965e2dff7d6cf3c77be62d82c0e48c8a04a5ed2d2e73035
+  md5: 8ee01a693aecff5432069eaaf1183c45
   depends:
-  - libhwloc >=2.12.2,<2.12.3.0a0
+  - libhwloc >=2.13.0,<2.13.1.0a0
   - ucrt >=10.0.20348.0
   - vc >=14.3,<15
   - vc14_runtime >=14.44.35208
   license: Apache-2.0
-  license_family: APACHE
   purls: []
-  size: 155869
-  timestamp: 1767886839029
+  size: 156515
+  timestamp: 1778673901757
 - conda: https://conda.anaconda.org/conda-forge/noarch/terminado-0.18.1-pyh6dadd2b_1.conda
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@@ -15242,6 +15414,11 @@ packages:
   - pkg:pypi/tomli?source=hash-mapping
   size: 21561
   timestamp: 1774492402955
+- pypi: https://files.pythonhosted.org/packages/c7/18/c86eb8e0202e32dd3df50d43d7ff9854f8e0603945ff398974c1d91ac1ef/tomli_w-1.2.0-py3-none-any.whl
+  name: tomli-w
+  version: 1.2.0
+  sha256: 188306098d013b691fcadc011abd66727d3c414c571bb01b1a174ba8c983cf90
+  requires_python: '>=3.9'
 - conda: https://conda.anaconda.org/conda-forge/linux-64/tornado-6.5.5-py314h5bd0f2a_0.conda
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@@ -15285,39 +15462,39 @@ packages:
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   timestamp: 1774358183098
-- conda: https://conda.anaconda.org/conda-forge/noarch/traitlets-5.14.3-pyhd8ed1ab_1.conda
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+- conda: https://conda.anaconda.org/conda-forge/noarch/traitlets-5.15.0-pyhcf101f3_0.conda
+  sha256: dfb681579be59c2e790c95f7f49b7529a9b0511d6385ad276e3c8988cbd54d2c
+  md5: 4bada6a6d908a27262af8ebddf4f7492
   depends:
-  - python >=3.9
+  - python >=3.10
+  - python
   license: BSD-3-Clause
   license_family: BSD
   purls:
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-  size: 110051
-  timestamp: 1733367480074
-- conda: https://conda.anaconda.org/conda-forge/linux-64/ty-0.0.32-h4e94fc0_0.conda
+  size: 115165
+  timestamp: 1778074251714
+- conda: https://conda.anaconda.org/conda-forge/linux-64/ty-0.0.37-h4e94fc0_0.conda
   noarch: python
-  sha256: 5bfdb00561609b5c065a3b8cce3f95dd2c66de4cca9150d7b7819979b4ffe518
-  md5: 4e3454fe01079cc508d75ba1b7fcd916
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+  md5: a9d531eb76731b39c6919a907ec74153
   depends:
   - python
-  - __glibc >=2.17,<3.0.a0
   - libgcc >=14
+  - __glibc >=2.17,<3.0.a0
   - _python_abi3_support 1.*
   - cpython >=3.10
   constrains:
   - __glibc >=2.17
   license: MIT
-  license_family: MIT
   purls:
   - pkg:pypi/ty?source=hash-mapping
-  size: 9600067
-  timestamp: 1776721452584
-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/ty-0.0.32-hdfcc030_0.conda
+  size: 9809531
+  timestamp: 1778924736785
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/ty-0.0.37-hdfcc030_0.conda
   noarch: python
-  sha256: df97a07d76c93cb5849b22422e05ef94e1b17b659c8cc6078d2abdf0ebeb09e7
-  md5: 318569c90d67843f52be5570a88e494f
+  sha256: 20ec9265a68590829049db32ea4cdb8f62694fce33515962eebb21ee1b96b379
+  md5: 8ba816369178d668776075e749214c38
   depends:
   - python
   - __osx >=11.0
@@ -15326,15 +15503,14 @@ packages:
   constrains:
   - __osx >=11.0
   license: MIT
-  license_family: MIT
   purls:
   - pkg:pypi/ty?source=hash-mapping
-  size: 8649328
-  timestamp: 1776721470806
-- conda: https://conda.anaconda.org/conda-forge/win-64/ty-0.0.32-hc21aad4_0.conda
+  size: 8832640
+  timestamp: 1778924753670
+- conda: https://conda.anaconda.org/conda-forge/win-64/ty-0.0.37-hc21aad4_0.conda
   noarch: python
-  sha256: 32ddaab3bf4a6f6a70b15ac477140f2a2f00ad016e998228e1ba0eb4745af4af
-  md5: a54c912498faf708aefbd1a9ae111a79
+  sha256: 9c254e409ea4d419263b0b6432ad908dfb01105dca50e287523115a4b2127179
+  md5: bd5d50945659ab7b1c90a49fdf14ead7
   depends:
   - python
   - vc >=14.3,<15
@@ -15343,21 +15519,21 @@ packages:
   - _python_abi3_support 1.*
   - cpython >=3.10
   license: MIT
-  license_family: MIT
   purls:
   - pkg:pypi/ty?source=hash-mapping
-  size: 9634314
-  timestamp: 1776721486913
-- conda: https://conda.anaconda.org/conda-forge/noarch/types-pytz-2026.1.1.20260408-pyhd8ed1ab_0.conda
-  sha256: f7dca54c2e9b1ec9c8a12931128a78b3a75c4b511dd2688b1f06bfe8880df3ae
-  md5: 792307c4c22804c7ce39900e518f0b20
+  size: 9843839
+  timestamp: 1778924768700
+- conda: https://conda.anaconda.org/conda-forge/noarch/types-pytz-2026.2.0.20260506-pyhcf101f3_0.conda
+  sha256: 7c4a1234a4ce213a65574f6080b41693965d00892edce77237ea110efb98a805
+  md5: 464d6104d7802b4c92c39c0a6b19370f
   depends:
   - python >=3.10
+  - python
   license: Apache-2.0 AND MIT
   purls:
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-  size: 20040
-  timestamp: 1775643470173
+  size: 20355
+  timestamp: 1778054628795
 - conda: https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.15.0-h396c80c_0.conda
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   md5: edd329d7d3a4ab45dcf905899a7a6115
@@ -15494,9 +15670,9 @@ packages:
   - pkg:pypi/uri-template?source=hash-mapping
   size: 23990
   timestamp: 1733323714454
-- conda: https://conda.anaconda.org/conda-forge/noarch/urllib3-2.6.3-pyhd8ed1ab_0.conda
-  sha256: af641ca7ab0c64525a96fd9ad3081b0f5bcf5d1cbb091afb3f6ed5a9eee6111a
-  md5: 9272daa869e03efe68833e3dc7a02130
+- conda: https://conda.anaconda.org/conda-forge/noarch/urllib3-2.7.0-pyhd8ed1ab_0.conda
+  sha256: feff959a816f7988a0893201aa9727bbb7ee1e9cec2c4f0428269b489eb93fb4
+  md5: cbb88288f74dbe6ada1c6c7d0a97223e
   depends:
   - backports.zstd >=1.0.0
   - brotli-python >=1.2.0
@@ -15507,48 +15683,48 @@ packages:
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-  size: 103172
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-  md5: 1e610f2416b6acdd231c5f573d754a0f
+  size: 103560
+  timestamp: 1778188657149
+- conda: https://conda.anaconda.org/conda-forge/win-64/vc-14.5-h1b7c187_36.conda
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+  md5: 10eac3d81ceea1be614f1d90045c7e9b
   depends:
-  - vc14_runtime >=14.44.35208
+  - vc14_runtime >=14.51.36231
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   license: BSD-3-Clause
   license_family: BSD
   purls: []
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-- conda: https://conda.anaconda.org/conda-forge/win-64/vc14_runtime-14.44.35208-h818238b_34.conda
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+- conda: https://conda.anaconda.org/conda-forge/win-64/vc14_runtime-14.51.36231-h1b9f54f_36.conda
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-  - vcomp14 14.44.35208 h818238b_34
+  - vcomp14 14.51.36231 h1b9f54f_36
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-  - vs2015_runtime 14.44.35208.* *_34
+  - vs2015_runtime 14.51.36231.* *_36
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+- conda: https://conda.anaconda.org/conda-forge/win-64/vcomp14-14.51.36231-h1b9f54f_36.conda
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-  - vs2015_runtime 14.44.35208.* *_34
+  - vs2015_runtime 14.51.36231.* *_36
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   license_family: Proprietary
   purls: []
-  size: 115235
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-- conda: https://conda.anaconda.org/conda-forge/noarch/virtualenv-21.2.4-pyhcf101f3_0.conda
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-  md5: 15be1b64e7a4501abb4f740c28ceadaf
+  size: 124124
+  timestamp: 1779061850036
+- conda: https://conda.anaconda.org/conda-forge/noarch/virtualenv-21.3.3-pyhcf101f3_0.conda
+  sha256: 67a5a8b0976f11bd821909dd7ffd393ae32879ec22be622344277f04a1450aff
+  md5: a678237f7d0db44f8a20a21f03844613
   depends:
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   - distlib >=0.3.7,<1
@@ -15559,21 +15735,20 @@ packages:
   - typing_extensions >=4.13.2
   - python
   license: MIT
-  license_family: MIT
   purls:
-  - pkg:pypi/virtualenv?source=compressed-mapping
-  size: 4659433
-  timestamp: 1776247061232
-- conda: https://conda.anaconda.org/conda-forge/win-64/vs2015_runtime-14.44.35208-h38c0c73_34.conda
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-  md5: f276d1de4553e8fca1dfb6988551ebb4
+  - pkg:pypi/virtualenv?source=hash-mapping
+  size: 5154878
+  timestamp: 1778710685928
+- conda: https://conda.anaconda.org/conda-forge/win-64/vs2015_runtime-14.51.36231-h84cd919_36.conda
+  sha256: 720d9bbbd0cd48f80d1b295fd6eb9f2e4accade35226470db97b6f09745df149
+  md5: d4e73c2ddb98e1071de99e37f4499c6c
   depends:
-  - vc14_runtime >=14.44.35208
+  - vc14_runtime >=14.51.36231
   license: BSD-3-Clause
   license_family: BSD
   purls: []
-  size: 19347
-  timestamp: 1767320221943
+  size: 20247
+  timestamp: 1779061872946
 - pypi: https://files.pythonhosted.org/packages/8d/96/04e7b441807b26b794da5b11e59ed7f83b2cf8af202bd7eba8ad2fa6046e/wadler_lindig-0.1.7-py3-none-any.whl
   name: wadler-lindig
   version: 0.1.7
@@ -15604,17 +15779,17 @@ packages:
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   size: 334139
   timestamp: 1773959575393
-- conda: https://conda.anaconda.org/conda-forge/noarch/wcwidth-0.6.0-pyhd8ed1ab_0.conda
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-  md5: c3197f8c0d5b955c904616b716aca093
+- conda: https://conda.anaconda.org/conda-forge/noarch/wcwidth-0.7.0-pyhd8ed1ab_0.conda
+  sha256: 1ee2d8384972ecbf8630ce8a3ea9d16858358ad3e8566675295e66996d5352da
+  md5: eb9538b8e55069434a18547f43b96059
   depends:
   - python >=3.10
   license: MIT
   license_family: MIT
   purls:
   - pkg:pypi/wcwidth?source=hash-mapping
-  size: 71550
-  timestamp: 1770634638503
+  size: 82917
+  timestamp: 1777744489106
 - conda: https://conda.anaconda.org/conda-forge/noarch/webcolors-25.10.0-pyhd8ed1ab_0.conda
   sha256: 21f6c8a20fe050d09bfda3fb0a9c3493936ce7d6e1b3b5f8b01319ee46d6c6f6
   md5: 6639b6b0d8b5a284f027a2003669aa65
diff --git a/pyproject.toml b/pyproject.toml
index 89846aec9..4bf957a61 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -29,8 +29,10 @@ classifiers = [
 ]
 dynamic = [ "version" ]
 dependencies = [
+  "beartype>=0.21",
   "cloudpickle>=3.1.2",
-  "dags>=0.5.1",
+  # Require the beartype fix, replace once release is there
+  # "dags>=0.6.0",
   "h5py>=3.12",
   "jax>=0.9",
   "jaxtyping>=0.3.2",
@@ -98,7 +100,7 @@ tests-cuda13 = { features = [ "tests", "cuda13" ], solve-group = "cuda13" }
 tests-metal = { features = [ "tests", "metal" ], solve-group = "metal" }
 type-checking = { features = [ "type-checking", "tests" ], solve-group = "default" }
 [tool.pixi.feature.benchmarks.pypi-dependencies]
-aca-model = { git = "https://github.com/OpenSourceEconomics/aca-model.git", rev = "b807b286175e00f8a721f039f4afd657bbb10e2f" }
+aca-model = { git = "https://github.com/OpenSourceEconomics/aca-model.git", rev = "bce9101b049f4088789c19be14ab7b436daf44f7" }
 [tool.pixi.feature.cuda12]
 platforms = [ "linux-64" ]
 system-requirements = { cuda = "12" }
@@ -148,6 +150,8 @@ ty = "ty check"
 jax = ">=0.9"
 pdbp = "*"
 pylcm = { path = ".", editable = true }
+# Remove and comment in `dags >= 0.6.0` above once released.
+dags = { git = "https://github.com/OpenSourceEconomics/dags.git", branch = "main" }
 [tool.pixi.tasks]
 asv-compare = "asv compare"
 asv-preview = "asv preview"
diff --git a/src/lcm/__init__.py b/src/lcm/__init__.py
index d6d267568..4cce08bf8 100644
--- a/src/lcm/__init__.py
+++ b/src/lcm/__init__.py
@@ -25,13 +25,31 @@
 
 import jax
 
+# Patch jaxtyping's `"..."` sentinel to survive pickling before any
+# `jaxtyping`-subscripted type is created (see the module docstring).
+from lcm import _jaxtyping_patch  # noqa: F401
+
 with contextlib.suppress(ImportError):
     import pdbp  # noqa: F401
 
-from lcm import shocks
-from lcm._version import __version__
-from lcm.ages import AgeGrid
-from lcm.grids import (
+# Register beartype's package claw before any submodule import so every
+# `lcm.*` module loads with runtime type checks installed via
+# `INTERNAL_CONF`. User-facing constructors stack an explicit
+# `@beartype(conf=...)` decorator that maps violations to the relevant
+# project exception (see `lcm._beartype_conf`).
+from beartype.claw import beartype_package
+
+from lcm._beartype_conf import INTERNAL_CONF
+
+beartype_package("lcm", conf=INTERNAL_CONF)
+
+# Modules with TYPE_CHECKING-only forward references expose a
+# `_bind_forward_refs` helper; calling it here makes the claw's
+# rewritten string annotations resolve at call time.
+from lcm import shocks  # noqa: E402
+from lcm._version import __version__  # noqa: E402
+from lcm.ages import AgeGrid  # noqa: E402
+from lcm.grids import (  # noqa: E402
     DiscreteGrid,
     IrregSpacedGrid,
     LinSpacedGrid,
@@ -41,19 +59,29 @@
     PiecewiseLogSpacedGrid,
     categorical,
 )
-from lcm.interfaces import SolveSimulateFunctionPair
-from lcm.model import Model
-from lcm.persistence import (
+from lcm.interfaces import SolveSimulateFunctionPair  # noqa: E402
+from lcm.model import Model  # noqa: E402
+from lcm.persistence import (  # noqa: E402
     SimulateSnapshot,
     SolveSnapshot,
     load_snapshot,
     load_solution,
     save_solution,
 )
-from lcm.regime import MarkovTransition, Regime
-from lcm.simulation.result import SimulationResult
-from lcm.utils.containers import invert_regime_ids
-from lcm.utils.error_handling import validate_transition_probs
+from lcm.persistence import (  # noqa: E402
+    _bind_forward_refs as _bind_persistence_forward_refs,
+)
+from lcm.regime import MarkovTransition, Regime  # noqa: E402
+from lcm.simulation.result import SimulationResult  # noqa: E402
+from lcm.utils.containers import invert_regime_ids  # noqa: E402
+from lcm.utils.error_handling import validate_transition_probs  # noqa: E402
+from lcm.variables import (  # noqa: E402
+    _bind_forward_refs as _bind_variables_forward_refs,
+)
+
+_bind_variables_forward_refs(regime_cls=Regime)
+_bind_persistence_forward_refs(model_cls=Model, simulation_result_cls=SimulationResult)
+del _bind_persistence_forward_refs, _bind_variables_forward_refs
 
 # Register MappingProxyType as a JAX pytree so it can be used in JIT-traced functions.
 # This allows regime transition probabilities to use immutable mappings.
diff --git a/src/lcm/_beartype_conf.py b/src/lcm/_beartype_conf.py
new file mode 100644
index 000000000..440a13e36
--- /dev/null
+++ b/src/lcm/_beartype_conf.py
@@ -0,0 +1,69 @@
+"""`BeartypeConf` instances for pylcm's perimeter and internal claws.
+
+`INTERNAL_CONF` is the default conf for the `lcm` package-wide claw
+registered in `lcm/__init__.py`. Violations under that claw surface as
+beartype's own `BeartypeCallHintViolation`, marking them as internal
+pylcm bugs rather than user error.
+
+The remaining confs (`MODEL_CONF`, `REGIME_CONF`, `GRID_CONF`,
+`PARAMS_CONF`, `CATEGORICAL_CONF`) are used by explicit
+`@beartype(conf=...)` decorators on user-facing constructors and entry
+points. They map type violations to the existing project exception
+class, preserving the documented exception hierarchy at the user
+boundary. The decorators stack on top of the package claw and take
+precedence at the call sites they cover.
+
+"""
+
+from beartype import BeartypeConf, BeartypeStrategy
+
+from lcm.exceptions import (
+    CategoricalDefinitionError,
+    GridInitializationError,
+    InvalidParamsError,
+    ModelInitializationError,
+    RegimeInitializationError,
+)
+
+
+def _conf(exc: type[Exception]) -> BeartypeConf:
+    # `On` strategy: full O(n) container validation so every bad entry in a
+    # mapping/sequence is reported, not just one sampled element. The
+    # decorated entry points are called rarely (construction, solve,
+    # simulate), so per-call cost is invisible.
+    # `is_pep484_tower=True`: respect the PEP-484 numeric tower so `int`
+    # satisfies `float`-typed parameters (matches the implicit numeric
+    # conversion that Python and ruff's PYI041 both assume).
+    return BeartypeConf(
+        violation_param_type=exc,
+        strategy=BeartypeStrategy.On,
+        is_pep484_tower=True,
+    )
+
+
+# Used on `Regime` and `MarkovTransition`.
+REGIME_CONF = _conf(RegimeInitializationError)
+
+# Used on `Model`.
+MODEL_CONF = _conf(ModelInitializationError)
+
+# Used on all grid and shock-grid constructors.
+GRID_CONF = _conf(GridInitializationError)
+
+# Used on the `categorical` decorator factory.
+CATEGORICAL_CONF = _conf(CategoricalDefinitionError)
+
+# Used on `Model.solve`, `Model.simulate`, and the `as_leaf` factory.
+PARAMS_CONF = _conf(InvalidParamsError)
+
+# Default conf for the package-wide claw on `lcm` registered in
+# `lcm/__init__.py`. A type violation in any internal helper surfaces as
+# beartype's own `BeartypeCallHintViolation` rather than a project
+# exception. User-facing constructors layer their own
+# `@beartype(conf=...)` decorators on top to map violations to project
+# exceptions; those decorators take precedence at the call sites they
+# cover.
+INTERNAL_CONF = BeartypeConf(
+    strategy=BeartypeStrategy.On,
+    is_pep484_tower=True,
+)
diff --git a/src/lcm/_jaxtyping_patch.py b/src/lcm/_jaxtyping_patch.py
new file mode 100644
index 000000000..e9dced877
--- /dev/null
+++ b/src/lcm/_jaxtyping_patch.py
@@ -0,0 +1,36 @@
+"""Make jaxtyping's anonymous-variadic-dim sentinel survive pickling.
+
+jaxtyping marks a `"..."` axis with a module-level `object()` sentinel
+(`_anonymous_variadic_dim`). A plain `object()` does not keep its identity
+across a pickle round-trip, so cloudpickling a value whose type annotations
+reference a `Foo[Array, "..."]` type — which the beartype claw makes
+pervasive — yields a type whose variadic-dim marker no longer matches the
+live module global. jaxtyping's shape check then trips
+`assert type(variadic_dim) is _NamedVariadicDim`.
+
+Replacing the sentinel with a `__reduce__`-backed singleton makes it
+round-trip to the same object, so unpickled annotation types stay valid.
+This module must be imported before any `jaxtyping`-subscripted type is
+created — `lcm/__init__.py` imports it before every other `lcm` submodule.
+"""
+
+from typing import Self
+
+from jaxtyping import _array_types
+
+
+class _AnonymousVariadicDim:
+    """Picklable singleton for jaxtyping's `"..."` axis marker."""
+
+    _instance: Self | None = None
+
+    def __new__(cls) -> Self:
+        if cls._instance is None:
+            cls._instance = super().__new__(cls)
+        return cls._instance
+
+    def __reduce__(self) -> tuple[type[_AnonymousVariadicDim], tuple[()]]:
+        return (_AnonymousVariadicDim, ())
+
+
+_array_types._anonymous_variadic_dim = _AnonymousVariadicDim()  # noqa: SLF001
diff --git a/src/lcm/ages.py b/src/lcm/ages.py
index 11d2c88c7..b7736951f 100644
--- a/src/lcm/ages.py
+++ b/src/lcm/ages.py
@@ -8,9 +8,11 @@
 from typing import overload
 
 import jax.numpy as jnp
+from beartype import beartype
 
+from lcm._beartype_conf import GRID_CONF
 from lcm.exceptions import GridInitializationError, format_messages
-from lcm.typing import Float1D, Int1D
+from lcm.typing import Float1D, Int1D, UserAge
 
 STEP_UNITS: MappingProxyType[str, Fraction] = MappingProxyType(
     {
@@ -38,8 +40,8 @@ class AgeGrid:
     def __init__(
         self,
         *,
-        start: int | Fraction,
-        stop: int | Fraction,
+        start: UserAge,
+        stop: UserAge,
         step: str,
     ) -> None: ...
 
@@ -47,16 +49,17 @@ def __init__(
     def __init__(
         self,
         *,
-        exact_values: Iterable[int | Fraction],
+        exact_values: Iterable[UserAge],
     ) -> None: ...
 
+    @beartype(conf=GRID_CONF)
     def __init__(
         self,
         *,
-        start: int | Fraction | None = None,
-        stop: int | Fraction | None = None,
+        start: UserAge | None = None,
+        stop: UserAge | None = None,
         step: str | None = None,
-        exact_values: Iterable[int | Fraction] | None = None,
+        exact_values: Iterable[UserAge] | None = None,
     ) -> None:
         _validate_age_grid(start=start, stop=stop, step=step, exact_values=exact_values)
 
@@ -97,7 +100,7 @@ def values(self) -> Int1D | Float1D:
         return self._values
 
     @property
-    def exact_values(self) -> tuple[int | Fraction, ...]:
+    def exact_values(self) -> tuple[UserAge, ...]:
         """Exact ages; indexed by period.
 
         Could be:
@@ -221,10 +224,10 @@ def _is_integer_valued(value: int | Fraction) -> bool:
 
 def _validate_age_grid(
     *,
-    start: int | Fraction | None,
-    stop: int | Fraction | None,
+    start: UserAge | None,
+    stop: UserAge | None,
     step: str | None,
-    exact_values: Iterable[int | Fraction] | None,
+    exact_values: Iterable[UserAge] | None,
 ) -> None:
     error_messages: list[str] = []
 
@@ -249,9 +252,7 @@ def _validate_age_grid(
         raise GridInitializationError(format_messages(error_messages))
 
 
-def _validate_range(
-    *, start: int | Fraction, stop: int | Fraction, step: str
-) -> list[str]:
+def _validate_range(*, start: UserAge, stop: UserAge, step: str) -> list[str]:
     errors: list[str] = []
 
     if start >= stop:
@@ -282,7 +283,7 @@ def _validate_range(
     return errors
 
 
-def _validate_values(values: Iterable[int | Fraction]) -> list[str]:
+def _validate_values(values: Iterable[UserAge]) -> list[str]:
     errors: list[str] = []
 
     try:
diff --git a/src/lcm/dtypes.py b/src/lcm/dtypes.py
index f2cf31708..45e7f46a7 100644
--- a/src/lcm/dtypes.py
+++ b/src/lcm/dtypes.py
@@ -11,24 +11,27 @@
 import jax
 import jax.numpy as jnp
 import numpy as np
-from jax import Array
+
+from lcm.typing import FloatND, IntND
 
 _INT32_MIN = int(np.iinfo(np.int32).min)
 _INT32_MAX = int(np.iinfo(np.int32).max)
 _FLOAT32_MAX = float(np.finfo(np.float32).max)
 
 
-def canonical_float_dtype() -> jnp.dtype:
+def canonical_float_dtype() -> type:
     """Return pylcm's canonical float dtype, derived from `jax_enable_x64`.
 
     Returns `jnp.float64` if `jax.config.jax_enable_x64` is True,
-    otherwise `jnp.float32`. The value is read at call time, not at
-    import, so toggling the JAX config (e.g. between tests) is honoured.
+    otherwise `jnp.float32`. The return is a JAX scalar type, passable
+    wherever JAX/NumPy accept a dtype-like specifier. The value is read
+    at call time, not at import, so toggling the JAX config (e.g.
+    between tests) is honoured.
     """
     return jnp.float64 if jax.config.read("jax_enable_x64") else jnp.float32
 
 
-def safe_to_int_dtype(value: object, *, name: str) -> Array:
+def safe_to_int_dtype(value: object, *, name: str) -> IntND:
     """Cast a scalar, sequence, or array to `jnp.int32`, checking int32 range.
 
     Args:
@@ -58,7 +61,7 @@ def safe_to_int_dtype(value: object, *, name: str) -> Array:
     return jnp.asarray(np_value, dtype=jnp.int32)
 
 
-def safe_to_float_dtype(value: object, *, name: str) -> Array:
+def safe_to_float_dtype(value: object, *, name: str) -> FloatND:
     """Cast a scalar, sequence, or array to the canonical float dtype.
 
     Range check fires only on a down-cast:
diff --git a/src/lcm/grids/categorical.py b/src/lcm/grids/categorical.py
index b45834424..618925b81 100644
--- a/src/lcm/grids/categorical.py
+++ b/src/lcm/grids/categorical.py
@@ -5,7 +5,9 @@
 import jax
 import jax.numpy as jnp
 import pandas as pd
+from beartype import beartype
 
+from lcm._beartype_conf import CATEGORICAL_CONF
 from lcm.exceptions import (
     CategoricalDefinitionError,
     GridInitializationError,
@@ -15,6 +17,7 @@
 from lcm.utils.containers import find_duplicates, get_field_names_and_values
 
 
+@beartype(conf=CATEGORICAL_CONF)
 def categorical[T](*, ordered: bool) -> Callable[[type[T]], type[T]]:
     """Create a categorical class with auto-assigned `ScalarInt` values.
 
diff --git a/src/lcm/grids/continuous.py b/src/lcm/grids/continuous.py
index 9a7678eb4..0c7865d92 100644
--- a/src/lcm/grids/continuous.py
+++ b/src/lcm/grids/continuous.py
@@ -2,17 +2,19 @@
 from abc import ABC, abstractmethod
 from collections.abc import Sequence
 from dataclasses import dataclass
-from typing import overload
+from typing import Any
 
 import jax.numpy as jnp
-from jax import Array
+from beartype import beartype
 
+from lcm._beartype_conf import GRID_CONF
 from lcm.dtypes import canonical_float_dtype
 from lcm.exceptions import GridInitializationError, format_messages
 from lcm.grids import coordinates as grid_coordinates
 from lcm.grids.base import Grid
 from lcm.typing import (
     Float1D,
+    FloatND,
     ScalarFloat,
     ScalarInt,
 )
@@ -32,12 +34,8 @@ class ContinuousGrid(Grid):
     distributed: bool = False
     """Whether to distribute the grid over the available devices."""
 
-    @overload
-    def get_coordinate(self, value: ScalarFloat) -> ScalarFloat: ...
-    @overload
-    def get_coordinate(self, value: Array) -> Array: ...
     @abstractmethod
-    def get_coordinate(self, value: ScalarFloat | Array) -> ScalarFloat | Array:
+    def get_coordinate(self, value: FloatND) -> FloatND:
         """Return the generalized coordinate of a value in the grid."""
 
 
@@ -59,6 +57,7 @@ class UniformContinuousGrid(ContinuousGrid, ABC):
     n_points: ScalarInt
     """The number of points in the grid (`jnp.int32` JAX scalar)."""
 
+    @beartype(conf=GRID_CONF)
     def __init__(
         self,
         *,
@@ -82,12 +81,8 @@ def __init__(
     def to_jax(self) -> Float1D:
         """Convert the grid to a Jax array."""
 
-    @overload
-    def get_coordinate(self, value: ScalarFloat) -> ScalarFloat: ...
-    @overload
-    def get_coordinate(self, value: Array) -> Array: ...
     @abstractmethod
-    def get_coordinate(self, value: ScalarFloat | Array) -> ScalarFloat | Array:
+    def get_coordinate(self, value: FloatND) -> FloatND:
         """Return the generalized coordinate of a value in the grid."""
 
     def replace(self, **kwargs: float) -> UniformContinuousGrid:
@@ -123,11 +118,7 @@ def to_jax(self) -> Float1D:
             start=self.start, stop=self.stop, n_points=self.n_points
         )
 
-    @overload
-    def get_coordinate(self, value: ScalarFloat) -> ScalarFloat: ...
-    @overload
-    def get_coordinate(self, value: Array) -> Array: ...
-    def get_coordinate(self, value: ScalarFloat | Array) -> ScalarFloat | Array:
+    def get_coordinate(self, value: FloatND) -> FloatND:
         """Return the generalized coordinate of a value in the grid."""
         return grid_coordinates.get_linspace_coordinate(
             value=value,
@@ -148,6 +139,7 @@ class LogSpacedGrid(UniformContinuousGrid):
 
     """
 
+    @beartype(conf=GRID_CONF)
     def __init__(
         self,
         *,
@@ -173,11 +165,7 @@ def to_jax(self) -> Float1D:
             start=self.start, stop=self.stop, n_points=self.n_points
         )
 
-    @overload
-    def get_coordinate(self, value: ScalarFloat) -> ScalarFloat: ...
-    @overload
-    def get_coordinate(self, value: Array) -> Array: ...
-    def get_coordinate(self, value: ScalarFloat | Array) -> ScalarFloat | Array:
+    def get_coordinate(self, value: FloatND) -> FloatND:
         """Return the generalized coordinate of a value in the grid."""
         return grid_coordinates.get_logspace_coordinate(
             value=value,
@@ -248,6 +236,7 @@ class IrregSpacedGrid(ContinuousGrid):
     n_points: int
     """Number of points. Derived from `len(points)` when points are given."""
 
+    @beartype(conf=GRID_CONF)
     def __init__(
         self,
         *,
@@ -308,11 +297,7 @@ def to_jax(self) -> Float1D:
             )
         return self.points
 
-    @overload
-    def get_coordinate(self, value: ScalarFloat) -> ScalarFloat: ...
-    @overload
-    def get_coordinate(self, value: Array) -> Array: ...
-    def get_coordinate(self, value: ScalarFloat | Array) -> ScalarFloat | Array:
+    def get_coordinate(self, value: FloatND) -> FloatND:
         """Return the generalized coordinate of a value in the grid."""
         if self.points is None:
             raise GridInitializationError(
@@ -377,9 +362,16 @@ def _validate_continuous_grid(
         raise GridInitializationError(msg)
 
 
-def _validate_irreg_spaced_grid(points: Sequence[float] | Float1D) -> None:
+def _validate_irreg_spaced_grid(points: Sequence[Any] | Float1D) -> None:
     """Validate the irregular spaced grid parameters.
 
+    The element type is `Any` because the function's manual loop is what
+    surfaces the user-facing `GridInitializationError` for non-numeric
+    entries; tightening to `Sequence[float]` makes beartype's package-
+    claw deep-check intercept first (observed flaking under cuda12 +
+    32-bit precision in CI), raising raw `BeartypeCallHintViolation`
+    before the manual check fires.
+
     Args:
         points: The grid points.
 
diff --git a/src/lcm/grids/coordinates.py b/src/lcm/grids/coordinates.py
index dc95b40c5..20e83afbf 100644
--- a/src/lcm/grids/coordinates.py
+++ b/src/lcm/grids/coordinates.py
@@ -1,17 +1,15 @@
 """Functions to generate and work with different kinds of grids.
 
-These helpers operate on JAX scalars (`ScalarFloat` for endpoints/values,
-`ScalarInt` or Python `int` for `n_points`) — every production caller is
-either a `Grid` method that has already converted user input to a JAX
-scalar, or a piecewise dispatch that selects pieces via `searchsorted`.
+The `get_*_coordinate` helpers take JAX arrays of any rank: a `Grid`
+method passes a 0-d value, while a piecewise dispatch selects pieces via
+`searchsorted` and passes `start` / `stop` / `n_points` indexed by an
+N-d `piece_idx`. `linspace` / `logspace` build a grid and take JAX
+scalars.
 """
 
-from typing import overload
-
 import jax.numpy as jnp
-from jax import Array
 
-from lcm.typing import Float1D, ScalarFloat, ScalarInt
+from lcm.typing import Float1D, FloatND, IntND, ScalarFloat, ScalarInt
 
 
 def linspace(
@@ -29,29 +27,13 @@ def linspace(
     return jnp.linspace(start, stop, n_points)  # ty: ignore[no-matching-overload]
 
 
-@overload
 def get_linspace_coordinate(
     *,
-    value: ScalarFloat,
-    start: ScalarFloat,
-    stop: ScalarFloat,
-    n_points: ScalarInt,
-) -> ScalarFloat: ...
-@overload
-def get_linspace_coordinate(
-    *,
-    value: Array,
-    start: ScalarFloat,
-    stop: ScalarFloat,
-    n_points: ScalarInt,
-) -> Array: ...
-def get_linspace_coordinate(
-    *,
-    value: ScalarFloat | Array,
-    start: ScalarFloat,
-    stop: ScalarFloat,
-    n_points: ScalarInt,
-) -> ScalarFloat | Array:
+    value: FloatND,
+    start: FloatND,
+    stop: FloatND,
+    n_points: IntND,
+) -> FloatND:
     """Map a value into the input needed for jax.scipy.ndimage.map_coordinates."""
     step_length = (stop - start) / (n_points - 1)
     return (value - start) / step_length
@@ -83,29 +65,13 @@ def logspace(
     return grid.at[0].set(start).at[-1].set(stop)
 
 
-@overload
 def get_logspace_coordinate(
     *,
-    value: ScalarFloat,
-    start: ScalarFloat,
-    stop: ScalarFloat,
-    n_points: ScalarInt,
-) -> ScalarFloat: ...
-@overload
-def get_logspace_coordinate(
-    *,
-    value: Array,
-    start: ScalarFloat,
-    stop: ScalarFloat,
-    n_points: ScalarInt,
-) -> Array: ...
-def get_logspace_coordinate(
-    *,
-    value: ScalarFloat | Array,
-    start: ScalarFloat,
-    stop: ScalarFloat,
-    n_points: ScalarInt,
-) -> ScalarFloat | Array:
+    value: FloatND,
+    start: FloatND,
+    stop: FloatND,
+    n_points: IntND,
+) -> FloatND:
     """Map a value into the input needed for jax.scipy.ndimage.map_coordinates."""
     # Transform start, stop, and value to linear scale
     start_linear = jnp.log(start)
@@ -143,23 +109,11 @@ def get_logspace_coordinate(
     return rank_lower_gridpoint + decimal_part
 
 
-@overload
-def get_irreg_coordinate(
-    *,
-    value: ScalarFloat,
-    points: Float1D,
-) -> ScalarFloat: ...
-@overload
-def get_irreg_coordinate(
-    *,
-    value: Array,
-    points: Float1D,
-) -> Array: ...
 def get_irreg_coordinate(
     *,
-    value: ScalarFloat | Array,
+    value: FloatND,
     points: Float1D,
-) -> ScalarFloat | Array:
+) -> FloatND:
     """Return the generalized coordinate of a value in an irregularly spaced grid.
 
     Uses binary search (jnp.searchsorted) to find the position of the value among
diff --git a/src/lcm/grids/discrete.py b/src/lcm/grids/discrete.py
index f6fa98043..0852867a0 100644
--- a/src/lcm/grids/discrete.py
+++ b/src/lcm/grids/discrete.py
@@ -1,5 +1,7 @@
 import jax.numpy as jnp
+from beartype import beartype
 
+from lcm._beartype_conf import GRID_CONF
 from lcm.grids.base import Grid
 from lcm.grids.categorical import _validate_discrete_grid
 from lcm.typing import Int1D
@@ -19,6 +21,7 @@ class DiscreteGrid(Grid):
 
     """
 
+    @beartype(conf=GRID_CONF)
     def __init__(
         self, category_class: type, batch_size: int = 0, *, distributed: bool = False
     ) -> None:
diff --git a/src/lcm/grids/piecewise.py b/src/lcm/grids/piecewise.py
index 43ff2a91e..dc3abdead 100644
--- a/src/lcm/grids/piecewise.py
+++ b/src/lcm/grids/piecewise.py
@@ -1,16 +1,17 @@
 import dataclasses
 from dataclasses import dataclass
-from typing import overload
 
 import jax.numpy as jnp
 import portion
-from jax import Array
+from beartype import beartype
 
+from lcm._beartype_conf import GRID_CONF
 from lcm.exceptions import GridInitializationError, format_messages
 from lcm.grids import coordinates as grid_coordinates
 from lcm.grids.continuous import ContinuousGrid
 from lcm.typing import (
     Float1D,
+    FloatND,
     Int1D,
     ScalarFloat,
     ScalarInt,
@@ -34,6 +35,7 @@ class Piece:
     n_points: ScalarInt
     """The number of grid points in this piece (`jnp.int32` JAX scalar)."""
 
+    @beartype(conf=GRID_CONF)
     def __init__(
         self,
         *,
@@ -44,6 +46,7 @@ def __init__(
         object.__setattr__(self, "n_points", jnp.int32(n_points))
 
 
+@beartype(conf=GRID_CONF)
 @dataclass(frozen=True, kw_only=True)
 class PiecewiseLinSpacedGrid(ContinuousGrid):
     """A piecewise linearly spaced grid with multiple segments.
@@ -85,7 +88,7 @@ def __post_init__(self) -> None:
     @property
     def n_points(self) -> ScalarInt:
         """Return the total number of points in the grid."""
-        return self._piece_n_points.sum()
+        return self._piece_n_points.sum(dtype=jnp.int32)
 
     def to_jax(self) -> Float1D:
         """Convert the grid to a Jax array."""
@@ -95,11 +98,7 @@ def to_jax(self) -> Float1D:
         ]
         return jnp.concatenate(piece_arrays)
 
-    @overload
-    def get_coordinate(self, value: ScalarFloat) -> ScalarFloat: ...
-    @overload
-    def get_coordinate(self, value: Array) -> Array: ...
-    def get_coordinate(self, value: ScalarFloat | Array) -> ScalarFloat | Array:
+    def get_coordinate(self, value: FloatND) -> FloatND:
         """Return the generalized coordinate of a value in the grid."""
         piece_idx = jnp.searchsorted(self._breakpoints, value, side="right")
         local_coord = grid_coordinates.get_linspace_coordinate(
@@ -111,6 +110,7 @@ def get_coordinate(self, value: ScalarFloat | Array) -> ScalarFloat | Array:
         return self._cumulative_offsets[piece_idx] + local_coord
 
 
+@beartype(conf=GRID_CONF)
 @dataclass(frozen=True, kw_only=True)
 class PiecewiseLogSpacedGrid(ContinuousGrid):
     """A piecewise logarithmically spaced grid with multiple segments.
@@ -152,7 +152,7 @@ def __post_init__(self) -> None:
     @property
     def n_points(self) -> ScalarInt:
         """Return the total number of points in the grid."""
-        return self._piece_n_points.sum()
+        return self._piece_n_points.sum(dtype=jnp.int32)
 
     def to_jax(self) -> Float1D:
         """Convert the grid to a Jax array."""
@@ -166,11 +166,7 @@ def to_jax(self) -> Float1D:
         ]
         return jnp.concatenate(piece_arrays)
 
-    @overload
-    def get_coordinate(self, value: ScalarFloat) -> ScalarFloat: ...
-    @overload
-    def get_coordinate(self, value: Array) -> Array: ...
-    def get_coordinate(self, value: ScalarFloat | Array) -> ScalarFloat | Array:
+    def get_coordinate(self, value: FloatND) -> FloatND:
         """Return the generalized coordinate of a value in the grid."""
         piece_idx = jnp.searchsorted(self._breakpoints, value, side="right")
         local_coord = grid_coordinates.get_logspace_coordinate(
@@ -236,8 +232,10 @@ def _init_piecewise_grid_cache(
     # Breakpoints are the effective starts of pieces 1..k-1
     breakpoints = starts[1:] if len(starts) > 1 else jnp.array([])
 
-    n_points = jnp.array([p.n_points for p in grid.pieces])
-    cumulative = jnp.concatenate([jnp.array([0]), jnp.cumsum(n_points[:-1])])
+    n_points = jnp.array([p.n_points for p in grid.pieces], dtype=jnp.int32)
+    cumulative = jnp.concatenate(
+        [jnp.array([0], dtype=jnp.int32), jnp.cumsum(n_points[:-1])]
+    )
 
     object.__setattr__(grid, "_breakpoints", breakpoints)
     object.__setattr__(grid, "_piece_starts", starts)
diff --git a/src/lcm/interfaces.py b/src/lcm/interfaces.py
index d4288f4f7..dbb2e0745 100644
--- a/src/lcm/interfaces.py
+++ b/src/lcm/interfaces.py
@@ -19,11 +19,17 @@
     DiscreteAction,
     DiscreteState,
     FlatRegimeParams,
+    Float1D,
+    FloatND,
     FunctionsMapping,
+    IntND,
     MaxQOverAFunction,
     NextStateSimulationFunction,
+    RegimeName,
     RegimeParamsTemplate,
     RegimeTransitionFunction,
+    ScalarFloat,
+    ScalarInt,
     StateName,
     StateOrActionName,
     TransitionFunctionName,
@@ -214,7 +220,7 @@ class SimulateFunctions:
 class InternalRegime:
     """Internal representation of a user regime."""
 
-    name: str
+    name: RegimeName
     """Regime name (key in the regimes dict)."""
 
     terminal: bool
@@ -273,10 +279,13 @@ def state_action_space(self, regime_params: FlatRegimeParams) -> StateActionSpac
                 points_key = f"{name}__points"
                 if points_key not in all_params:
                     continue
+                # Runtime grid-point params are flat JAX arrays — never a
+                # `MappingLeaf` / `SequenceLeaf` — so narrow via `cast`.
+                points = cast("Array", all_params[points_key])
                 if in_states:
-                    state_replacements[name] = all_params[points_key]
+                    state_replacements[name] = points
                 else:
-                    action_replacements[name] = all_params[points_key]
+                    action_replacements[name] = points
             # `_ShockGrid` is state-only by construction (intrinsic
             # transitions, forbidden as actions per AGENTS.md). The
             # `in_states` gate makes that invariant explicit — a
@@ -292,9 +301,13 @@ def state_action_space(self, regime_params: FlatRegimeParams) -> StateActionSpac
                 )
                 if not all_present:
                     continue
-                shock_kw: dict[str, float] = dict(spec.params)
+                shock_kw: dict[str, ScalarFloat | ScalarInt] = dict(spec.params)
                 for p in spec.params_to_pass_at_runtime:
-                    shock_kw[p] = cast("float", all_params[f"{name}__{p}"])
+                    # Runtime shock-grid params are flat JAX scalars — never
+                    # a `MappingLeaf` / `SequenceLeaf` — so narrow via `cast`.
+                    shock_kw[p] = cast(
+                        "ScalarFloat | ScalarInt", all_params[f"{name}__{p}"]
+                    )
                 state_replacements[name] = spec.compute_gridpoints(**shock_kw)
 
         new_states = (
@@ -417,9 +430,9 @@ def _build_regime_sharding(
 
 def _distribute_states_to_devices(
     *,
-    states: MappingProxyType[StateName, Array],
+    states: MappingProxyType[StateName, FloatND | IntND],
     grids: MappingProxyType[StateOrActionName, Grid],
-) -> MappingProxyType[StateName, Array]:
+) -> MappingProxyType[StateName, FloatND | IntND]:
     """Place each distributed state's array on its device mesh.
 
     States whose grid carries `distributed=True` are placed via
@@ -451,13 +464,13 @@ def _distribute_states_to_devices(
 class PeriodRegimeSimulationData:
     """Raw simulation data for one period in one regime."""
 
-    V_arr: Array
+    V_arr: Float1D
     """Value function array for all subjects at this period."""
 
-    actions: MappingProxyType[ActionName, Array]
+    actions: MappingProxyType[ActionName, FloatND | IntND]
     """Immutable mapping of action names to optimal action arrays for all subjects."""
 
-    states: MappingProxyType[StateName, Array]
+    states: MappingProxyType[StateName, FloatND | IntND]
     """Immutable mapping of state names to state value arrays for all subjects."""
 
     in_regime: Bool1D
diff --git a/src/lcm/model.py b/src/lcm/model.py
index 01b76e01f..1b5de6dfa 100644
--- a/src/lcm/model.py
+++ b/src/lcm/model.py
@@ -8,8 +8,9 @@
 from types import MappingProxyType
 
 import pandas as pd
-from jax import Array
+from beartype import beartype
 
+from lcm._beartype_conf import MODEL_CONF, PARAMS_CONF
 from lcm.ages import AgeGrid
 from lcm.exceptions import InvalidValueFunctionError, ModelInitializationError
 from lcm.grids import DiscreteGrid
@@ -34,18 +35,22 @@
 from lcm.regime import Regime
 from lcm.regime_building.processing import InternalRegime
 from lcm.simulation.compile import compile_all_simulate_functions
-from lcm.simulation.initial_conditions import validate_initial_conditions
+from lcm.simulation.initial_conditions import (
+    canonicalize_initial_conditions,
+    validate_initial_conditions,
+)
 from lcm.simulation.result import SimulationResult, get_simulation_output_dtypes
 from lcm.simulation.simulate import simulate
 from lcm.solution.solve_brute import solve
 from lcm.typing import (
-    FloatND,
     FunctionName,
     InternalParams,
     ParamsTemplate,
+    PeriodToRegimeToVArr,
     RegimeName,
     RegimeNamesToIds,
     UserFacingParamsTemplate,
+    UserInitialConditions,
     UserParams,
 )
 from lcm.utils.containers import (
@@ -128,6 +133,7 @@ class Model:
     simulate() calls don't serialise on logging I/O.
     """
 
+    @beartype(conf=MODEL_CONF)
     def __init__(
         self,
         *,
@@ -239,6 +245,7 @@ def get_params_template(self) -> UserFacingParamsTemplate:
             for regime, funcs in mutable.items()
         }
 
+    @beartype(conf=PARAMS_CONF)
     def solve(
         self,
         *,
@@ -247,7 +254,7 @@ def solve(
         log_level: LogLevel = "progress",
         log_path: str | Path | None = None,
         log_keep_n_latest: int = 3,
-    ) -> MappingProxyType[int, MappingProxyType[RegimeName, FloatND]]:
+    ) -> PeriodToRegimeToVArr:
         """Solve the model using the pre-computed functions.
 
         Args:
@@ -301,7 +308,7 @@ def _solve_compiled(
         log_path: str | Path | None,
         log_keep_n_latest: int,
         max_compilation_workers: int | None,
-    ) -> MappingProxyType[int, MappingProxyType[RegimeName, FloatND]]:
+    ) -> PeriodToRegimeToVArr:
         """Run backward induction, persisting a snapshot on debug or NaN failure."""
         try:
             period_to_regime_to_V_arr = solve(
@@ -368,15 +375,13 @@ def _resolve_simulate_internal_regimes(
         with self._simulate_compile_lock:
             return self._simulate_compile_cache[self.n_subjects]
 
+    @beartype(conf=PARAMS_CONF)
     def simulate(
         self,
         *,
         params: UserParams,
-        initial_conditions: Mapping[str, Array],
-        period_to_regime_to_V_arr: MappingProxyType[
-            int, MappingProxyType[RegimeName, FloatND]
-        ]
-        | None,
+        initial_conditions: UserInitialConditions | pd.DataFrame,
+        period_to_regime_to_V_arr: PeriodToRegimeToVArr | None,
         check_initial_conditions: bool = True,
         seed: int | None = None,
         log_level: LogLevel = "progress",
@@ -401,11 +406,12 @@ def simulate(
                   specification
                 Values may be `pd.Series` with labeled indices; they are
                 auto-converted to JAX arrays.
-            initial_conditions: Mapping of state names (plus `"regime"`) to arrays.
+            initial_conditions: Mapping of state names (plus `"regime_id"`) to arrays.
                 All arrays must have the same length (number of subjects). The
-                `"regime"` entry must contain integer regime codes (from
+                `"regime_id"` entry must contain integer regime codes (from
                 `model.regime_names_to_ids`). May also be a `pd.DataFrame`
-                with a `"regime"` column (auto-converted).
+                with a `"regime_name"` column carrying regime label strings
+                (auto-converted via `initial_conditions_from_dataframe`).
             period_to_regime_to_V_arr: Value function arrays from `solve()`.
                 When `None`, the model is solved automatically before simulating.
             check_initial_conditions: Whether to validate initial conditions.
@@ -433,6 +439,10 @@ def simulate(
                 regimes=self.regimes,
                 regime_names_to_ids=self.regime_names_to_ids,
             )
+        initial_conditions = canonicalize_initial_conditions(
+            initial_conditions=initial_conditions,
+            internal_regimes=self.internal_regimes,
+        )
         internal_params = self._process_params(params)
         if check_initial_conditions:
             validate_initial_conditions(
diff --git a/src/lcm/model_processing.py b/src/lcm/model_processing.py
index 4f149736a..6df0fb5f9 100644
--- a/src/lcm/model_processing.py
+++ b/src/lcm/model_processing.py
@@ -151,20 +151,19 @@ def validate_model_inputs(
     regime_id_class: type,
     n_subjects: int | None = None,
 ) -> None:
-    """Validate model constructor inputs."""
-    _fail_if_invalid_n_subjects(n_subjects=n_subjects)
+    """Validate model constructor inputs.
 
-    # Early exit if regimes are not lcm.Regime instances
-    if not all(isinstance(regime, Regime) for regime in regimes.values()):
-        raise ModelInitializationError(
-            "All items in regimes must be instances of lcm.Regime."
-        )
+    `n_periods` is derived from `Model.__init__`'s `ages: AgeGrid` and
+    `regimes` is typed via beartype on `Model.__init__`; both reach this
+    function with their declared types. This function focuses on value and
+    cross-field rules.
+
+    """
+    _fail_if_invalid_n_subjects(n_subjects=n_subjects)
 
     error_messages: list[str] = []
 
-    if not isinstance(n_periods, int):
-        error_messages.append("n_periods must be an integer.")
-    elif n_periods <= 1:
+    if n_periods <= 1:
         error_messages.append("n_periods must be at least 2.")
 
     if not regimes:
diff --git a/src/lcm/pandas_utils.py b/src/lcm/pandas_utils.py
index 68f8ad25e..044087547 100644
--- a/src/lcm/pandas_utils.py
+++ b/src/lcm/pandas_utils.py
@@ -3,24 +3,26 @@
 from collections.abc import Callable, Mapping
 from dataclasses import dataclass
 from types import MappingProxyType
-from typing import cast
+from typing import Literal, cast
 
 import jax.numpy as jnp
 import numpy as np
 import pandas as pd
 from dags.tree import qname_from_tree_path, tree_path_from_qname
-from jax import Array
 
 from lcm.ages import PSEUDO_STATE_NAMES, AgeGrid
 from lcm.dtypes import canonical_float_dtype
 from lcm.grids import DiscreteGrid, IrregSpacedGrid
-from lcm.params import MappingLeaf
-from lcm.params.sequence_leaf import SequenceLeaf
+from lcm.params import UserMappingLeaf, UserSequenceLeaf
 from lcm.regime import Regime
 from lcm.shocks import _ShockGrid
 from lcm.simulation.initial_conditions import MISSING_CAT_CODE
 from lcm.typing import (
+    Float1D,
+    FloatND,
     FunctionName,
+    InitialConditions,
+    Int1D,
     InternalParams,
     RegimeName,
     RegimeNamesToIds,
@@ -38,9 +40,11 @@ def has_series(params: Mapping) -> bool:
             return True
         if isinstance(value, Mapping) and has_series(value):
             return True
-        if isinstance(value, (MappingLeaf, SequenceLeaf)):
+        if isinstance(value, (UserMappingLeaf, UserSequenceLeaf)):
             items = (
-                value.data.values() if isinstance(value, MappingLeaf) else value.data
+                value.data.values()
+                if isinstance(value, UserMappingLeaf)
+                else value.data
             )
             if any(isinstance(v, pd.Series) for v in items):
                 return True
@@ -52,28 +56,30 @@ def initial_conditions_from_dataframe(  # noqa: C901
     df: pd.DataFrame,
     regimes: Mapping[RegimeName, Regime],
     regime_names_to_ids: RegimeNamesToIds,
-) -> dict[str, Array]:
+) -> InitialConditions:
     """Convert a DataFrame of initial conditions to LCM initial conditions format.
 
     Args:
-        df: DataFrame with columns for states and a "regime" column.
+        df: DataFrame with columns for states and a "regime_name" column
+            carrying the regime label strings.
         regimes: Mapping of regime names to user Regime instances.
         regime_names_to_ids: Immutable mapping from regime names to integer
             indices.
 
     Returns:
-        Dict mapping state names (plus `"regime"`) to JAX arrays. The
-        `"regime"` entry contains integer codes derived from the `"regime"`
-        column via `regime_names_to_ids`.
+        Immutable mapping of state names (plus `"regime_id"`) to JAX
+        arrays. The `"regime_id"` entry contains integer codes derived
+        from the `"regime_name"` column via `regime_names_to_ids`.
 
     Raises:
-        ValueError: If the DataFrame is empty, the "regime" column is missing,
-            contains invalid regime names, has unknown columns, is missing required
-            states, or categorical columns contain invalid labels.
+        ValueError: If the DataFrame is empty, the "regime_name" column is
+            missing, contains invalid regime names, has unknown columns, is
+            missing required states, or categorical columns contain invalid
+            labels.
 
     """
-    if "regime" not in df.columns:
-        msg = "DataFrame must contain a 'regime' column."
+    if "regime_name" not in df.columns:
+        msg = "DataFrame must contain a 'regime_name' column."
         raise ValueError(msg)
 
     if len(df) == 0:
@@ -82,23 +88,24 @@ def initial_conditions_from_dataframe(  # noqa: C901
 
     # Validate regime names
     valid_regimes = set(regime_names_to_ids.keys())
-    invalid_regimes = set(df["regime"]) - valid_regimes
+    invalid_regimes = set(df["regime_name"]) - valid_regimes
     if invalid_regimes:
         msg = (
-            f"Invalid regime names in 'regime' column: {sorted(invalid_regimes)}. "
+            f"Invalid regime names in 'regime_name' column: "
+            f"{sorted(invalid_regimes)}. "
             f"Valid regimes: {sorted(valid_regimes)}."
         )
         raise ValueError(msg)
 
-    state_columns = {col for col in df.columns if col != "regime"}
+    state_columns = {col for col in df.columns if col != "regime_name"}
     _validate_state_columns(
         state_columns=state_columns,
         regimes=regimes,
-        initial_regimes=df["regime"].tolist(),
+        initial_regimes=df["regime_name"].tolist(),
     )
 
     n_subjects = len(df)
-    state_cols = [col for col in df.columns if col != "regime"]
+    state_cols = [col for col in df.columns if col != "regime_name"]
 
     # Pre-allocate result arrays (NaN default surfaces bugs for missing states)
     result_arrays: dict[str, np.ndarray] = {
@@ -107,7 +114,7 @@ def initial_conditions_from_dataframe(  # noqa: C901
     discrete_state_names: set[StateName] = set()
 
     # Process per regime group (vectorised .map() within each group)
-    for regime_name, group in df.groupby("regime"):
+    for regime_name, group in df.groupby("regime_name"):
         regime = regimes[str(regime_name)]
         idx = group.index
         discrete_grids = {
@@ -147,18 +154,18 @@ def initial_conditions_from_dataframe(  # noqa: C901
             nan_mask = np.isnan(result_arrays[col])
             result_arrays[col][nan_mask] = MISSING_CAT_CODE
 
-    initial_conditions: dict[str, Array] = {
+    initial_conditions: dict[StateName | Literal["regime_id"], Float1D | Int1D] = {
         col: jnp.array(arr, dtype=jnp.int32)
         if col in discrete_state_names
         else jnp.array(arr, dtype=canonical_float_dtype())
         for col, arr in result_arrays.items()
     }
-    initial_conditions["regime"] = jnp.array(
-        df["regime"].map(dict(regime_names_to_ids)).to_numpy(),
+    initial_conditions["regime_id"] = jnp.array(
+        df["regime_name"].map(dict(regime_names_to_ids)).to_numpy(),
         dtype=jnp.int32,
     )
 
-    return initial_conditions
+    return MappingProxyType(initial_conditions)
 
 
 def _map_discrete_labels(
@@ -196,7 +203,8 @@ def convert_series_in_params(
 
     Iterate over the template-shaped `internal_params` (produced by
     `process_params`) and convert any `pd.Series` leaf values via
-    `array_from_series`. `MappingLeaf` and `SequenceLeaf` values are
+    `array_from_series`. `UserMappingLeaf` and `UserSequenceLeaf` values
+    (and their canonical `MappingLeaf` / `SequenceLeaf` subclasses) are
     traversed and any Series inside are converted. Other values (scalars,
     existing arrays) pass through unchanged.
 
@@ -279,7 +287,8 @@ def _convert_param_value(
     """Convert a single param value, dispatching on type.
 
     Args:
-        value: The parameter value (Series, MappingLeaf, or passthrough).
+        value: The parameter value (Series, `UserMappingLeaf` /
+            `UserSequenceLeaf`, or passthrough).
         func: The function that uses this parameter (`None` for runtime
             grid params — triggers scalar passthrough).
         param_name: Parameter name in the function.
@@ -291,8 +300,9 @@ def _convert_param_value(
         regime_name: Regime name for action grid lookup.
 
     Returns:
-        Converted value: JAX array for Series, MappingLeaf with converted
-        Series entries, or the original value unchanged.
+        Converted value: JAX array for Series, a `UserMappingLeaf` /
+        `UserSequenceLeaf` with converted Series entries, or the original
+        value unchanged.
 
     """
 
@@ -319,10 +329,13 @@ def _recurse(inner_value: object) -> object:
             regime_names_to_ids=regime_names_to_ids,
             regime_name=regime_name,
         )
-    if isinstance(value, MappingLeaf):
-        return MappingLeaf({k: _recurse(v) for k, v in value.data.items()})
-    if isinstance(value, SequenceLeaf):
-        return SequenceLeaf(tuple(_recurse(v) for v in value.data))
+    # `convert_series_in_params` runs between broadcast and canonicalization,
+    # so leaves are still in user form. Preserve that user form on output:
+    # canonicalization happens downstream in `cast_params_to_canonical_dtypes`.
+    if isinstance(value, UserMappingLeaf):
+        return UserMappingLeaf({k: _recurse(v) for k, v in value.data.items()})
+    if isinstance(value, UserSequenceLeaf):
+        return UserSequenceLeaf(tuple(_recurse(v) for v in value.data))
     return value
 
 
@@ -336,7 +349,7 @@ def array_from_series(
     regimes: Mapping[RegimeName, Regime],
     regime_names_to_ids: RegimeNamesToIds,
     regime_name: RegimeName | None = None,
-) -> Array:
+) -> FloatND:
     """Convert a pandas Series to a JAX array.
 
     Inspect `func` to determine indexing dimensions (states, actions,
@@ -683,7 +696,7 @@ def _scatter_series(
     series: pd.Series,
     level_mappings: tuple[_LevelMapping, ...],
     fill_value: float = np.nan,
-) -> Array:
+) -> FloatND:
     """Scatter a MultiIndex Series into an N-dimensional JAX array.
 
     Each `_LevelMapping` defines one axis: its size, and how to map labels from
diff --git a/src/lcm/params/__init__.py b/src/lcm/params/__init__.py
index 9eba7e8f4..0705dde3f 100644
--- a/src/lcm/params/__init__.py
+++ b/src/lcm/params/__init__.py
@@ -1,24 +1,37 @@
 from collections.abc import Mapping, Sequence
 from typing import Any, overload
 
-from lcm.params.mapping_leaf import MappingLeaf
-from lcm.params.sequence_leaf import SequenceLeaf
+from beartype import beartype
+
+from lcm._beartype_conf import PARAMS_CONF
+from lcm.params.mapping_leaf import MappingLeaf, UserMappingLeaf
+from lcm.params.sequence_leaf import SequenceLeaf, UserSequenceLeaf
 
 
 @overload
-def as_leaf(data: Mapping[str, Any]) -> MappingLeaf: ...
+def as_leaf(data: Mapping[str, Any]) -> UserMappingLeaf: ...
 
 
 @overload
-def as_leaf(data: Sequence[Any]) -> SequenceLeaf: ...
+def as_leaf(data: Sequence[Any]) -> UserSequenceLeaf: ...
+
+
+@beartype(conf=PARAMS_CONF)
+def as_leaf(
+    data: Mapping[str, Any] | Sequence[Any],
+) -> UserMappingLeaf | UserSequenceLeaf:
+    """Wrap a Mapping or Sequence as a JAX-pytree leaf.
 
+    Returns the boundary (`User...Leaf`) variant — accepts Python scalars,
+    numpy arrays, `pd.Series`, JAX arrays, and nested leaves. The
+    canonical narrowed variants (`MappingLeaf` / `SequenceLeaf`) are the
+    output of `cast_params_to_canonical_dtypes`.
 
-def as_leaf(data: Mapping[str, Any] | Sequence[Any]) -> MappingLeaf | SequenceLeaf:
-    """Wrap a Mapping or Sequence as a JAX-pytree leaf."""
+    """
     if isinstance(data, Mapping):
-        return MappingLeaf(dict(data))
+        return UserMappingLeaf(dict(data))
     if isinstance(data, Sequence):
-        return SequenceLeaf(data)
+        return UserSequenceLeaf(data)
     msg = f"as_leaf() expects a Mapping or Sequence, got {type(data).__name__}"
     raise TypeError(msg)
 
@@ -32,4 +45,11 @@ def __getattr__(name: str) -> object:
     raise AttributeError(msg)
 
 
-__all__ = ["MappingLeaf", "SequenceLeaf", "as_leaf", "process_params"]
+__all__ = [
+    "MappingLeaf",
+    "SequenceLeaf",
+    "UserMappingLeaf",
+    "UserSequenceLeaf",
+    "as_leaf",
+    "process_params",
+]
diff --git a/src/lcm/params/mapping_leaf.py b/src/lcm/params/mapping_leaf.py
index 617c1e2f0..fc68be584 100644
--- a/src/lcm/params/mapping_leaf.py
+++ b/src/lcm/params/mapping_leaf.py
@@ -1,22 +1,40 @@
 """A Mapping wrapper that is a JAX pytree but not itself a Mapping."""
 
-from collections.abc import Mapping
-from typing import Any
+from collections.abc import Mapping, Sequence
+from typing import TYPE_CHECKING, Any
 
 import jax
 
+if TYPE_CHECKING:
+    from lcm.typing import _ParamsLeaf, _UserParamsLeaf
 
-class MappingLeaf:
+
+class UserMappingLeaf:
     """A Mapping wrapper that is a JAX pytree but not itself a Mapping.
 
-    Prevents flatten_regime_namespace from recursing into contents while
-    allowing JAX to trace through array values.
+    Holds the boundary leaf type — accepts the same wide value union as
+    `Model.solve` / `Model.simulate` parameters (Python scalars, numpy
+    arrays, `pd.Series`, JAX arrays, nested `UserMappingLeaf` /
+    `UserSequenceLeaf`).
+
+    Prevents `flatten_regime_namespace` from recursing into contents while
+    allowing JAX to trace through array values. The `data` attribute is
+    deep-converted to immutable containers (`MappingProxyType`, `tuple`,
+    `frozenset`) on construction; instances themselves are not hashable
+    (`__hash__ = None`), since `MappingProxyType` isn't.
+
+    The constructor accepts `Mapping[str, Any]` at runtime so beartype's
+    O(n) per-leaf check doesn't fire on user-supplied scalars or arrays;
+    the precise leaf-type contract is enforced statically through the
+    `data` class-attribute annotation.
 
-    Data is frozen to immutable containers on construction.
     """
 
     __slots__ = ("data",)
 
+    if TYPE_CHECKING:
+        data: Mapping[str, _UserParamsLeaf]
+
     def __init__(self, data: Mapping[str, Any]) -> None:
         from lcm.utils.containers import (  # noqa: PLC0415
             ensure_containers_are_immutable,
@@ -25,24 +43,51 @@ def __init__(self, data: Mapping[str, Any]) -> None:
         self.data = ensure_containers_are_immutable(data)
 
     def __repr__(self) -> str:
-        return f"MappingLeaf({dict(self.data)!r})"
+        return f"{type(self).__name__}({dict(self.data)!r})"
 
     __hash__ = None  # MappingProxyType is not hashable
 
     def __eq__(self, other: object) -> bool:
-        if not isinstance(other, MappingLeaf):
+        if not isinstance(other, UserMappingLeaf):
             return NotImplemented
         return self.data == other.data
 
 
-def _flatten(nmp: MappingLeaf) -> tuple[list[Any], tuple[str, ...]]:
-    keys = tuple(sorted(nmp.data.keys()))
-    values = [nmp.data[k] for k in keys]
+class MappingLeaf(UserMappingLeaf):
+    """Mapping leaf carrying only canonical-dtype values.
+
+    Output of `cast_params_to_canonical_dtypes`. Every value is either a
+    canonical JAX array (`FloatND` / `IntND` / `BoolND`) or another
+    canonical leaf (`MappingLeaf` / `SequenceLeaf`).
+
+    Subclasses `UserMappingLeaf`, so any code that accepts the wide user
+    form via `isinstance(_, UserMappingLeaf)` also accepts the canonical
+    form. Code requiring canonical values uses the narrower `MappingLeaf`
+    type explicitly.
+
+    """
+
+    __slots__ = ()
+
+    if TYPE_CHECKING:
+        data: Mapping[str, _ParamsLeaf]
+
+
+def _user_flatten(
+    leaf: UserMappingLeaf,
+) -> tuple[list[Any], tuple[str, ...]]:
+    keys = tuple(sorted(leaf.data.keys()))
+    values = [leaf.data[k] for k in keys]
     return values, keys
 
 
-def _unflatten(keys: tuple[str, ...], values: list[Any]) -> MappingLeaf:
+def _user_unflatten(keys: tuple[str, ...], values: Sequence[Any]) -> UserMappingLeaf:
+    return UserMappingLeaf(dict(zip(keys, values, strict=True)))
+
+
+def _canonical_unflatten(keys: tuple[str, ...], values: Sequence[Any]) -> MappingLeaf:
     return MappingLeaf(dict(zip(keys, values, strict=True)))
 
 
-jax.tree_util.register_pytree_node(MappingLeaf, _flatten, _unflatten)
+jax.tree_util.register_pytree_node(UserMappingLeaf, _user_flatten, _user_unflatten)
+jax.tree_util.register_pytree_node(MappingLeaf, _user_flatten, _canonical_unflatten)
diff --git a/src/lcm/params/processing.py b/src/lcm/params/processing.py
index d6afbccd6..5b36e091d 100644
--- a/src/lcm/params/processing.py
+++ b/src/lcm/params/processing.py
@@ -9,7 +9,9 @@
   range values surface as `ValueError`.
 - Python `float` and typed float arrays cast to `canonical_float_dtype()`.
   Down-cast overflow surfaces as `OverflowError`.
-- `MappingLeaf` / `SequenceLeaf` containers recurse.
+- `UserMappingLeaf` / `UserSequenceLeaf` containers (covering both the
+  user-input variant and the canonical narrow variant) recurse, always
+  emitting a canonical `MappingLeaf` / `SequenceLeaf`.
 
 The pass runs as the *last* step over `internal_params` — `pd.Series`
 leaves are reshaped to JAX arrays via `convert_series_in_params`
@@ -34,8 +36,8 @@
 from lcm.dtypes import safe_to_float_dtype, safe_to_int_dtype
 from lcm.exceptions import InvalidNameError, InvalidParamsError
 from lcm.interfaces import InternalRegime
-from lcm.params.mapping_leaf import MappingLeaf
-from lcm.params.sequence_leaf import SequenceLeaf
+from lcm.params.mapping_leaf import MappingLeaf, UserMappingLeaf
+from lcm.params.sequence_leaf import SequenceLeaf, UserSequenceLeaf
 from lcm.typing import (
     InternalParams,
     ParamsTemplate,
@@ -64,8 +66,9 @@ def process_params(
 
     The output always matches the params_template skeleton. Every numeric
     leaf — Python `bool` / `int` / `float`, typed JAX or numpy arrays, and
-    numerics inside `MappingLeaf` / `SequenceLeaf` — is cast to the
-    canonical pylcm dtype so the AOT signature is stable across calls.
+    numerics inside `UserMappingLeaf` / `UserSequenceLeaf` (or their
+    canonical narrow subclasses) — is cast to the canonical pylcm dtype
+    so the AOT signature is stable across calls.
 
     Callers that pass `pd.Series` leaves should orchestrate the steps
     themselves: `broadcast_to_template` (resolve), `convert_series_in_params`
@@ -205,7 +208,9 @@ def _cast_leaves_to_canonical_dtype(value: Any, *, name: str) -> Any:  # noqa: A
 
     Casts:
 
-    - `MappingLeaf` / `SequenceLeaf`: recurse on contents.
+    - `UserMappingLeaf` / `UserSequenceLeaf` (covers both wide user and
+      canonical narrow variants): recurse on contents, always emit the
+      canonical `MappingLeaf` / `SequenceLeaf`.
     - Python `bool`: `jnp.bool_(value)` (must come before `int` —
       `True` is a Python `int` subclass).
     - Python `int`: `safe_to_int_dtype(value)` → `jnp.int32`.
@@ -224,14 +229,16 @@ def _cast_leaves_to_canonical_dtype(value: Any, *, name: str) -> Any:  # noqa: A
     - Anything else (`str`, `None`, `dict`, lists, custom objects).
 
     """
-    if isinstance(value, MappingLeaf):
+    # `UserMappingLeaf` covers both user (wide) and canonical (`MappingLeaf`)
+    # variants — recursing always emits a canonical `MappingLeaf`.
+    if isinstance(value, UserMappingLeaf):
         return MappingLeaf(
             {
                 k: _cast_leaves_to_canonical_dtype(v, name=f"{name}.{k}")
                 for k, v in value.data.items()
             }
         )
-    if isinstance(value, SequenceLeaf):
+    if isinstance(value, UserSequenceLeaf):
         return SequenceLeaf(
             [
                 _cast_leaves_to_canonical_dtype(v, name=f"{name}[{i}]")
@@ -268,7 +275,7 @@ def _cast_leaves_to_canonical_dtype(value: Any, *, name: str) -> Any:  # noqa: A
     msg = (
         f"{name!r}: unsupported leaf type {type(value).__name__} "
         f"(expected bool / int / float / numpy or JAX array / "
-        f"MappingLeaf / SequenceLeaf)."
+        f"UserMappingLeaf / UserSequenceLeaf)."
     )
     raise InvalidParamsError(msg)
 
@@ -306,7 +313,8 @@ def create_params_template(  # noqa: C901
     are disjoint sets to enable unambiguous parameter propagation.
 
     Args:
-        internal_regimes: Mapping of regime names to InternalRegime instances.
+        internal_regimes: Immutable mapping of regime names to InternalRegime
+            instances.
 
     Returns:
         The parameter template.
diff --git a/src/lcm/params/sequence_leaf.py b/src/lcm/params/sequence_leaf.py
index 363eb963e..8176331f3 100644
--- a/src/lcm/params/sequence_leaf.py
+++ b/src/lcm/params/sequence_leaf.py
@@ -1,45 +1,86 @@
 """A Sequence wrapper that is a JAX pytree but not itself a Sequence."""
 
 from collections.abc import Sequence
-from typing import Any
+from typing import TYPE_CHECKING, Any
 
 import jax
 
+if TYPE_CHECKING:
+    from lcm.typing import _ParamsLeaf, _UserParamsLeaf
 
-class SequenceLeaf:
+
+class UserSequenceLeaf:
     """A Sequence wrapper that is a JAX pytree but not itself a Sequence.
 
-    Prevents flatten_regime_namespace from recursing into contents while
-    allowing JAX to trace through array values.
+    Holds the boundary leaf type — accepts the same wide value union as
+    `Model.solve` / `Model.simulate` parameters (Python scalars, numpy
+    arrays, `pd.Series`, JAX arrays, nested `UserMappingLeaf` /
+    `UserSequenceLeaf`).
+
+    Prevents `flatten_regime_namespace` from recursing into contents while
+    allowing JAX to trace through array values. Data is frozen to
+    immutable containers on construction.
+
+    The constructor accepts `Sequence[Any]` at runtime so beartype's
+    O(n) per-leaf check doesn't fire on user-supplied scalars or arrays;
+    the precise leaf-type contract is enforced statically through the
+    `data` class-attribute annotation.
 
-    Data is frozen to immutable containers on construction.
     """
 
     __slots__ = ("data",)
 
+    if TYPE_CHECKING:
+        data: tuple[_UserParamsLeaf, ...]
+
     def __init__(self, data: Sequence[Any]) -> None:
         from lcm.utils.containers import _make_immutable  # noqa: PLC0415
 
         self.data = tuple(_make_immutable(v) for v in data)
 
     def __repr__(self) -> str:
-        return f"SequenceLeaf({list(self.data)!r})"
+        return f"{type(self).__name__}({list(self.data)!r})"
 
     def __hash__(self) -> int:
         return hash(self.data)
 
     def __eq__(self, other: object) -> bool:
-        if not isinstance(other, SequenceLeaf):
+        if not isinstance(other, UserSequenceLeaf):
             return NotImplemented
         return self.data == other.data
 
 
-def _flatten(sl: SequenceLeaf) -> tuple[list[Any], None]:
-    return list(sl.data), None
+class SequenceLeaf(UserSequenceLeaf):
+    """Sequence leaf carrying only canonical-dtype values.
+
+    Output of `cast_params_to_canonical_dtypes`. Every value is either a
+    canonical JAX array (`FloatND` / `IntND` / `BoolND`) or another
+    canonical leaf (`MappingLeaf` / `SequenceLeaf`).
+
+    Subclasses `UserSequenceLeaf`, so any code that accepts the wide user
+    form via `isinstance(_, UserSequenceLeaf)` also accepts the canonical
+    form. Code requiring canonical values uses the narrower `SequenceLeaf`
+    type explicitly.
+
+    """
+
+    __slots__ = ()
+
+    if TYPE_CHECKING:
+        data: tuple[_ParamsLeaf, ...]
+
+
+def _user_flatten(leaf: UserSequenceLeaf) -> tuple[list[Any], None]:
+    return list(leaf.data), None
+
+
+def _user_unflatten(_aux: None, values: Sequence[Any]) -> UserSequenceLeaf:
+    return UserSequenceLeaf(values)
 
 
-def _unflatten(_aux: None, values: list[Any]) -> SequenceLeaf:
+def _canonical_unflatten(_aux: None, values: Sequence[Any]) -> SequenceLeaf:
     return SequenceLeaf(values)
 
 
-jax.tree_util.register_pytree_node(SequenceLeaf, _flatten, _unflatten)
+jax.tree_util.register_pytree_node(UserSequenceLeaf, _user_flatten, _user_unflatten)
+jax.tree_util.register_pytree_node(SequenceLeaf, _user_flatten, _canonical_unflatten)
diff --git a/src/lcm/persistence.py b/src/lcm/persistence.py
index c8498a199..e0cefdc6d 100644
--- a/src/lcm/persistence.py
+++ b/src/lcm/persistence.py
@@ -9,7 +9,7 @@
 import shutil
 import tempfile
 import textwrap
-from collections.abc import Mapping, Sequence
+from collections.abc import Sequence
 from dataclasses import dataclass
 from pathlib import Path
 from types import MappingProxyType
@@ -19,14 +19,50 @@
 import h5py
 import jax.numpy as jnp
 import numpy as np
-from jax import Array
 
-from lcm.typing import FloatND, PeriodToRegimeToVArr, RegimeName, UserParams
+from lcm.typing import (
+    FloatND,
+    InitialConditions,
+    PeriodToRegimeToVArr,
+    RegimeName,
+    UserParams,
+)
 
 if TYPE_CHECKING:
     from lcm.model import Model
     from lcm.simulation.result import SimulationResult
 
+    # Type-checker view: full precision.
+    _ModelOrNone = Model | None
+    _SimulationResultOrNone = SimulationResult | None
+else:
+    # Runtime view used by beartype's annotation evaluator. `Model` and
+    # `SimulationResult` cannot be imported here (circular), so collapse
+    # to `Any`. The snapshot dataclasses are serialization carriers; the
+    # API surface that needs strict checking is the `save_*_snapshot`
+    # callers, which beartype still polices via their own parameters.
+    _ModelOrNone = Any
+    _SimulationResultOrNone = Any
+
+
+def _bind_forward_refs(
+    *,
+    model_cls: type,
+    simulation_result_cls: type,
+) -> None:
+    """Bind `Model` and `SimulationResult` into this module's globals.
+
+    The package claw rewrites string annotations in `save_*_snapshot` into
+    runtime forward references resolved against this module's globals.
+    `lcm.__init__` calls this helper once both classes are loaded so the
+    refs resolve at call time without depending on an ad-hoc assignment
+    from outside the module.
+    """
+    global Model, SimulationResult  # noqa: PLW0603
+    Model = model_cls  # ty: ignore[invalid-assignment]
+    SimulationResult = simulation_result_cls  # ty: ignore[invalid-assignment]
+
+
 logger = logging.getLogger(__name__)
 
 
@@ -34,7 +70,7 @@
 class SolveSnapshot:
     """Snapshot of a solve run for offline reconstruction."""
 
-    model: Model | None
+    model: _ModelOrNone
     """The Model instance."""
 
     params: UserParams | None
@@ -51,19 +87,19 @@ class SolveSnapshot:
 class SimulateSnapshot:
     """Snapshot of a simulate run for offline reconstruction."""
 
-    model: Model | None
+    model: _ModelOrNone
     """The Model instance."""
 
     params: UserParams | None
     """User parameters passed to simulate."""
 
-    initial_conditions: Mapping[str, Array] | None
-    """Mapping of state names and "regime" to arrays."""
+    initial_conditions: InitialConditions | None
+    """Immutable mapping of state names and `"regime_id"` to canonical-dtype arrays."""
 
     period_to_regime_to_V_arr: PeriodToRegimeToVArr | None
     """Immutable mapping of periods to regime value function arrays."""
 
-    result: SimulationResult | None
+    result: _SimulationResultOrNone
     """SimulationResult object."""
 
     platform: str
@@ -189,7 +225,7 @@ def save_simulate_snapshot(
     *,
     model: Model,
     params: UserParams,
-    initial_conditions: Mapping[str, Array],
+    initial_conditions: InitialConditions,
     period_to_regime_to_V_arr: PeriodToRegimeToVArr,
     result: SimulationResult,
     log_path: Path,
@@ -200,7 +236,7 @@ def save_simulate_snapshot(
     Args:
         model: The Model instance.
         params: User parameters passed to simulate.
-        initial_conditions: Mapping of state names and "regime" to arrays.
+        initial_conditions: Mapping of state names and "regime_id" to arrays.
         period_to_regime_to_V_arr: Value function arrays.
         result: SimulationResult object.
         log_path: Parent directory for snapshot directories.
diff --git a/src/lcm/regime.py b/src/lcm/regime.py
index 6b4424a96..894f2dc0b 100644
--- a/src/lcm/regime.py
+++ b/src/lcm/regime.py
@@ -5,6 +5,9 @@
 from types import MappingProxyType
 from typing import Any, Literal, TypeAliasType, cast, overload
 
+from beartype import beartype
+
+from lcm._beartype_conf import REGIME_CONF
 from lcm.exceptions import RegimeInitializationError
 from lcm.grids import DiscreteGrid, Grid
 from lcm.interfaces import SolveSimulateFunctionPair
@@ -24,6 +27,7 @@
 )
 
 
+@beartype(conf=REGIME_CONF)
 @dataclass(frozen=True)
 class MarkovTransition:
     """Wrapper marking a transition function as stochastic (Markov).
@@ -48,11 +52,6 @@ class MarkovTransition:
     """The transition function returning a probability distribution."""
 
     def __post_init__(self) -> None:
-        if not callable(self.func):
-            raise RegimeInitializationError(
-                f"MarkovTransition requires a callable, "
-                f"but got {type(self.func).__name__}: {self.func!r}"
-            )
         # Copy __wrapped__ and __annotations__ from the wrapped function so
         # that inspect.signature and dags see the original signature. We use
         # object.__setattr__ because the dataclass is frozen.
@@ -65,7 +64,9 @@ def __call__(self, *args: Any, **kwargs: Any) -> FloatND:  # noqa: ANN401
         return self.func(*args, **kwargs)
 
 
-def _default_H(utility: float, E_next_V: float, discount_factor: float) -> float:
+def _default_H(
+    utility: FloatND, E_next_V: FloatND, discount_factor: FloatND
+) -> FloatND:
     return utility + discount_factor * E_next_V
 
 
@@ -104,6 +105,7 @@ def __call__(
         return kwargs[self._state_name]
 
 
+@beartype(conf=REGIME_CONF)
 @dataclass(frozen=True, kw_only=True)
 class Regime:
     """A user regime which can be processed into an internal regime.
@@ -184,11 +186,11 @@ def stochastic_regime_transition(self) -> bool:
 
     def __post_init__(self) -> None:
         from lcm.regime_building.validation import (  # noqa: PLC0415
-            validate_attribute_types,
             validate_logical_consistency,
+            validate_mapping_contents,
         )
 
-        validate_attribute_types(self)
+        validate_mapping_contents(self)
         validate_logical_consistency(self)
 
         def make_immutable(name: str) -> None:
diff --git a/src/lcm/regime_building/Q_and_F.py b/src/lcm/regime_building/Q_and_F.py
index 814619ddf..371a49018 100644
--- a/src/lcm/regime_building/Q_and_F.py
+++ b/src/lcm/regime_building/Q_and_F.py
@@ -4,7 +4,6 @@
 
 import jax.numpy as jnp
 from dags import concatenate_functions, with_signature
-from jax import Array
 
 from lcm.regime_building.h_dag import _get_build_H_kwargs
 from lcm.regime_building.next_state import (
@@ -18,6 +17,7 @@
     FloatND,
     FunctionsMapping,
     InternalUserFunction,
+    IntND,
     QAndFFunction,
     RegimeName,
     RegimeTransitionFunction,
@@ -136,7 +136,7 @@ def get_Q_and_F(
     )
     def Q_and_F(
         next_regime_to_V_arr: FloatND,
-        **states_actions_params: Array,
+        **states_actions_params: FloatND | IntND | BoolND,
     ) -> tuple[FloatND, BoolND]:
         """Calculate the state-action value and feasibility for a non-terminal period.
 
@@ -149,7 +149,7 @@ def Q_and_F(
             A tuple containing the arrays with state-action values and feasibilities.
 
         """
-        regime_transition_probs: MappingProxyType[str, Array] = (  # ty: ignore[invalid-assignment]
+        regime_transition_probs: MappingProxyType[RegimeName, FloatND] = (
             compute_regime_transition_probs(**states_actions_params)
         )
         U_arr, F_arr = U_and_F(**states_actions_params)
@@ -309,15 +309,17 @@ def get_compute_intermediates(
         args=arg_names_of_compute_intermediates,
         return_annotation=(
             "tuple[FloatND, FloatND, FloatND, FloatND, "
-            "MappingProxyType[RegimeName, Array]]"
+            "MappingProxyType[RegimeName, FloatND]]"
         ),
     )
     def compute_intermediates(
         next_regime_to_V_arr: FloatND,
-        **states_actions_params: Array,
-    ) -> tuple[FloatND, FloatND, FloatND, FloatND, MappingProxyType[RegimeName, Array]]:
+        **states_actions_params: FloatND | IntND | BoolND,
+    ) -> tuple[
+        FloatND, FloatND, FloatND, FloatND, MappingProxyType[RegimeName, FloatND]
+    ]:
         """Compute all Q_and_F intermediates."""
-        regime_transition_probs: MappingProxyType[str, Array] = (  # ty: ignore[invalid-assignment]
+        regime_transition_probs: MappingProxyType[RegimeName, FloatND] = (
             compute_regime_transition_probs(**states_actions_params)
         )
         U_arr, F_arr = U_and_F(**states_actions_params)
@@ -393,7 +395,7 @@ def get_Q_and_F_terminal(
     )
     def Q_and_F(
         next_regime_to_V_arr: FloatND,  # noqa: ARG001
-        **states_actions_params: Array,
+        **states_actions_params: FloatND | IntND | BoolND,
     ) -> tuple[FloatND, BoolND]:
         """Calculate the state-action values and feasibilities for a terminal period.
 
diff --git a/src/lcm/regime_building/V.py b/src/lcm/regime_building/V.py
index 31521a5c8..e49ae9888 100644
--- a/src/lcm/regime_building/V.py
+++ b/src/lcm/regime_building/V.py
@@ -5,14 +5,13 @@
 import jax.numpy as jnp
 from dags import concatenate_functions, with_signature
 from dags.tree import qname_from_tree_path
-from jax import Array
 
 from lcm.grids import ContinuousGrid, DiscreteGrid, IrregSpacedGrid
 from lcm.grids.coordinates import get_irreg_coordinate
 from lcm.regime import Regime
 from lcm.regime_building.ndimage import map_coordinates
 from lcm.shocks import _ShockGrid
-from lcm.typing import FloatND, ScalarFloat, StateName
+from lcm.typing import FloatND, IntND, ScalarFloat, StateName
 from lcm.utils.functools import all_as_kwargs
 from lcm.variables import Variables, get_grids
 
@@ -159,7 +158,7 @@ def _get_lookup_function(
     *,
     array_name: str,
     axis_names: list[str],
-) -> Callable[..., Array]:
+) -> Callable[..., FloatND]:
     """Create a function that emulates indexing into an array via named axes.
 
     Args:
@@ -173,8 +172,11 @@ def _get_lookup_function(
     """
     arg_names = [*axis_names, array_name]
 
-    @with_signature(args=dict.fromkeys(arg_names, "Array"), return_annotation="Array")
-    def lookup_wrapper(*args: Array, **kwargs: Array) -> Array:
+    @with_signature(
+        args=dict.fromkeys(arg_names, "FloatND | IntND"),
+        return_annotation="FloatND",
+    )
+    def lookup_wrapper(*args: FloatND | IntND, **kwargs: FloatND | IntND) -> FloatND:
         kwargs = all_as_kwargs(args=args, kwargs=kwargs, arg_names=arg_names)
         positions = tuple(kwargs[var] for var in axis_names)
         return kwargs[array_name][positions]
@@ -186,7 +188,7 @@ def _get_coordinate_finder(
     *,
     in_name: str,
     grid: ContinuousGrid,
-) -> Callable[..., Array]:
+) -> Callable[..., FloatND]:
     """Create a function that translates a value into coordinates on a grid.
 
     The resulting coordinates can be used to do linear interpolation via
@@ -210,9 +212,9 @@ def _get_coordinate_finder(
             arg_names = [in_name, points_param]
 
             @with_signature(
-                args=dict.fromkeys(arg_names, "Array"), return_annotation="Array"
+                args=dict.fromkeys(arg_names, "FloatND"), return_annotation="FloatND"
             )
-            def find_irreg_coordinate(*args: Array, **kwargs: Array) -> Array:
+            def find_irreg_coordinate(*args: FloatND, **kwargs: FloatND) -> FloatND:
                 kwargs = all_as_kwargs(args=args, kwargs=kwargs, arg_names=arg_names)
                 return get_irreg_coordinate(
                     value=kwargs[in_name], points=kwargs[points_param]
@@ -224,17 +226,19 @@ def find_irreg_coordinate(*args: Array, **kwargs: Array) -> Array:
         points_jax = grid.to_jax()
 
         @with_signature(
-            args=dict.fromkeys([in_name], "Array"), return_annotation="Array"
+            args=dict.fromkeys([in_name], "FloatND"), return_annotation="FloatND"
         )
-        def find_irreg_coordinate(*args: Array, **kwargs: Array) -> Array:
+        def find_irreg_coordinate(*args: FloatND, **kwargs: FloatND) -> FloatND:
             kwargs = all_as_kwargs(args=args, kwargs=kwargs, arg_names=[in_name])
             return get_irreg_coordinate(value=kwargs[in_name], points=points_jax)
 
         return find_irreg_coordinate
 
     # All other grid types (LinSpaced, LogSpaced, Piecewise*, ShockGrid)
-    @with_signature(args=dict.fromkeys([in_name], "Array"), return_annotation="Array")
-    def find_coordinate(*args: Array, **kwargs: Array) -> Array:
+    @with_signature(
+        args=dict.fromkeys([in_name], "FloatND"), return_annotation="FloatND"
+    )
+    def find_coordinate(*args: FloatND, **kwargs: FloatND) -> FloatND:
         kwargs = all_as_kwargs(args=args, kwargs=kwargs, arg_names=[in_name])
         return grid.get_coordinate(kwargs[in_name])
 
@@ -245,7 +249,7 @@ def _get_interpolator(
     *,
     name_of_values_on_grid: str,
     axis_names: list[str],
-) -> Callable[..., Array]:
+) -> Callable[..., FloatND]:
     """Create a function interpolator via named axes.
 
     Args:
@@ -260,8 +264,10 @@ def _get_interpolator(
     """
     arg_names = [name_of_values_on_grid, *axis_names]
 
-    @with_signature(args=dict.fromkeys(arg_names, "Array"), return_annotation="Array")
-    def interpolate(*args: Array, **kwargs: Array) -> Array:
+    @with_signature(
+        args=dict.fromkeys(arg_names, "FloatND"), return_annotation="FloatND"
+    )
+    def interpolate(*args: FloatND, **kwargs: FloatND) -> FloatND:
         kwargs = all_as_kwargs(args=args, kwargs=kwargs, arg_names=arg_names)
         coordinates = jnp.array([kwargs[var] for var in axis_names])
         return map_coordinates(
diff --git a/src/lcm/regime_building/argmax.py b/src/lcm/regime_building/argmax.py
index a4271e2f9..302fc4d4e 100644
--- a/src/lcm/regime_building/argmax.py
+++ b/src/lcm/regime_building/argmax.py
@@ -1,15 +1,14 @@
 import jax.numpy as jnp
-from jax import Array
 
-from lcm.typing import IntND
+from lcm.typing import BoolND, FloatND, IntND
 
 
 def argmax_and_max(
-    a: Array,
+    a: FloatND | IntND,
     axis: int | tuple[int, ...] | None = None,
     initial: float | None = None,
-    where: Array | None = None,
-) -> tuple[IntND, Array]:
+    where: BoolND | None = None,
+) -> tuple[IntND, FloatND | IntND]:
     """Compute the argmax of an n-dim array along axis.
 
     If multiple maxima exist, the first index will be selected.
@@ -70,7 +69,9 @@ def argmax_and_max(
     return _argmax, _max.reshape(_argmax.shape)
 
 
-def _move_axes_to_back(a: Array, axes: tuple[int, ...]) -> Array:
+def _move_axes_to_back(
+    a: FloatND | IntND | BoolND, axes: tuple[int, ...]
+) -> FloatND | IntND | BoolND:
     """Move specified axes to the back of the array.
 
     Args:
@@ -85,7 +86,9 @@ def _move_axes_to_back(a: Array, axes: tuple[int, ...]) -> Array:
     return a.transpose((*front_axes, *axes))
 
 
-def _flatten_last_n_axes(a: Array, n: int) -> Array:
+def _flatten_last_n_axes(
+    a: FloatND | IntND | BoolND, n: int
+) -> FloatND | IntND | BoolND:
     """Flatten the last n axes of a to 1 dimension.
 
     Args:
diff --git a/src/lcm/regime_building/diagnostics.py b/src/lcm/regime_building/diagnostics.py
index fb0ac9f17..e5321cdbd 100644
--- a/src/lcm/regime_building/diagnostics.py
+++ b/src/lcm/regime_building/diagnostics.py
@@ -15,7 +15,6 @@
 
 import jax
 import jax.numpy as jnp
-from jax import Array
 
 from lcm.ages import AgeGrid
 from lcm.grids import Grid
@@ -24,7 +23,10 @@
 from lcm.regime_building.V import VInterpolationInfo
 from lcm.typing import (
     ActionName,
+    BoolND,
+    FloatND,
     FunctionsMapping,
+    IntND,
     RegimeName,
     RegimeTransitionFunction,
     StateName,
@@ -164,14 +166,19 @@ def _wrap_with_reduction(
 
     """
 
-    def reduced(**kwargs: Array) -> dict[str, Any]:
+    # `kwargs` carries the wrapped function's full input map: the
+    # `next_regime_to_V_arr` mapping alongside the Float/Int/Bool-valued
+    # state/action inputs.
+    def reduced(
+        **kwargs: MappingProxyType[RegimeName, FloatND] | FloatND | IntND | BoolND,
+    ) -> dict[str, Any]:
         U_arr, F_arr, E_next_V, Q_arr, regime_probs = func(**kwargs)
         F_float = F_arr.astype(float)
         # NaN-count arrays are masked by feasibility: only feasible cells
         # contribute to numerators. Infeasible cells are zeroed out because
         # the solver masks them before the max, so a NaN there never
         # propagates to V_arr — reporting it would conflate causes.
-        nan_arrays: dict[str, Array] = {
+        nan_arrays: dict[str, FloatND] = {
             "U_nan": jnp.isnan(U_arr).astype(float) * F_float,
             "E_nan": jnp.isnan(E_next_V).astype(float) * F_float,
             "Q_nan": jnp.isnan(Q_arr).astype(float) * F_float,
diff --git a/src/lcm/regime_building/max_Q_over_a.py b/src/lcm/regime_building/max_Q_over_a.py
index f1e5d3913..4c9b3e979 100644
--- a/src/lcm/regime_building/max_Q_over_a.py
+++ b/src/lcm/regime_building/max_Q_over_a.py
@@ -4,7 +4,6 @@
 
 import jax.numpy as jnp
 from dags import with_signature
-from jax import Array
 
 from lcm.regime_building.argmax import argmax_and_max
 from lcm.typing import (
@@ -80,7 +79,7 @@ def get_max_Q_over_a(
     )
     def max_Q_over_a(
         next_regime_to_V_arr: MappingProxyType[RegimeName, FloatND],
-        **states_actions_params: Array,
+        **states_actions_params: FloatND | IntND | BoolND,
     ) -> FloatND:
         Q_arr, F_arr = Q_and_F(
             next_regime_to_V_arr=next_regime_to_V_arr,
@@ -153,7 +152,7 @@ def get_argmax_and_max_Q_over_a(
     )
     def argmax_and_max_Q_over_a(
         next_regime_to_V_arr: MappingProxyType[RegimeName, FloatND],
-        **states_actions_params: Array,
+        **states_actions_params: FloatND | IntND | BoolND,
     ) -> tuple[IntND, FloatND]:
         Q_arr, F_arr = Q_and_F(
             next_regime_to_V_arr=next_regime_to_V_arr,
diff --git a/src/lcm/regime_building/ndimage.py b/src/lcm/regime_building/ndimage.py
index 8d7d80d97..be0b338a4 100644
--- a/src/lcm/regime_building/ndimage.py
+++ b/src/lcm/regime_building/ndimage.py
@@ -19,14 +19,16 @@
 from collections.abc import Sequence
 
 import jax.numpy as jnp
-from jax import Array, jit, lax
+from jax import jit, lax
+
+from lcm.typing import FloatND, IntND
 
 
 @jit
 def map_coordinates(
-    input: Array,  # noqa: A002
-    coordinates: Sequence[Array],
-) -> Array:
+    input: FloatND | IntND,  # noqa: A002
+    coordinates: Sequence[FloatND | IntND] | FloatND | IntND,
+) -> FloatND | IntND:
     """Map the input array to new coordinates using linear interpolation.
 
     Modified from JAX implementation of `scipy.ndimage.map_coordinates`.
@@ -71,8 +73,8 @@ def map_coordinates(
 
 
 def _compute_indices_and_weights(
-    coordinate: Array, input_size: int
-) -> list[tuple[Array, Array]]:
+    coordinate: FloatND | IntND, input_size: int
+) -> list[tuple[IntND, FloatND | IntND]]:
     """Compute indices and weights for linear interpolation."""
     lower_index = jnp.clip(jnp.floor(coordinate), 0, input_size - 2).astype(jnp.int32)
     upper_weight = coordinate - lower_index
@@ -80,15 +82,15 @@ def _compute_indices_and_weights(
     return [(lower_index, lower_weight), (lower_index + 1, upper_weight)]
 
 
-def _multiply_all(arrs: Sequence[Array]) -> Array:
+def _multiply_all(arrs: Sequence[FloatND | IntND]) -> FloatND | IntND:
     """Multiply all arrays in the sequence."""
     return functools.reduce(operator.mul, arrs)
 
 
-def _sum_all(arrs: Sequence[Array]) -> Array:
+def _sum_all(arrs: Sequence[FloatND | IntND]) -> FloatND | IntND:
     """Sum all arrays in the sequence."""
     return functools.reduce(operator.add, arrs)
 
 
-def _round_half_away_from_zero(a: Array) -> Array:
+def _round_half_away_from_zero(a: FloatND | IntND) -> FloatND | IntND:
     return a if jnp.issubdtype(a.dtype, jnp.integer) else lax.round(a)
diff --git a/src/lcm/regime_building/next_state.py b/src/lcm/regime_building/next_state.py
index 1e81f0f23..e0639471e 100644
--- a/src/lcm/regime_building/next_state.py
+++ b/src/lcm/regime_building/next_state.py
@@ -6,7 +6,6 @@
 import jax
 from dags import concatenate_functions, with_signature
 from dags.tree import qname_from_tree_path
-from jax import Array
 
 from lcm.grids import Grid
 from lcm.shocks import _ShockGrid
@@ -17,6 +16,7 @@
     DiscreteState,
     FloatND,
     FunctionsMapping,
+    IntND,
     NextStateSimulationFunction,
     RegimeName,
     ShockName,
@@ -93,7 +93,7 @@ def get_next_state_function_for_simulation(
         Returns `{target_regime_name: {next_<state>: array}}`.
 
     """
-    per_target_funcs: dict[RegimeName, Callable[..., dict[str, Array]]] = {}
+    per_target_funcs: dict[RegimeName, Callable[..., dict[str, FloatND | IntND]]] = {}
     for target, target_transitions in transitions.items():
         extended = _extend_target_transitions_for_simulation(
             target=target,
@@ -125,7 +125,7 @@ def get_next_stochastic_weights_function(
     functions: FunctionsMapping,
     transitions: FunctionsMapping,
     stochastic_transition_names: frozenset[TransitionFunctionName],
-) -> Callable[..., dict[str, Array]]:
+) -> Callable[..., dict[str, FloatND | IntND]]:
     """Get function that computes the weights for the next stochastic states.
 
     Args:
@@ -155,11 +155,13 @@ def get_next_stochastic_weights_function(
 def _extend_target_transitions_for_simulation(
     *,
     target: RegimeName,
-    target_transitions: MappingProxyType[TransitionFunctionName, Callable[..., Array]],
+    target_transitions: MappingProxyType[
+        TransitionFunctionName, Callable[..., FloatND | IntND]
+    ],
     all_grids: MappingProxyType[RegimeName, MappingProxyType[StateOrActionName, Grid]],
     variables: Variables,
     stochastic_transition_names: frozenset[TransitionFunctionName],
-) -> dict[TransitionFunctionName, Callable[..., Array]]:
+) -> dict[TransitionFunctionName, Callable[..., FloatND | IntND]]:
     """Replace stochastic transitions for one target with realisation wrappers.
 
     Deterministic transitions are passed through unchanged. Stochastic transitions
@@ -185,7 +187,7 @@ def _extend_target_transitions_for_simulation(
 
     """
     shock_names: frozenset[ShockName] = frozenset(variables.shock_names)
-    extended: dict[TransitionFunctionName, Callable[..., Array]] = dict(
+    extended: dict[TransitionFunctionName, Callable[..., FloatND | IntND]] = dict(
         target_transitions
     )
     for next_state_name in target_transitions:
@@ -236,7 +238,7 @@ def _create_discrete_stochastic_next_func(
     qname = qname_from_tree_path((target, next_state_name))
 
     @with_signature(
-        args={f"weight_{qname}": "FloatND", f"key_{qname}": "dict[str, Array]"},
+        args={f"weight_{qname}": "FloatND", f"key_{qname}": "PRNGKeyND"},
         return_annotation="DiscreteState",
     )
     def next_stochastic_state(**kwargs: FloatND) -> DiscreteState:
@@ -292,9 +294,9 @@ def _create_ar1_next_func(
         qname_from_tree_path((state_name, p)): p for p in grid.params_to_pass_at_runtime
     }
     args: dict[str, str] = {
-        f"key_{qname}": "dict[str, Array]",
+        f"key_{qname}": "PRNGKeyND",
         state_name: "ContinuousState",
-        **dict.fromkeys(runtime_param_names, "float"),
+        **dict.fromkeys(runtime_param_names, "FloatND"),
     }
     _draw_shock = grid.draw_shock
 
@@ -323,8 +325,8 @@ def _create_iid_next_func(
         qname_from_tree_path((state_name, p)): p for p in grid.params_to_pass_at_runtime
     }
     args: dict[str, str] = {
-        f"key_{qname}": "dict[str, Array]",
-        **dict.fromkeys(runtime_param_names, "float"),
+        f"key_{qname}": "PRNGKeyND",
+        **dict.fromkeys(runtime_param_names, "FloatND"),
     }
     _draw_shock = grid.draw_shock
 
diff --git a/src/lcm/regime_building/processing.py b/src/lcm/regime_building/processing.py
index 0b0c78945..aa9c79f77 100644
--- a/src/lcm/regime_building/processing.py
+++ b/src/lcm/regime_building/processing.py
@@ -9,7 +9,6 @@
 from dags import concatenate_functions, get_annotations, with_signature
 from dags.signature import rename_arguments
 from dags.tree import QNAME_DELIMITER, qname_from_tree_path, tree_path_from_qname
-from jax import Array
 from jax import numpy as jnp
 
 from lcm.ages import AgeGrid
@@ -45,9 +44,11 @@
 from lcm.typing import (
     ArgmaxQOverAFunction,
     Float1D,
+    FloatND,
     FunctionsMapping,
     Int1D,
     InternalUserFunction,
+    IntND,
     MaxQOverAFunction,
     NextStateSimulationFunction,
     QAndFFunction,
@@ -866,7 +867,7 @@ def _get_stochastic_next_function_for_shock(
 
     @with_signature(args={f"{name}": "ContinuousState"}, return_annotation="Int1D")
     def next_func(**kwargs: Any) -> Int1D:  # noqa: ARG001, ANN401
-        return jnp.arange(grid.shape[0])
+        return jnp.arange(grid.shape[0], dtype=jnp.int32)
 
     return next_func
 
@@ -884,11 +885,16 @@ def _get_weights_func_for_shock(*, name: str, grid: _ShockGrid) -> UserFunction:
         runtime_param_names = {
             qname_from_tree_path((name, p)): p for p in grid.params_to_pass_at_runtime
         }
-        args = {name: "ContinuousState", **dict.fromkeys(runtime_param_names, "float")}
+        args = {
+            name: "ContinuousState",
+            **dict.fromkeys(runtime_param_names, "FloatND"),
+        }
 
         @with_signature(args=args, return_annotation="FloatND", enforce=False)
-        def weights_func_runtime(*a: Array, **kwargs: Array) -> Float1D:  # noqa: ARG001
-            shock_kw: dict[str, float] = {  # ty: ignore[invalid-assignment]
+        def weights_func_runtime(*a: FloatND, **kwargs: FloatND) -> Float1D:  # noqa: ARG001
+            # `grid.params` is canonical (0-d JAX scalars) from its own
+            # boundary cast; `kwargs` arrive as JAX tracers from JIT.
+            shock_kw: dict[str, FloatND | IntND] = {
                 **fixed_params,
                 **{raw: kwargs[qn] for qn, raw in runtime_param_names.items()},
             }
@@ -899,7 +905,7 @@ def weights_func_runtime(*a: Array, **kwargs: Array) -> Float1D:  # noqa: ARG001
                 input=transition_probs,
                 coordinates=[
                     jnp.full(n_points, fill_value=coord),
-                    jnp.arange(n_points),
+                    jnp.arange(n_points, dtype=jnp.int32),
                 ],
             )
 
@@ -913,13 +919,13 @@ def weights_func_runtime(*a: Array, **kwargs: Array) -> Float1D:  # noqa: ARG001
         return_annotation="FloatND",
         enforce=False,
     )
-    def weights_func(*args: Array, **kwargs: Array) -> Float1D:  # noqa: ARG001
+    def weights_func(*args: FloatND, **kwargs: FloatND) -> Float1D:  # noqa: ARG001
         coordinate = get_irreg_coordinate(value=kwargs[f"{name}"], points=gridpoints)
         return map_coordinates(
             input=transition_probs,
             coordinates=[
                 jnp.full(grid.n_points, fill_value=coordinate),
-                jnp.arange(grid.n_points),
+                jnp.arange(grid.n_points, dtype=jnp.int32),
             ],
         )
 
@@ -1256,7 +1262,8 @@ def _wrap_regime_transition_probs(
         regime_names_to_ids: Immutable mapping of regime names to integer indices.
 
     Returns:
-        A wrapped function that returns MappingProxyType[str, float|Array].
+        A wrapped function that returns an immutable mapping of regime
+        names to probability scalars.
 
     """
     # Get regime names in index order from regime_names_to_ids. Coerce
@@ -1276,8 +1283,8 @@ def _wrap_regime_transition_probs(
     )
     @functools.wraps(func)
     def wrapped(
-        *args: Array | int,
-        **kwargs: Array | int,
+        *args: FloatND | IntND | int,
+        **kwargs: FloatND | IntND | int,
     ) -> MappingProxyType[str, Any]:
         result = func(*args, **kwargs)
         # Convert array to dict using ordering by regime id
@@ -1285,6 +1292,11 @@ def wrapped(
             {name: result[idx] for idx, name in enumerate(regime_names)}
         )
 
+    # Pin `__annotations__` on the final wrapper: `concatenate_functions`
+    # reads `__annotations__` (not `__signature__`) to reconcile the DAG, and
+    # the decorator stack can drop them when `func` carries deferred (PEP 649)
+    # annotations through `functools.wraps`.
+    wrapped.__annotations__ = {**annotations, "return": return_annotation}
     return wrapped
 
 
@@ -1312,15 +1324,20 @@ def _wrap_deterministic_regime_transition(
     # Preserve original annotations but update return type
     annotations = {k: v for k, v in get_annotations(func).items() if k != "return"}
 
-    @with_signature(args=annotations, return_annotation="Array")
+    @with_signature(args=annotations, return_annotation="FloatND")
     @functools.wraps(func)
     def wrapped(
-        *args: Array | int,
-        **kwargs: Array | int,
-    ) -> Array:
+        *args: FloatND | IntND | int,
+        **kwargs: FloatND | IntND | int,
+    ) -> FloatND:
         regime_idx = func(*args, **kwargs)
         return jax.nn.one_hot(regime_idx, n_regimes)
 
+    # Pin `__annotations__` on the final wrapper: `concatenate_functions`
+    # reads `__annotations__` (not `__signature__`) to reconcile the DAG, and
+    # the decorator stack can drop them when `func` carries deferred (PEP 649)
+    # annotations through `functools.wraps`.
+    wrapped.__annotations__ = {**annotations, "return": "FloatND"}
     return wrapped
 
 
diff --git a/src/lcm/regime_building/validation.py b/src/lcm/regime_building/validation.py
index b51a0fdad..471d16548 100644
--- a/src/lcm/regime_building/validation.py
+++ b/src/lcm/regime_building/validation.py
@@ -30,53 +30,32 @@
 )
 
 
-def validate_attribute_types(regime: Regime) -> None:  # noqa: C901, PLR0912
-    """Validate the types of the regime attributes."""
-    error_messages = []
-
-    # Validate types of states and actions
-    for attr_name in ("actions", "states"):
-        attr = getattr(regime, attr_name)
-        if isinstance(attr, Mapping):
-            for k, v in attr.items():
-                if not isinstance(k, str):
-                    error_messages.append(f"{attr_name} key {k} must be a string.")
-                if not isinstance(v, Grid):
-                    error_messages.append(f"{attr_name} value {v} must be an LCM grid.")
-        else:
-            error_messages.append(f"{attr_name} must be a mapping.")
+def validate_mapping_contents(regime: Regime) -> None:
+    """Exhaustively check key/value types of `regime`'s mapping fields.
 
-    # Validate types of function mappings (constraints and functions)
-    function_collections = [
-        regime.constraints,
-        regime.functions,
-    ]
-    for func_collection in function_collections:
-        if isinstance(func_collection, Mapping):
-            for k, v in func_collection.items():
-                if not isinstance(k, str):
-                    error_messages.append(
-                        f"function keys must be a strings, but is {k}."
-                    )
-                if not callable(v) and not isinstance(v, SolveSimulateFunctionPair):
-                    error_messages.append(
-                        f"function values must be a callable, but is {v}."
-                    )
-        else:
-            error_messages.append(
-                "constraints and functions must each be a mapping of callables."
-            )
+    Beartype on `Regime` catches top-level type mismatches and a sampled
+    Mapping entry, but does not deep-check every key/value of a Mapping
+    parameter — especially when the value type is a `Callable`/`Protocol`,
+    which beartype skips entirely. This function fills that gap by
+    iterating each entry and reporting all type violations together via
+    the standard `error_messages` aggregator.
 
-    # Validate state_transitions is a mapping
-    if not isinstance(regime.state_transitions, Mapping):
-        error_messages.append("state_transitions must be a mapping.")
+    """
+    error_messages: list[str] = []
 
-    # Validate regime transition is callable or None
-    if not regime.terminal and not callable(regime.transition):
-        error_messages.append(
-            "transition must be callable or None, "
-            f"but is {type(regime.transition).__name__}."
-        )
+    for attr_name in ("states", "actions"):
+        for k, v in getattr(regime, attr_name).items():
+            if not isinstance(k, str):
+                error_messages.append(f"{attr_name} key {k!r} must be a string.")
+            if not isinstance(v, Grid):
+                error_messages.append(f"{attr_name} value {v!r} must be an LCM grid.")
+
+    for attr_name in ("functions", "constraints"):
+        for k, v in getattr(regime, attr_name).items():
+            if not isinstance(k, str):
+                error_messages.append(f"{attr_name} key {k!r} must be a string.")
+            if not callable(v) and not isinstance(v, SolveSimulateFunctionPair):
+                error_messages.append(f"{attr_name} value {v!r} must be a callable.")
 
     if error_messages:
         msg = format_messages(error_messages)
@@ -262,7 +241,7 @@ def _find_function_output_grid_indexing(
 
 
 def collect_state_transitions(
-    states: Mapping[StateName, Grid],
+    states: Mapping[StateName, Grid | None],
     state_transitions: Mapping[
         StateName,
         UserFunction | Callable | None | Mapping[RegimeName, UserFunction | Callable],
diff --git a/src/lcm/shocks/_base.py b/src/lcm/shocks/_base.py
index 24e33fcbd..de2b1fd80 100644
--- a/src/lcm/shocks/_base.py
+++ b/src/lcm/shocks/_base.py
@@ -1,21 +1,23 @@
 from abc import abstractmethod
 from dataclasses import dataclass, fields
 from types import MappingProxyType
-from typing import ClassVar, overload
+from typing import ClassVar
 
 import jax.numpy as jnp
 import numpy as np
-from jax import Array
 from jax.scipy.stats.norm import cdf
 
 from lcm.exceptions import GridInitializationError
 from lcm.grids import ContinuousGrid
 from lcm.grids import coordinates as grid_coordinates
-from lcm.typing import Float1D, FloatND, ScalarFloat
+from lcm.typing import Float1D, FloatND, ScalarFloat, ScalarInt
 
 
 def _gauss_hermite_normal(
-    *, n_points: int, mu: float | Float1D, sigma: float | Float1D
+    *,
+    n_points: int,
+    mu: ScalarFloat,
+    sigma: ScalarFloat,
 ) -> tuple[Float1D, Float1D]:
     """Compute Gauss-Hermite quadrature nodes and weights for $N(\\mu, \\sigma^2)$.
 
@@ -59,15 +61,27 @@ def _param_field_names(self) -> tuple[str, ...]:
         )
 
     @property
-    def params(self) -> MappingProxyType[str, float]:
-        """Mapping of the distribution's parameters' names to their specified values."""
-        return MappingProxyType(
-            {
-                name: getattr(self, name)
-                for name in self._param_field_names
-                if getattr(self, name) is not None
-            }
-        )
+    def params(self) -> MappingProxyType[str, ScalarFloat | ScalarInt]:
+        """Distribution parameters as canonical 0-d JAX scalars.
+
+        Boundary cast: dataclass fields supplied by the user as Python
+        `bool` / `int` / `float` are returned as `ScalarInt` / `ScalarFloat`
+        so every consumer downstream — `compute_gridpoints`,
+        `compute_transition_probs`, the regime-building runtime closures —
+        sees the canonical dtype.
+
+        """
+        out: dict[str, ScalarFloat | ScalarInt] = {}
+        for name in self._param_field_names:
+            value = getattr(self, name)
+            if value is None:
+                continue
+            # `bool` before `int` — `True` is a Python `int` subclass.
+            if isinstance(value, bool | int):
+                out[name] = jnp.int32(value)
+            else:
+                out[name] = jnp.asarray(value)
+        return MappingProxyType(out)
 
     @property
     def params_to_pass_at_runtime(self) -> tuple[str, ...]:
@@ -82,11 +96,11 @@ def is_fully_specified(self) -> bool:
         return not self.params_to_pass_at_runtime
 
     @abstractmethod
-    def compute_gridpoints(self, **kwargs: float) -> Float1D:
+    def compute_gridpoints(self, **kwargs: ScalarFloat | ScalarInt) -> Float1D:
         """Compute discretized gridpoints for the shock distribution."""
 
     @abstractmethod
-    def compute_transition_probs(self, **kwargs: float) -> FloatND:
+    def compute_transition_probs(self, **kwargs: ScalarFloat | ScalarInt) -> FloatND:
         """Compute transition probability matrix for the shock distribution."""
 
     def get_gridpoints(self) -> Float1D:
@@ -115,11 +129,7 @@ def to_jax(self) -> Float1D:
         """Convert the grid to a Jax array."""
         return self.get_gridpoints()
 
-    @overload
-    def get_coordinate(self, value: ScalarFloat) -> ScalarFloat: ...
-    @overload
-    def get_coordinate(self, value: Array) -> Array: ...
-    def get_coordinate(self, value: ScalarFloat | Array) -> ScalarFloat | Array:
+    def get_coordinate(self, value: FloatND) -> FloatND:
         """Return the generalized coordinate of a value in the grid."""
         if not self.is_fully_specified:
             raise GridInitializationError(
@@ -150,11 +160,11 @@ def _validate_gauss_hermite_grid(
 def _mixture_cdf(
     *,
     x: FloatND,
-    p1: float,
-    mu1: float,
-    sigma1: float,
-    mu2: float,
-    sigma2: float,
+    p1: ScalarFloat,
+    mu1: ScalarFloat,
+    sigma1: ScalarFloat,
+    mu2: ScalarFloat,
+    sigma2: ScalarFloat,
 ) -> FloatND:
     """Evaluate the CDF of a two-component normal mixture.
 
diff --git a/src/lcm/shocks/ar1.py b/src/lcm/shocks/ar1.py
index b307a7b78..1f74f06ff 100644
--- a/src/lcm/shocks/ar1.py
+++ b/src/lcm/shocks/ar1.py
@@ -5,15 +5,17 @@
 
 import jax
 import jax.numpy as jnp
+from beartype import beartype
 from jax.scipy.stats.norm import cdf
 
+from lcm._beartype_conf import GRID_CONF
 from lcm.shocks._base import (
     _gauss_hermite_normal,
     _mixture_cdf,
     _ShockGrid,
     _validate_gauss_hermite_grid,
 )
-from lcm.typing import Float1D, FloatND
+from lcm.typing import Float1D, FloatND, PRNGKeyND, ScalarFloat, ScalarInt
 
 
 @dataclass(frozen=True, kw_only=True)
@@ -23,12 +25,13 @@ class _ShockGridAR1(_ShockGrid):
     @abstractmethod
     def draw_shock(
         self,
-        params: MappingProxyType[str, float | FloatND],
-        key: FloatND,
-        current_value: Float1D,
-    ) -> Float1D: ...
+        params: MappingProxyType[str, ScalarFloat | ScalarInt],
+        key: PRNGKeyND,
+        current_value: ScalarFloat,
+    ) -> ScalarFloat: ...
 
 
+@beartype(conf=GRID_CONF)
 @dataclass(frozen=True, kw_only=True)
 class Tauchen(_ShockGridAR1):
     r"""AR(1) shock discretized via Tauchen (1986).
@@ -49,16 +52,16 @@ class Tauchen(_ShockGridAR1):
     gauss_hermite: bool
     """Use Gauss-Hermite quadrature nodes and weights."""
 
-    rho: float | None = None
+    rho: float | int | None = None
     """Persistence parameter of the AR(1) process."""
 
-    sigma: float | None = None
+    sigma: float | int | None = None
     """Standard deviation of the innovation."""
 
-    mu: float | None = None
+    mu: float | int | None = None
     """Intercept (drift) of the AR(1) process."""
 
-    n_std: float | None = None
+    n_std: float | int | None = None
     """Number of standard deviations for the grid boundary."""
 
     def __post_init__(self) -> None:
@@ -75,7 +78,7 @@ def _param_field_names(self) -> tuple[str, ...]:
             exclude = exclude | {"n_std"}
         return tuple(f.name for f in fields(self) if f.name not in exclude)
 
-    def compute_gridpoints(self, **kwargs: float) -> Float1D:
+    def compute_gridpoints(self, **kwargs: ScalarFloat | ScalarInt) -> Float1D:
         n_points = self.n_points
         rho, sigma, mu = kwargs["rho"], kwargs["sigma"], kwargs["mu"]
         std_y = jnp.sqrt(sigma**2 / (1 - rho**2))
@@ -88,14 +91,14 @@ def compute_gridpoints(self, **kwargs: float) -> Float1D:
         x = jnp.linspace(-x_max, x_max, n_points)
         return x + mu / (1 - rho)
 
-    def compute_transition_probs(self, **kwargs: float) -> FloatND:
+    def compute_transition_probs(self, **kwargs: ScalarFloat | ScalarInt) -> FloatND:
         n_points = self.n_points
         rho, sigma = kwargs["rho"], kwargs["sigma"]
         std_y = jnp.sqrt(sigma**2 / (1 - rho**2))
 
         if self.gauss_hermite:
             nodes, _weights = _gauss_hermite_normal(
-                n_points=n_points, mu=0.0, sigma=std_y
+                n_points=n_points, mu=jnp.asarray(0.0), sigma=std_y
             )
         else:
             n_std = kwargs["n_std"]
@@ -117,10 +120,10 @@ def compute_transition_probs(self, **kwargs: float) -> FloatND:
 
     def draw_shock(
         self,
-        params: MappingProxyType[str, float | FloatND],
-        key: FloatND,
-        current_value: Float1D,
-    ) -> Float1D:
+        params: MappingProxyType[str, ScalarFloat | ScalarInt],
+        key: PRNGKeyND,
+        current_value: ScalarFloat,
+    ) -> ScalarFloat:
         return (
             params["mu"]
             + params["rho"] * current_value
@@ -128,6 +131,7 @@ def draw_shock(
         )
 
 
+@beartype(conf=GRID_CONF)
 @dataclass(frozen=True, kw_only=True)
 class Rouwenhorst(_ShockGridAR1):
     r"""AR(1) shock discretized via Rouwenhorst (1995).
@@ -140,23 +144,23 @@ class Rouwenhorst(_ShockGridAR1):
 
     """
 
-    rho: float | None = None
+    rho: float | int | None = None
     """Persistence parameter of the AR(1) process."""
 
-    sigma: float | None = None
+    sigma: float | int | None = None
     """Standard deviation of the innovation."""
 
-    mu: float | None = None
+    mu: float | int | None = None
     """Intercept (drift) of the AR(1) process."""
 
-    def compute_gridpoints(self, **kwargs: float) -> Float1D:
+    def compute_gridpoints(self, **kwargs: ScalarFloat | ScalarInt) -> Float1D:
         n_points = self.n_points
         rho, sigma, mu = kwargs["rho"], kwargs["sigma"], kwargs["mu"]
         nu = jnp.sqrt((n_points - 1) / (1 - rho**2)) * sigma
         long_run_mean = mu / (1.0 - rho)
         return jnp.linspace(long_run_mean - nu, long_run_mean + nu, n_points)
 
-    def compute_transition_probs(self, **kwargs: float) -> FloatND:
+    def compute_transition_probs(self, **kwargs: ScalarFloat | ScalarInt) -> FloatND:
         n_points = self.n_points
         rho = kwargs["rho"]
         q = (rho + 1) / 2
@@ -185,10 +189,10 @@ def compute_transition_probs(self, **kwargs: float) -> FloatND:
 
     def draw_shock(
         self,
-        params: MappingProxyType[str, float | FloatND],
-        key: FloatND,
-        current_value: Float1D,
-    ) -> Float1D:
+        params: MappingProxyType[str, ScalarFloat | ScalarInt],
+        key: PRNGKeyND,
+        current_value: ScalarFloat,
+    ) -> ScalarFloat:
         return (
             params["mu"]
             + params["rho"] * current_value
@@ -196,6 +200,7 @@ def draw_shock(
         )
 
 
+@beartype(conf=GRID_CONF)
 @dataclass(frozen=True, kw_only=True)
 class TauchenNormalMixture(_ShockGridAR1):
     r"""AR(1) shock with mixture-of-normals innovations, discretized via Tauchen.
@@ -211,44 +216,44 @@ class TauchenNormalMixture(_ShockGridAR1):
 
     """
 
-    rho: float | None = None
+    rho: float | int | None = None
     """Persistence parameter of the AR(1) process."""
 
-    mu: float | None = None
+    mu: float | int | None = None
     """Intercept (drift) of the AR(1) process."""
 
-    n_std: float | None = None
+    n_std: float | int | None = None
     """Number of unconditional standard deviations for the grid boundary."""
 
-    p1: float | None = None
+    p1: float | int | None = None
     """Probability of the first mixture component."""
 
-    mu1: float | None = None
+    mu1: float | int | None = None
     """Mean of the first mixture component."""
 
-    sigma1: float | None = None
+    sigma1: float | int | None = None
     """Standard deviation of the first mixture component."""
 
-    mu2: float | None = None
+    mu2: float | int | None = None
     """Mean of the second mixture component."""
 
-    sigma2: float | None = None
+    sigma2: float | int | None = None
     """Standard deviation of the second mixture component."""
 
     @staticmethod
     def _innovation_variance(
         *,
-        p1: float,
-        mu1: float,
-        sigma1: float,
-        mu2: float,
-        sigma2: float,
-    ) -> float:
+        p1: ScalarFloat,
+        mu1: ScalarFloat,
+        sigma1: ScalarFloat,
+        mu2: ScalarFloat,
+        sigma2: ScalarFloat,
+    ) -> ScalarFloat:
         """Compute the variance of the mixture innovation."""
         mean_eps = p1 * mu1 + (1 - p1) * mu2
         return p1 * (sigma1**2 + mu1**2) + (1 - p1) * (sigma2**2 + mu2**2) - mean_eps**2
 
-    def compute_gridpoints(self, **kwargs: float) -> Float1D:
+    def compute_gridpoints(self, **kwargs: ScalarFloat | ScalarInt) -> Float1D:
         n_points = self.n_points
         rho, mu = kwargs["rho"], kwargs["mu"]
         n_std = kwargs["n_std"]
@@ -264,7 +269,7 @@ def compute_gridpoints(self, **kwargs: float) -> Float1D:
         x_max = n_std * std_y
         return jnp.linspace(long_run_mean - x_max, long_run_mean + x_max, n_points)
 
-    def compute_transition_probs(self, **kwargs: float) -> FloatND:
+    def compute_transition_probs(self, **kwargs: ScalarFloat | ScalarInt) -> FloatND:
         n_points = self.n_points
         rho, mu = kwargs["rho"], kwargs["mu"]
         n_std = kwargs["n_std"]
@@ -298,10 +303,10 @@ def compute_transition_probs(self, **kwargs: float) -> FloatND:
 
     def draw_shock(
         self,
-        params: MappingProxyType[str, float | FloatND],
-        key: FloatND,
-        current_value: Float1D,
-    ) -> Float1D:
+        params: MappingProxyType[str, ScalarFloat | ScalarInt],
+        key: PRNGKeyND,
+        current_value: ScalarFloat,
+    ) -> ScalarFloat:
         key1, key2 = jax.random.split(key)
         component = jax.random.bernoulli(key1, params["p1"])
         normal = jax.random.normal(key2)
diff --git a/src/lcm/shocks/iid.py b/src/lcm/shocks/iid.py
index 60096024c..a6b745ddc 100644
--- a/src/lcm/shocks/iid.py
+++ b/src/lcm/shocks/iid.py
@@ -4,15 +4,17 @@
 
 import jax
 import jax.numpy as jnp
+from beartype import beartype
 from jax.scipy.stats.norm import cdf
 
+from lcm._beartype_conf import GRID_CONF
 from lcm.shocks._base import (
     _gauss_hermite_normal,
     _mixture_cdf,
     _ShockGrid,
     _validate_gauss_hermite_grid,
 )
-from lcm.typing import Float1D, FloatND
+from lcm.typing import Float1D, FloatND, PRNGKeyND, ScalarFloat, ScalarInt
 
 
 @dataclass(frozen=True, kw_only=True)
@@ -22,11 +24,12 @@ class _ShockGridIID(_ShockGrid):
     @abstractmethod
     def draw_shock(
         self,
-        params: MappingProxyType[str, float | FloatND],
-        key: FloatND,
-    ) -> Float1D: ...
+        params: MappingProxyType[str, ScalarFloat | ScalarInt],
+        key: PRNGKeyND,
+    ) -> ScalarFloat: ...
 
 
+@beartype(conf=GRID_CONF)
 @dataclass(frozen=True, kw_only=True)
 class Uniform(_ShockGridIID):
     r"""Discretized iid uniform shock: $U(\text{start}, \text{stop})$.
@@ -36,31 +39,32 @@ class Uniform(_ShockGridIID):
 
     """
 
-    start: float | None = None
+    start: float | int | None = None
     """Lower bound of the uniform distribution."""
 
-    stop: float | None = None
+    stop: float | int | None = None
     """Upper bound of the uniform distribution."""
 
-    def compute_gridpoints(self, **kwargs: float) -> Float1D:
+    def compute_gridpoints(self, **kwargs: ScalarFloat | ScalarInt) -> Float1D:
         return jnp.linspace(
             start=kwargs["start"], stop=kwargs["stop"], num=self.n_points
         )
 
-    def compute_transition_probs(self, **kwargs: float) -> FloatND:  # noqa: ARG002
+    def compute_transition_probs(self, **kwargs: ScalarFloat | ScalarInt) -> FloatND:  # noqa: ARG002
         n_points = self.n_points
         return jnp.full((n_points, n_points), fill_value=1 / n_points)
 
     def draw_shock(
         self,
-        params: MappingProxyType[str, float | FloatND],
-        key: FloatND,
-    ) -> Float1D:
+        params: MappingProxyType[str, ScalarFloat | ScalarInt],
+        key: PRNGKeyND,
+    ) -> ScalarFloat:
         return jax.random.uniform(
             key=key, minval=params["start"], maxval=params["stop"]
         )
 
 
+@beartype(conf=GRID_CONF)
 @dataclass(frozen=True, kw_only=True)
 class Normal(_ShockGridIID):
     r"""Discretized iid normal shock: $N(\mu_\varepsilon, \sigma_\varepsilon^2)$.
@@ -75,13 +79,13 @@ class Normal(_ShockGridIID):
     gauss_hermite: bool
     """Use Gauss-Hermite quadrature nodes and weights."""
 
-    mu: float | None = None
+    mu: float | int | None = None
     """Mean of the shock distribution."""
 
-    sigma: float | None = None
+    sigma: float | int | None = None
     """Standard deviation of the shock distribution."""
 
-    n_std: float | None = None
+    n_std: float | int | None = None
     """Number of standard deviations from the mean to the grid boundary."""
 
     def __post_init__(self) -> None:
@@ -96,7 +100,7 @@ def _param_field_names(self) -> tuple[str, ...]:
             exclude = exclude | {"n_std"}
         return tuple(f.name for f in fields(self) if f.name not in exclude)
 
-    def compute_gridpoints(self, **kwargs: float) -> Float1D:
+    def compute_gridpoints(self, **kwargs: ScalarFloat | ScalarInt) -> Float1D:
         n_points = self.n_points
         mu, sigma = kwargs["mu"], kwargs["sigma"]
         if self.gauss_hermite:
@@ -109,7 +113,7 @@ def compute_gridpoints(self, **kwargs: float) -> Float1D:
         x_max = mu + n_std * sigma
         return jnp.linspace(start=x_min, stop=x_max, num=n_points)
 
-    def compute_transition_probs(self, **kwargs: float) -> FloatND:
+    def compute_transition_probs(self, **kwargs: ScalarFloat | ScalarInt) -> FloatND:
         n_points = self.n_points
         mu, sigma = kwargs["mu"], kwargs["sigma"]
         if self.gauss_hermite:
@@ -129,12 +133,13 @@ def compute_transition_probs(self, **kwargs: float) -> FloatND:
 
     def draw_shock(
         self,
-        params: MappingProxyType[str, float | FloatND],
-        key: FloatND,
-    ) -> Float1D:
+        params: MappingProxyType[str, ScalarFloat | ScalarInt],
+        key: PRNGKeyND,
+    ) -> ScalarFloat:
         return params["mu"] + params["sigma"] * jax.random.normal(key=key)
 
 
+@beartype(conf=GRID_CONF)
 @dataclass(frozen=True, kw_only=True)
 class LogNormal(_ShockGridIID):
     r"""Discretized iid log-normal shock: $\ln X \sim N(\mu, \sigma^2)$."""
@@ -142,13 +147,13 @@ class LogNormal(_ShockGridIID):
     gauss_hermite: bool
     """Use Gauss-Hermite quadrature nodes and weights."""
 
-    mu: float | None = None
+    mu: float | int | None = None
     """Mean of the underlying normal distribution ($E[\\ln X]$)."""
 
-    sigma: float | None = None
+    sigma: float | int | None = None
     """Standard deviation of the underlying normal distribution."""
 
-    n_std: float | None = None
+    n_std: float | int | None = None
     """Number of standard deviations in log-space for the grid boundary."""
 
     def __post_init__(self) -> None:
@@ -163,7 +168,7 @@ def _param_field_names(self) -> tuple[str, ...]:
             exclude = exclude | {"n_std"}
         return tuple(f.name for f in fields(self) if f.name not in exclude)
 
-    def compute_gridpoints(self, **kwargs: float) -> Float1D:
+    def compute_gridpoints(self, **kwargs: ScalarFloat | ScalarInt) -> Float1D:
         n_points = self.n_points
         mu, sigma = kwargs["mu"], kwargs["sigma"]
         if self.gauss_hermite:
@@ -174,7 +179,7 @@ def compute_gridpoints(self, **kwargs: float) -> Float1D:
         n_std = kwargs["n_std"]
         return jnp.exp(jnp.linspace(mu - n_std * sigma, mu + n_std * sigma, n_points))
 
-    def compute_transition_probs(self, **kwargs: float) -> FloatND:
+    def compute_transition_probs(self, **kwargs: ScalarFloat | ScalarInt) -> FloatND:
         n_points = self.n_points
         mu, sigma = kwargs["mu"], kwargs["sigma"]
         if self.gauss_hermite:
@@ -194,12 +199,13 @@ def compute_transition_probs(self, **kwargs: float) -> FloatND:
 
     def draw_shock(
         self,
-        params: MappingProxyType[str, float | FloatND],
-        key: FloatND,
-    ) -> Float1D:
+        params: MappingProxyType[str, ScalarFloat | ScalarInt],
+        key: PRNGKeyND,
+    ) -> ScalarFloat:
         return jnp.exp(params["mu"] + params["sigma"] * jax.random.normal(key=key))
 
 
+@beartype(conf=GRID_CONF)
 @dataclass(frozen=True, kw_only=True)
 class NormalMixture(_ShockGridIID):
     r"""Discretized IID normal-mixture shock.
@@ -215,25 +221,25 @@ class NormalMixture(_ShockGridIID):
 
     """
 
-    n_std: float | None = None
+    n_std: float | int | None = None
     """Number of mixture standard deviations for the grid boundary."""
 
-    p1: float | None = None
+    p1: float | int | None = None
     """Probability of the first mixture component."""
 
-    mu1: float | None = None
+    mu1: float | int | None = None
     """Mean of the first mixture component."""
 
-    sigma1: float | None = None
+    sigma1: float | int | None = None
     """Standard deviation of the first mixture component."""
 
-    mu2: float | None = None
+    mu2: float | int | None = None
     """Mean of the second mixture component."""
 
-    sigma2: float | None = None
+    sigma2: float | int | None = None
     """Standard deviation of the second mixture component."""
 
-    def compute_gridpoints(self, **kwargs: float) -> Float1D:
+    def compute_gridpoints(self, **kwargs: ScalarFloat | ScalarInt) -> Float1D:
         n_points = self.n_points
         n_std = kwargs["n_std"]
         p1, mu1, sigma1 = kwargs["p1"], kwargs["mu1"], kwargs["sigma1"]
@@ -248,7 +254,7 @@ def compute_gridpoints(self, **kwargs: float) -> Float1D:
             mean_eps - n_std * std_eps, mean_eps + n_std * std_eps, n_points
         )
 
-    def compute_transition_probs(self, **kwargs: float) -> FloatND:
+    def compute_transition_probs(self, **kwargs: ScalarFloat | ScalarInt) -> FloatND:
         n_points = self.n_points
         n_std = kwargs["n_std"]
         p1, mu1, sigma1 = kwargs["p1"], kwargs["mu1"], kwargs["sigma1"]
@@ -278,9 +284,9 @@ def compute_transition_probs(self, **kwargs: float) -> FloatND:
 
     def draw_shock(
         self,
-        params: MappingProxyType[str, float | FloatND],
-        key: FloatND,
-    ) -> Float1D:
+        params: MappingProxyType[str, ScalarFloat | ScalarInt],
+        key: PRNGKeyND,
+    ) -> ScalarFloat:
         key1, key2 = jax.random.split(key)
         component = jax.random.bernoulli(key1, params["p1"])
         normal = jax.random.normal(key2)
diff --git a/src/lcm/simulation/compile.py b/src/lcm/simulation/compile.py
index 06a8ceff7..60e554a73 100644
--- a/src/lcm/simulation/compile.py
+++ b/src/lcm/simulation/compile.py
@@ -21,7 +21,6 @@
 import jax
 import jax.numpy as jnp
 from dags.tree import qname_from_tree_path
-from jax import Array
 
 from lcm.ages import AgeGrid
 from lcm.interfaces import InternalRegime
@@ -32,7 +31,9 @@
 )
 from lcm.typing import (
     FlatRegimeParams,
+    FloatND,
     InternalParams,
+    IntND,
     RegimeName,
 )
 from lcm.utils.logging import format_duration
@@ -340,7 +341,7 @@ def _build_argmax_args(
     ages: AgeGrid,
     period: int,
     n_subjects: int,
-    next_regime_to_V_arr: MappingProxyType[RegimeName, Array],
+    next_regime_to_V_arr: MappingProxyType[RegimeName, FloatND],
 ) -> dict[str, object]:
     base = internal_regime.state_action_space(regime_params=regime_params)
     subject_states = _subject_shape_arrays(base.states, n_subjects=n_subjects)
@@ -419,10 +420,10 @@ def _build_crtp_args(
 
 
 def _subject_shape_arrays(
-    base_arrays: Mapping[str, Array],
+    base_arrays: Mapping[str, FloatND | IntND],
     *,
     n_subjects: int,
-) -> dict[str, Array]:
+) -> dict[str, FloatND | IntND]:
     """Return zeros of shape `(n_subjects,)` mirroring each base array's dtype.
 
     With `build_initial_states` casting discrete states to the grid dtype,
diff --git a/src/lcm/simulation/initial_conditions.py b/src/lcm/simulation/initial_conditions.py
index c65db11b6..725e48495 100644
--- a/src/lcm/simulation/initial_conditions.py
+++ b/src/lcm/simulation/initial_conditions.py
@@ -7,16 +7,19 @@
 
 from collections.abc import Callable, Mapping, Sequence
 from types import MappingProxyType
-from typing import Never, cast
+from typing import NoReturn, cast
 
 import jax
 import numpy as np
 import pandas as pd
-from jax import Array
 from jax import numpy as jnp
 
 from lcm.ages import PSEUDO_STATE_NAMES, AgeGrid
-from lcm.dtypes import canonical_float_dtype, safe_to_float_dtype
+from lcm.dtypes import (
+    canonical_float_dtype,
+    safe_to_float_dtype,
+    safe_to_int_dtype,
+)
 from lcm.exceptions import (
     InvalidInitialConditionsError,
     PyLCMError,
@@ -27,13 +30,20 @@
 from lcm.regime_building.Q_and_F import _get_feasibility
 from lcm.typing import (
     ActionName,
+    BoolND,
     FlatRegimeParams,
+    Float1D,
+    FloatND,
+    InitialConditions,
+    Int1D,
     InternalParams,
+    IntND,
     RegimeIdsToNames,
     RegimeName,
     RegimeNamesToIds,
     StateName,
     StatesPerRegime,
+    UserInitialConditions,
 )
 from lcm.utils.containers import invert_regime_ids
 from lcm.utils.functools import get_union_of_args
@@ -44,9 +54,57 @@
 MISSING_CAT_CODE = jnp.iinfo(jnp.int32).min
 
 
+def canonicalize_initial_conditions(
+    *,
+    initial_conditions: UserInitialConditions,
+    internal_regimes: MappingProxyType[RegimeName, InternalRegime],
+) -> InitialConditions:
+    """Cast every initial-conditions array to its canonical pylcm dtype.
+
+    This is pylcm's simulation input boundary: `"regime_id"` and discrete
+    states cast to `int32`; `"age"` and continuous states cast to the canonical
+    float dtype. Keys that match no model state are cast by their array kind
+    (integer arrays to `int32`, otherwise to canonical float) and left for
+    `validate_initial_conditions` to report. Downstream validation and the
+    simulate stack receive canonical-dtype arrays and do not re-cast.
+
+    Args:
+        initial_conditions: Mapping of state names (plus `"regime_id"`) to
+            user-supplied arrays of any integer or floating dtype.
+        internal_regimes: Immutable mapping of regime names to internal regime
+            instances, used to classify each state as discrete or continuous.
+
+    Returns:
+        Mapping of the same keys to JAX arrays at their canonical dtype.
+
+    """
+    discrete_state_names = {
+        state_name
+        for internal_regime in internal_regimes.values()
+        for state_name, grid in internal_regime.grids.items()
+        if isinstance(grid, DiscreteGrid)
+    }
+    known_state_names = {
+        state_name
+        for internal_regime in internal_regimes.values()
+        for state_name in internal_regime.grids
+    }
+    canonical: dict[str, FloatND | IntND] = {}
+    for name, value in initial_conditions.items():
+        if name == "regime_id" or name in discrete_state_names:
+            canonical[name] = safe_to_int_dtype(value, name=name)
+        elif name == "age" or name in known_state_names:
+            canonical[name] = safe_to_float_dtype(value, name=name)
+        elif np.asarray(value).dtype.kind in "iu":
+            canonical[name] = safe_to_int_dtype(value, name=name)
+        else:
+            canonical[name] = safe_to_float_dtype(value, name=name)
+    return MappingProxyType(canonical)
+
+
 def build_initial_states(
     *,
-    initial_states: Mapping[StateName, Array],
+    initial_states: Mapping[StateName, Float1D | Int1D],
     internal_regimes: MappingProxyType[RegimeName, InternalRegime],
 ) -> StatesPerRegime:
     """Build the regime-keyed state carrier from user-provided initial states.
@@ -66,10 +124,12 @@ def build_initial_states(
 
     """
     n_subjects = len(next(iter(initial_states.values())))
-    states_per_regime: dict[RegimeName, MappingProxyType[StateName, Array]] = {}
+    states_per_regime: dict[
+        RegimeName, MappingProxyType[StateName, Float1D | Int1D]
+    ] = {}
 
     for regime_name, internal_regime in internal_regimes.items():
-        regime_states: dict[StateName, Array] = {}
+        regime_states: dict[StateName, Float1D | Int1D] = {}
         # Logic for distribution of subjects over devices
         distributed = any(grid.distributed for grid in internal_regime.grids.values())
         devices = jax.devices()
@@ -121,7 +181,7 @@ def build_initial_states(
 
 def validate_initial_conditions(
     *,
-    initial_conditions: Mapping[str, Array],
+    initial_conditions: InitialConditions,
     internal_regimes: MappingProxyType[RegimeName, InternalRegime],
     regime_names_to_ids: RegimeNamesToIds,
     internal_params: InternalParams,
@@ -130,14 +190,14 @@ def validate_initial_conditions(
     """Validate initial conditions (regimes, states, and feasibility).
 
     Checks that:
-    1. `"regime"` is present, non-empty, and contains only valid regime IDs
+    1. `"regime_id"` is present, non-empty, and contains only valid regime IDs
     2. All required state names (across all regimes) are provided, with no extras
     3. All arrays have the same length
     4. Discrete state values are valid codes
     5. Each subject has at least one feasible action combination
 
     Args:
-        initial_conditions: Mapping of state names (plus `"regime"`) to arrays.
+        initial_conditions: Mapping of state names (plus `"regime_id"`) to arrays.
         internal_regimes: Immutable mapping of regime names to internal regime
             instances.
         regime_names_to_ids: Immutable mapping of regime names to integer IDs.
@@ -154,10 +214,10 @@ def validate_initial_conditions(
     regime_ids_to_names = invert_regime_ids(regime_names_to_ids)
 
     # Extract regime array
-    regime_arr = initial_conditions.get("regime")
+    regime_arr = initial_conditions.get("regime_id")
     if regime_arr is None:
         raise InvalidInitialConditionsError(
-            format_messages(["'regime' must be provided in initial_conditions."])
+            format_messages(["'regime_id' must be provided in initial_conditions."])
         )
 
     # Vectorized regime ID validity check
@@ -174,7 +234,7 @@ def validate_initial_conditions(
             )
         )
 
-    initial_states = {k: v for k, v in initial_conditions.items() if k != "regime"}
+    initial_states = {k: v for k, v in initial_conditions.items() if k != "regime_id"}
 
     # Validate regime names and state names/shapes first; early-exit on errors so that
     # downstream checks (discrete codes, feasibility) can assume correct names.
@@ -239,8 +299,8 @@ def _format_missing_states_message(missing: set[str], required: set[str]) -> str
 
 def _collect_state_name_errors(
     *,
-    initial_states: Mapping[StateName, Array],
-    regime_id_arr: Array,
+    initial_states: Mapping[StateName, FloatND | IntND],
+    regime_id_arr: Int1D,
     regime_ids_to_names: RegimeIdsToNames,
     internal_regimes: MappingProxyType[RegimeName, InternalRegime],
     valid_regime_names: set[RegimeName],
@@ -297,8 +357,8 @@ def _collect_state_name_errors(
 
 def _collect_structural_errors(
     *,
-    initial_states: Mapping[StateName, Array],
-    regime_id_arr: Array,
+    initial_states: Mapping[StateName, FloatND | IntND],
+    regime_id_arr: Int1D,
     regime_ids_to_names: RegimeIdsToNames,
     regime_names_to_ids: RegimeNamesToIds,
     internal_regimes: MappingProxyType[RegimeName, InternalRegime],
@@ -369,12 +429,17 @@ def _collect_structural_errors(
     else:
         # Validate that each subject's initial regime is active at their starting age.
         # Only safe to run when all ages are valid (so age_to_period lookup succeeds).
-        periods = jnp.array([ages.age_to_period(a.item()) for a in age_values])
+        periods = jnp.array(
+            [ages.age_to_period(a.item()) for a in age_values], dtype=jnp.int32
+        )
 
         active_mask = jnp.ones(regime_id_arr.size, dtype=bool)
         for regime_name, internal_regime in internal_regimes.items():
             in_regime = regime_id_arr == regime_names_to_ids[regime_name]
-            period_active = jnp.isin(periods, jnp.array(internal_regime.active_periods))
+            period_active = jnp.isin(
+                periods,
+                jnp.array(internal_regime.active_periods, dtype=jnp.int32),
+            )
             active_mask = active_mask & (~in_regime | period_active)
 
         if not jnp.all(active_mask):
@@ -397,8 +462,8 @@ def _collect_structural_errors(
 
 def _collect_feasibility_errors(
     *,
-    initial_states: Mapping[StateName, Array],
-    regime_id_arr: Array,
+    initial_states: Mapping[StateName, FloatND | IntND],
+    regime_id_arr: Int1D,
     regime_names_to_ids: RegimeNamesToIds,
     internal_regimes: MappingProxyType[RegimeName, InternalRegime],
     internal_params: InternalParams,
@@ -448,9 +513,9 @@ def _collect_feasibility_errors(
 
 def _validate_discrete_state_values(
     *,
-    initial_states: Mapping[StateName, Array],
+    initial_states: Mapping[StateName, FloatND | IntND],
     internal_regimes: MappingProxyType[RegimeName, InternalRegime],
-    regime_id_arr: Array,
+    regime_id_arr: Int1D,
     regime_names_to_ids: RegimeNamesToIds,
 ) -> None:
     """Validate that discrete state values are valid codes.
@@ -512,12 +577,12 @@ def _validate_discrete_state_values(
 
 def _batched_feasibility_check(
     *,
-    feasibility_func: Callable[..., Array],
-    subject_states: Mapping[StateName, Array],
-    action_kwargs: Mapping[str, Array],
+    feasibility_func: Callable[..., BoolND],
+    subject_states: Mapping[str, FloatND | IntND],
+    action_kwargs: Mapping[str, FloatND | IntND],
     filtered_params: Mapping[str, object],
-    flat_actions: Mapping[ActionName, Array],
-) -> Array:
+    flat_actions: Mapping[ActionName, FloatND | IntND],
+) -> BoolND:
     """Check feasibility for all subjects, batching to avoid OOM.
 
     Vmaps over action combos individually (like solve/simulate do) so each
@@ -540,12 +605,14 @@ def _batched_feasibility_check(
     if action_kwargs:
 
         def _is_combo_feasible(
-            action_kw: dict[str, Array],
-            subject_kw: dict[str, Array],
-        ) -> Array:
+            action_kw: dict[str, FloatND | IntND],
+            subject_kw: dict[str, FloatND | IntND],
+        ) -> BoolND:
             return feasibility_func(**action_kw, **subject_kw, **filtered_params)
 
-        def _is_any_action_feasible(per_subject_kwargs: dict[str, Array]) -> Array:
+        def _is_any_action_feasible(
+            per_subject_kwargs: dict[str, FloatND | IntND],
+        ) -> BoolND:
             per_combo = jax.vmap(_is_combo_feasible, in_axes=(0, None))(
                 action_kwargs,
                 per_subject_kwargs,
@@ -554,7 +621,9 @@ def _is_any_action_feasible(per_subject_kwargs: dict[str, Array]) -> Array:
 
     else:
 
-        def _is_any_action_feasible(per_subject_kwargs: dict[str, Array]) -> Array:
+        def _is_any_action_feasible(
+            per_subject_kwargs: dict[str, FloatND | IntND],
+        ) -> BoolND:
             return jnp.any(feasibility_func(**per_subject_kwargs, **filtered_params))
 
     vmapped_check = jax.vmap(_is_any_action_feasible)
@@ -569,7 +638,7 @@ def _is_any_action_feasible(per_subject_kwargs: dict[str, Array]) -> Array:
     if n_subjects <= batch_size:
         return vmapped_check(subject_states)
 
-    results: list[Array] = []
+    results: list[BoolND] = []
     for start in range(0, n_subjects, batch_size):
         end = min(start + batch_size, n_subjects)
         batch = {k: v[start:end] for k, v in subject_states.items()}
@@ -581,7 +650,7 @@ def _check_regime_feasibility(  # noqa: C901
     *,
     internal_regime: InternalRegime,
     regime_name: RegimeName,
-    initial_states: Mapping[StateName, Array],
+    initial_states: Mapping[StateName, FloatND | IntND],
     subject_indices: list[int],
     regime_params: Mapping[str, object],
     ages: AgeGrid,
@@ -617,7 +686,7 @@ def _check_regime_feasibility(  # noqa: C901
     state_action_space = internal_regime.state_action_space(
         regime_params=cast("FlatRegimeParams", MappingProxyType(dict(regime_params))),
     )
-    action_grids: dict[str, Array] = {
+    action_grids: dict[ActionName, FloatND | IntND] = {
         **state_action_space.discrete_actions,
         **state_action_space.continuous_actions,
     }
@@ -632,8 +701,8 @@ def _check_regime_feasibility(  # noqa: C901
     needs_period = "period" in accepted
 
     # Build per-subject state arrays
-    idx_arr = jnp.array(subject_indices)
-    subject_states: dict[StateName, Array] = {}
+    idx_arr = jnp.array(subject_indices, dtype=jnp.int32)
+    subject_states: dict[StateName, FloatND | IntND] = {}
     for sn in state_names:
         if sn in accepted:
             subject_states[sn] = initial_states[sn][idx_arr]
@@ -642,11 +711,12 @@ def _check_regime_feasibility(  # noqa: C901
         subject_states["age"] = initial_states["age"][idx_arr]
     if needs_period:
         subject_states["period"] = jnp.array(
-            [ages.age_to_period(a.item()) for a in initial_states["age"][idx_arr]]
+            [ages.age_to_period(a.item()) for a in initial_states["age"][idx_arr]],
+            dtype=jnp.int32,
         )
 
     # Split actions and params — actions are vmapped over, params are not
-    action_kwargs: dict[str, Array] = {
+    action_kwargs: dict[str, FloatND | IntND] = {
         k: v for k, v in flat_actions.items() if k in accepted
     }
 
@@ -672,7 +742,7 @@ def _check_regime_feasibility(  # noqa: C901
         # No per-subject varying states: feasibility is identical for all subjects.
         if action_kwargs:
 
-            def _check_combo(action_kw: dict[str, Array]) -> Array:
+            def _check_combo(action_kw: dict[str, FloatND | IntND]) -> BoolND:
                 return feasibility_func(**action_kw, **filtered_params)  # ty: ignore[invalid-argument-type]
 
             result = jax.vmap(_check_combo)(action_kwargs)
@@ -704,14 +774,14 @@ def _check_combo(action_kw: dict[str, Array]) -> Array:
 
 def _admits_any_action(
     *,
-    feasibility_func: Callable[..., Array],
-    action_kwargs: Mapping[str, Array],
+    feasibility_func: Callable[..., BoolND],
+    action_kwargs: Mapping[str, FloatND | IntND],
     params: Mapping[str, object],
 ) -> bool:
     """Return True iff the feasibility function admits ≥ 1 action under params."""
     if action_kwargs:
 
-        def _check_combo(action_kw: dict[str, Array]) -> Array:
+        def _check_combo(action_kw: dict[str, FloatND | IntND]) -> BoolND:
             return feasibility_func(**action_kw, **params)
 
         per_combo = jax.vmap(_check_combo)(action_kwargs)
@@ -722,10 +792,10 @@ def _check_combo(action_kw: dict[str, Array]) -> Array:
 def _per_constraint_feasibility(
     *,
     internal_regime: InternalRegime,
-    subject_states: Mapping[StateName, Array],
+    subject_states: Mapping[str, FloatND | IntND],
     regime_params: Mapping[str, object],
-    flat_actions: Mapping[ActionName, Array],
-    idx_arr: Array,
+    flat_actions: Mapping[ActionName, FloatND | IntND],
+    idx_arr: Int1D,
     infeasible_indices: Sequence[int],
 ) -> dict[str, np.ndarray]:
     """Per-constraint feasibility for the infeasible subjects.
@@ -789,8 +859,8 @@ def _raise_feasibility_type_error(
     exc: TypeError,
     regime_name: RegimeName,
     internal_regime: InternalRegime,
-    subject_states: dict[StateName, Array],
-) -> Never:
+    subject_states: dict[StateName, FloatND | IntND],
+) -> NoReturn:
     """Re-raise a TypeError from feasibility checking with diagnostic context.
 
     Args:
@@ -835,7 +905,7 @@ def _format_infeasibility_message(
     infeasible_indices: Sequence[int],
     internal_regime: InternalRegime,
     regime_name: RegimeName,
-    initial_states: Mapping[StateName, Array],
+    initial_states: Mapping[StateName, FloatND | IntND],
     state_names: Sequence[str],
     per_constraint_admits_any: Mapping[str, np.ndarray],
 ) -> str:
@@ -902,8 +972,8 @@ def _format_infeasibility_message(
 def _build_flat_action_grid(
     *,
     action_names: list[ActionName],
-    grids: MappingProxyType[str, Array],
-) -> dict[str, Array]:
+    grids: MappingProxyType[str, FloatND | IntND],
+) -> dict[str, FloatND | IntND]:
     """Build a flat array of all action combinations from action grids.
 
     Args:
diff --git a/src/lcm/simulation/random.py b/src/lcm/simulation/random.py
index b89c0b0eb..69189bc0c 100644
--- a/src/lcm/simulation/random.py
+++ b/src/lcm/simulation/random.py
@@ -1,12 +1,13 @@
 import os
 
 import jax
-from jax import Array
+
+from lcm.typing import PRNGKeyND
 
 
 def generate_simulation_keys(
-    *, key: Array, names: list[str], n_initial_states: int
-) -> tuple[Array, dict[str, Array]]:
+    *, key: PRNGKeyND, names: list[str], n_initial_states: int
+) -> tuple[PRNGKeyND, dict[str, PRNGKeyND]]:
     """Generate pseudo-random number generator keys (PRNG keys) for simulation.
 
     PRNG keys in JAX are immutable objects used to control random number generation.
diff --git a/src/lcm/simulation/result.py b/src/lcm/simulation/result.py
index e5f8c0cb4..44fb57907 100644
--- a/src/lcm/simulation/result.py
+++ b/src/lcm/simulation/result.py
@@ -12,7 +12,6 @@
 import jax.numpy as jnp
 import pandas as pd
 from dags import concatenate_functions
-from jax import Array
 
 from lcm.ages import AgeGrid
 from lcm.exceptions import InvalidAdditionalTargetsError
@@ -23,9 +22,11 @@
 from lcm.regime_building.processing import compute_merged_discrete_categories
 from lcm.typing import (
     ActionName,
+    BoolND,
     FlatRegimeParams,
     FloatND,
     InternalParams,
+    IntND,
     RegimeName,
     RegimeNamesToIds,
     StateName,
@@ -238,7 +239,7 @@ def get_simulation_output_dtypes(
 
     Returns:
         Immutable mapping of variable name to `pd.CategoricalDtype`. Includes
-        all discrete state/action variables plus the `"regime"` column.
+        all discrete state/action variables plus the `"regime_name"` column.
 
     """
     merged_categories, ordered_flags = compute_merged_discrete_categories(regimes)
@@ -250,7 +251,7 @@ def get_simulation_output_dtypes(
             ordered=ordered_flags[var_name],
         )
 
-    dtypes["regime"] = pd.CategoricalDtype(
+    dtypes["regime_name"] = pd.CategoricalDtype(
         categories=list(regime_names_to_ids.keys()),
         ordered=False,
     )
@@ -322,7 +323,7 @@ def _compute_metadata(
     discrete_categories: dict[str, tuple[str, ...]] = {}
     discrete_ordered: dict[str, bool] = {}
     for var_name, dtype in simulation_output_dtypes.items():
-        if var_name == "regime":
+        if var_name == "regime_name":
             continue
         discrete_categories[var_name] = tuple(dtype.categories)
         discrete_ordered[var_name] = bool(dtype.ordered)
@@ -490,13 +491,15 @@ def _process_regime(
     ]
 
     # Concatenate and filter to in-regime subjects
-    data: dict[str, Array | Sequence[str]] = _concatenate_and_filter(period_dicts)  # ty: ignore[invalid-assignment]
+    data: dict[str, FloatND | IntND | BoolND | Sequence[str]] = _concatenate_and_filter(
+        period_dicts
+    )  # ty: ignore[invalid-assignment]
 
     # Add age column (computed from period using ages grid)
     data["age"] = ages.values[data["period"]]  # noqa: PD011
 
     # Add regime name
-    data["regime"] = [internal_regime.name] * len(data["period"])
+    data["regime_name"] = [internal_regime.name] * len(data["period"])
 
     # Compute additional targets
     if additional_targets:
@@ -521,10 +524,10 @@ def _extract_period_data(
     period: int,
     regime_states: tuple[str, ...],
     regime_actions: tuple[str, ...],
-) -> dict[str, Array]:
+) -> dict[str, FloatND | IntND | BoolND]:
     """Extract data from a single period's simulation results."""
-    data: dict[str, Array] = {
-        "subject_id": jnp.arange(len(result.in_regime)),
+    data: dict[str, FloatND | IntND | BoolND] = {
+        "subject_id": jnp.arange(len(result.in_regime), dtype=jnp.int32),
         "period": jnp.full_like(result.in_regime, period, dtype=jnp.int32),
         "_in_regime": result.in_regime,
         "value": result.V_arr,
@@ -541,7 +544,9 @@ def _extract_period_data(
     return data
 
 
-def _concatenate_and_filter(period_dicts: list[dict[str, Array]]) -> dict[str, Array]:
+def _concatenate_and_filter(
+    period_dicts: list[dict[str, FloatND | IntND | BoolND]],
+) -> dict[str, FloatND | IntND | BoolND]:
     """Concatenate period data and filter to in-regime subjects."""
     keys = [k for k in period_dicts[0] if k != "_in_regime"]
 
@@ -585,7 +590,7 @@ def _empty_dataframe(
     action_names: list[ActionName],
 ) -> pd.DataFrame:
     """Create empty DataFrame with correct columns."""
-    columns = ["subject_id", "period", "regime", "value"]
+    columns = ["subject_id", "period", "regime_name", "value"]
     columns.extend(state_names)
     columns.extend(action_names)
     return pd.DataFrame(columns=pd.Index(columns))
@@ -613,8 +618,8 @@ def _reorder_columns(
     state_names: list[StateName],
     action_names: list[ActionName],
 ) -> pd.DataFrame:
-    """Reorder columns: subject_id, period, regime, value, states, actions, rest."""
-    base = ["subject_id", "period", "regime", "value"]
+    """Reorder columns: id, period, regime_name, value, states, actions, rest."""
+    base = ["subject_id", "period", "regime_name", "value"]
     known = set(base) | set(state_names) | set(action_names)
     rest = [c for c in df.columns if c not in known]
     return df[base + state_names + action_names + rest]
@@ -628,14 +633,16 @@ def _convert_to_categorical(
     """Convert discrete columns to pandas Categorical dtype with string labels.
 
     Converts:
-    - regime column: uses regime_names as categories
+    - regime_name column: uses regime_names as categories
     - discrete state/action columns: uses categories from simulation metadata
 
     """
     df = df.copy()
 
-    # Convert regime column
-    df["regime"] = pd.Categorical(df["regime"], categories=metadata.regime_names)
+    # Convert regime name column
+    df["regime_name"] = pd.Categorical(
+        df["regime_name"], categories=metadata.regime_names
+    )
 
     # Convert discrete state and action columns
     for var_name, merged_categories in metadata.discrete_categories.items():
@@ -690,7 +697,7 @@ def _remap_codes_per_regime(
         if regime_cats is None:
             continue
 
-        mask = df["regime"] == regime_name
+        mask = df["regime_name"] == regime_name
         if not mask.any():
             continue
 
@@ -745,11 +752,11 @@ def _codes_to_categorical(
 
 def _compute_targets(
     *,
-    data: dict[str, Array | Sequence[str]],
+    data: dict[str, FloatND | IntND | BoolND | Sequence[str]],
     targets: list[str],
     internal_regime: InternalRegime,
     regime_params: FlatRegimeParams,
-) -> dict[str, Array]:
+) -> dict[str, FloatND | IntND | BoolND]:
     """Compute additional targets for a regime."""
     functions_pool = _build_functions_pool(internal_regime)
     target_func = _create_target_function(
diff --git a/src/lcm/simulation/simulate.py b/src/lcm/simulation/simulate.py
index 70c6f0d6c..efcd61f3d 100644
--- a/src/lcm/simulation/simulate.py
+++ b/src/lcm/simulation/simulate.py
@@ -6,7 +6,7 @@
 import jax
 import jax.numpy as jnp
 import pandas as pd
-from jax import Array, vmap
+from jax import vmap
 
 from lcm.ages import AgeGrid
 from lcm.interfaces import (
@@ -25,14 +25,18 @@
     create_regime_state_action_space,
 )
 from lcm.typing import (
+    Float1D,
     FloatND,
+    InitialConditions,
     Int1D,
     InternalParams,
     IntND,
+    PRNGKeyND,
     RegimeName,
     RegimeNamesToIds,
     ScalarFloat,
     ScalarInt,
+    StateOrActionName,
     StatesPerRegime,
 )
 from lcm.utils.containers import invert_regime_ids
@@ -49,7 +53,7 @@
 def simulate(
     *,
     internal_params: InternalParams,
-    initial_conditions: Mapping[str, Array],
+    initial_conditions: InitialConditions,
     internal_regimes: MappingProxyType[RegimeName, InternalRegime],
     regime_names_to_ids: RegimeNamesToIds,
     logger: logging.Logger,
@@ -64,10 +68,11 @@ def simulate(
 
     Args:
         internal_params: Immutable mapping of regime names to flat parameter mappings.
-        initial_conditions: Flat mapping of state names (plus `"regime"`) to arrays.
-            All arrays must have the same length (number of subjects). The `"regime"`
-            entry must contain integer regime codes.
-            Example: {"wealth": jnp.array([10.0, 50.0]), "regime": jnp.array([0, 0])}
+        initial_conditions: Flat mapping of state names (plus `"regime_id"`) to
+            arrays. All arrays must have the same length (number of subjects).
+            The `"regime_id"` entry must contain integer regime codes.
+            Example:
+            {"wealth": jnp.array([10.0, 50.0]), "regime_id": jnp.array([0, 0])}
         internal_regimes: Immutable mapping of regime names to internal regime
             instances.
         regime_names_to_ids: Immutable mapping of regime names to integer indices.
@@ -91,7 +96,7 @@ def simulate(
     total_start = time.monotonic()
 
     # Extract state arrays from initial conditions, which include the regime on top.
-    initial_states = {k: v for k, v in initial_conditions.items() if k != "regime"}
+    initial_states = {k: v for k, v in initial_conditions.items() if k != "regime_id"}
 
     # Preparations
     key = jax.random.key(seed=seed)
@@ -104,7 +109,7 @@ def simulate(
         initial_ages=initial_states["age"], ages=ages
     )
     subject_regime_ids = jnp.full_like(
-        initial_conditions["regime"], MISSING_CAT_CODE, dtype=jnp.int32
+        initial_conditions["regime_id"], MISSING_CAT_CODE, dtype=jnp.int32
     )
 
     # Forward simulation
@@ -122,7 +127,7 @@ def simulate(
         # Activate subjects whose starting period matches the current period
         subject_regime_ids = jnp.where(
             starting_periods == period,
-            initial_conditions["regime"],
+            initial_conditions["regime_id"],
             subject_regime_ids,
         )
 
@@ -215,8 +220,8 @@ def _simulate_regime_in_period(
     internal_params: InternalParams,
     regime_names_to_ids: RegimeNamesToIds,
     active_regimes_next_period: tuple[RegimeName, ...],
-    key: Array,
-) -> tuple[PeriodRegimeSimulationData, StatesPerRegime, Int1D, Array]:
+    key: PRNGKeyND,
+) -> tuple[PeriodRegimeSimulationData, StatesPerRegime, Int1D, PRNGKeyND]:
     """Simulate one regime for one period.
 
     This function processes all subjects in a given regime for a single period,
@@ -335,8 +340,8 @@ def _simulate_regime_in_period(
 def _lookup_values_from_indices(
     *,
     flat_indices: IntND,
-    grids: MappingProxyType[str, Array],
-) -> MappingProxyType[str, Array]:
+    grids: MappingProxyType[StateOrActionName, FloatND | IntND],
+) -> MappingProxyType[StateOrActionName, FloatND | IntND]:
     """Retrieve values from indices.
 
     Args:
@@ -369,7 +374,7 @@ def _lookup_values_from_indices(
 
 def _compute_starting_periods(
     *,
-    initial_ages: Array,
+    initial_ages: Float1D,
     ages: AgeGrid,
 ) -> Int1D:
     """Convert per-subject initial ages to starting period indices.
diff --git a/src/lcm/simulation/transitions.py b/src/lcm/simulation/transitions.py
index 484fe9f12..9d407a587 100644
--- a/src/lcm/simulation/transitions.py
+++ b/src/lcm/simulation/transitions.py
@@ -10,8 +10,8 @@
 
 import jax
 from dags.tree import qname_from_tree_path
-from jax import Array, vmap
 from jax import numpy as jnp
+from jax import vmap
 
 from lcm.interfaces import InternalRegime, StateActionSpace
 from lcm.simulation.random import generate_simulation_keys
@@ -20,7 +20,11 @@
     ActionName,
     Bool1D,
     FlatRegimeParams,
+    Float1D,
+    FloatND,
     Int1D,
+    IntND,
+    PRNGKeyND,
     RegimeName,
     RegimeNamesToIds,
     RegimeStates,
@@ -69,13 +73,13 @@ def create_regime_state_action_space(
 def calculate_next_states(
     *,
     internal_regime: InternalRegime,
-    optimal_actions: MappingProxyType[ActionName, Array],
+    optimal_actions: MappingProxyType[ActionName, FloatND | IntND],
     period: int,
     age: ScalarInt | ScalarFloat,
     regime_params: FlatRegimeParams,
     states_per_regime: StatesPerRegime,
     state_action_space: StateActionSpace,
-    key: Array,
+    key: PRNGKeyND,
     subjects_in_regime: Bool1D,
 ) -> StatesPerRegime:
     """Calculate next period states for subjects in a regime.
@@ -159,14 +163,14 @@ def calculate_next_regime_membership(
     *,
     internal_regime: InternalRegime,
     state_action_space: StateActionSpace,
-    optimal_actions: MappingProxyType[ActionName, Array],
+    optimal_actions: MappingProxyType[ActionName, FloatND | IntND],
     period: int,
     age: ScalarInt | ScalarFloat,
     regime_params: FlatRegimeParams,
     regime_names_to_ids: RegimeNamesToIds,
     new_subject_regime_ids: Int1D,
     active_regimes_next_period: tuple[RegimeName, ...],
-    key: Array,
+    key: PRNGKeyND,
     subjects_in_regime: Bool1D,
 ) -> Int1D:
     """Calculate next period regime membership for subjects in a regime.
@@ -196,7 +200,7 @@ def calculate_next_regime_membership(
     """
     # Compute regime transition probabilities
     # ---------------------------------------------------------------------------------
-    regime_transition_probs: MappingProxyType[str, Array] = (  # ty: ignore[invalid-assignment]
+    regime_transition_probs: MappingProxyType[RegimeName, FloatND] = (
         internal_regime.simulate_functions.compute_regime_transition_probs(  # ty: ignore[call-non-callable]
             **state_action_space.states,
             **optimal_actions,
@@ -230,9 +234,9 @@ def calculate_next_regime_membership(
 
 def draw_key_from_dict(
     *,
-    d: MappingProxyType[RegimeName, Array],
+    d: MappingProxyType[RegimeName, Float1D],
     regime_names_to_ids: RegimeNamesToIds,
-    keys: Array,
+    keys: PRNGKeyND,
 ) -> Int1D:
     """Draw a random key from a dictionary of arrays.
 
@@ -256,9 +260,9 @@ def draw_key_from_dict(
     )
 
     def random_id(
-        key: Array,
-        p: Array,
-    ) -> Int1D:
+        key: PRNGKeyND,
+        p: Float1D,
+    ) -> ScalarInt:
         return jax.random.choice(
             key,
             regime_ids,
@@ -298,7 +302,7 @@ def _advance_states_for_subjects(
         Updated carrier with next-period values written in for selected subjects.
 
     """
-    updated: dict[RegimeName, dict[StateName, Array]] = {
+    updated: dict[RegimeName, dict[StateName, Float1D | Int1D]] = {
         regime_name: dict(regime_states)
         for regime_name, regime_states in states_per_regime.items()
     }
diff --git a/src/lcm/solution/solve_brute.py b/src/lcm/solution/solve_brute.py
index 3a9a8b9ed..4d88c6a05 100644
--- a/src/lcm/solution/solve_brute.py
+++ b/src/lcm/solution/solve_brute.py
@@ -12,7 +12,7 @@
 
 from lcm.ages import AgeGrid
 from lcm.interfaces import InternalRegime, _build_regime_sharding
-from lcm.typing import FloatND, InternalParams, RegimeName, StateName
+from lcm.typing import BoolND, FloatND, InternalParams, RegimeName, StateName
 from lcm.utils.error_handling import validate_V
 from lcm.utils.logging import (
     format_duration,
@@ -107,8 +107,8 @@ def solve(
     diagnostic_min: list[FloatND] = []
     diagnostic_max: list[FloatND] = []
     diagnostic_mean: list[FloatND] = []
-    running_any_nan: FloatND = jnp.zeros((), dtype=bool)
-    running_any_inf: FloatND = jnp.zeros((), dtype=bool)
+    running_any_nan: BoolND = jnp.zeros((), dtype=bool)
+    running_any_inf: BoolND = jnp.zeros((), dtype=bool)
 
     logger.info("Starting solution")
     total_start = time.monotonic()
@@ -436,7 +436,7 @@ def _get_regime_V_shapes_and_shardings(
     return topology
 
 
-def _build_zero_V_arr(*, topology: _RegimeVTopology) -> jax.Array:
+def _build_zero_V_arr(*, topology: _RegimeVTopology) -> FloatND:
     """Build the zero V-array template for a regime, sharded where requested."""
     zeros = jnp.zeros(topology.shape)
     if topology.sharding is None:
@@ -471,18 +471,17 @@ def _emit_post_loop_diagnostics(
     solution: MappingProxyType[int, MappingProxyType[RegimeName, FloatND]],
     internal_regimes: MappingProxyType[RegimeName, InternalRegime],
     internal_params: InternalParams,
-    running_any_nan: FloatND,
-    running_any_inf: FloatND,
+    running_any_nan: BoolND,
+    running_any_inf: BoolND,
     diagnostic_min: list[FloatND] | None,
     diagnostic_max: list[FloatND] | None,
     diagnostic_mean: list[FloatND] | None,
 ) -> None:
     """Flush async diagnostics: raise on NaN, warn on Inf, log debug stats.
 
-    The two `.item()` calls on the running scalars decide whether to
-    enter the per-row failure-path localisation. On a healthy solve
-    neither inner walk runs and no per-row scalar is materialised, so
-    device memory stays bounded by the V templates currently in flight.
+    Only enters the per-row failure path when the running NaN or Inf
+    accumulators are set, so a healthy solve incurs no host-side scalar
+    materialisation here.
     """
     if running_any_nan.item():
         _raise_first_nan_row(
@@ -516,9 +515,7 @@ def _raise_first_nan_row(
 ) -> None:
     """Find the first NaN-bearing (regime, period) and raise.
 
-    Only invoked on the failure path (`running_any_nan` was True).
-    Materialises one host-side bool per row until the first hit; on
-    a healthy solve this function is never called.
+    Failure-path only — walks rows until the first NaN hit.
     """
     for row in diagnostic_rows:
         V_arr = solution[row.period][row.regime_name]
diff --git a/src/lcm/state_action_space.py b/src/lcm/state_action_space.py
index 32f59bb1e..ed0fe2283 100644
--- a/src/lcm/state_action_space.py
+++ b/src/lcm/state_action_space.py
@@ -1,11 +1,10 @@
 from types import MappingProxyType
 
 import jax.numpy as jnp
-from jax import Array
 
 from lcm.grids import Grid, IrregSpacedGrid
 from lcm.interfaces import StateActionSpace
-from lcm.typing import StateName, StateOrActionName
+from lcm.typing import FloatND, IntND, StateName, StateOrActionName
 from lcm.variables import Variables
 
 
@@ -13,7 +12,7 @@ def create_state_action_space(
     *,
     variables: Variables,
     grids: MappingProxyType[StateOrActionName, Grid],
-    states: dict[StateName, Array] | None = None,
+    states: dict[StateName, FloatND | IntND] | None = None,
 ) -> StateActionSpace:
     """Create a state-action-space.
 
@@ -58,7 +57,7 @@ def create_state_action_space(
     )
 
 
-def _grid_to_jax_or_placeholder(grid: Grid) -> Array:
+def _grid_to_jax_or_placeholder(grid: Grid) -> FloatND | IntND:
     """Return the grid's points, or a NaN placeholder for runtime-supplied grids.
 
     `IrregSpacedGrid.to_jax()` raises when its points haven't been supplied — that
@@ -74,7 +73,9 @@ def _grid_to_jax_or_placeholder(grid: Grid) -> Array:
 
 
 def _validate_all_states_present(
-    *, provided_states: dict[StateName, Array], required_state_names: set[StateName]
+    *,
+    provided_states: dict[StateName, FloatND | IntND],
+    required_state_names: set[StateName],
 ) -> None:
     """Check that all states are present in the provided states."""
     provided_state_names = set(provided_states)
diff --git a/src/lcm/typing.py b/src/lcm/typing.py
index f93b70e52..ada8acb40 100644
--- a/src/lcm/typing.py
+++ b/src/lcm/typing.py
@@ -1,13 +1,15 @@
 from collections.abc import Mapping
+from fractions import Fraction
 from types import MappingProxyType
-from typing import Any, Protocol
+from typing import Any, Literal, Protocol, runtime_checkable
 
+import numpy as np
 import pandas as pd
 from jax import Array
-from jaxtyping import Bool, Float, Int32, Scalar
+from jaxtyping import Bool, Float, Int32, Key, Scalar
 
-from lcm.params import MappingLeaf
-from lcm.params.sequence_leaf import SequenceLeaf
+from lcm.params import MappingLeaf, UserMappingLeaf
+from lcm.params.sequence_leaf import SequenceLeaf, UserSequenceLeaf
 
 type ContinuousState = Float[Array, "..."]
 type ContinuousAction = Float[Array, "..."]
@@ -27,8 +29,16 @@
 type ScalarFloat = Float[Scalar, ""]
 type ScalarBool = Bool[Scalar, ""]
 
+# JAX PRNG keys (`jax.random`) carry the dedicated `key<fry>` dtype, which
+# jaxtyping matches via `Key` — distinct from `FloatND`/`IntND`. Covers both a
+# single 0-d key and a batched 1-d array of keys.
+type PRNGKeyND = Key[Array, "..."]
+
 type Period = ScalarInt
 type Age = ScalarInt | ScalarFloat
+# Boundary form accepted by `AgeGrid.__init__` for `start`, `stop`, and
+# `exact_values` entries — converted to canonical JAX scalars internally.
+type UserAge = int | Fraction
 type RegimeName = str
 type StateName = str
 type ActionName = str
@@ -45,20 +55,59 @@
     RegimeName, MappingProxyType[TransitionFunctionName, InternalUserFunction]
 ]
 
-type RegimeStates = MappingProxyType[StateName, Array]
+type RegimeStates = MappingProxyType[StateName, Float1D | Int1D]
 type StatesPerRegime = MappingProxyType[RegimeName, RegimeStates]
 
+# Boundary form of initial conditions — accepted by `Model.simulate` and
+# canonicalized by `canonicalize_initial_conditions`.
+type UserInitialConditions = Mapping[
+    StateName | Literal["regime_id"], Array | np.ndarray
+]
 
-type _ParamsLeaf = bool | float | Array | pd.Series | MappingLeaf | SequenceLeaf
+# Post-canonicalization form — emitted by `canonicalize_initial_conditions`
+# and consumed by `validate_initial_conditions`, `simulate`, and persistence.
+# Read-protocol typing so callers don't have to wrap a dict in
+# `MappingProxyType` before passing it in; pylcm producers still wrap on
+# the way out to preserve immutability at runtime. Values are 1-D arrays
+# of length `n_subjects`; the validator checks the rank-1 invariant.
+type InitialConditions = Mapping[StateName | Literal["regime_id"], Float1D | Int1D]
+
+
+# Boundary leaf type — accepted by `Model.__init__` / `Model.solve` /
+# `Model.simulate` and canonicalized by `cast_params_to_canonical_dtypes`.
+type _UserParamsLeaf = (
+    bool
+    | int
+    | float
+    | FloatND
+    | IntND
+    | BoolND
+    | np.ndarray
+    | pd.Series
+    | UserMappingLeaf
+    | UserSequenceLeaf
+)
 type UserParams = Mapping[
+    str,
+    _UserParamsLeaf | Mapping[str, _UserParamsLeaf | Mapping[str, _UserParamsLeaf]],
+]
+
+# Post-canonicalization leaf type — output of
+# `cast_params_to_canonical_dtypes`. Only canonical-dtype JAX arrays and
+# canonical-narrow `MappingLeaf` / `SequenceLeaf` instances survive.
+type _ParamsLeaf = FloatND | IntND | BoolND | MappingLeaf | SequenceLeaf
+type Params = Mapping[
     str,
     _ParamsLeaf | Mapping[str, _ParamsLeaf | Mapping[str, _ParamsLeaf]],
 ]
 
 # Internal regime parameters: A flat mapping with function-qualified names.
 # Keys are always function-qualified (e.g., "utility__risk_aversion",
-# "H__discount_factor"). Values are scalars or arrays.
-type FlatRegimeParams = MappingProxyType[str, Array]
+# "H__discount_factor"). Values are canonical-dtype JAX arrays or
+# canonical-narrow container leaves.
+type FlatRegimeParams = MappingProxyType[
+    str, FloatND | IntND | BoolND | MappingLeaf | SequenceLeaf
+]
 type InternalParams = MappingProxyType[RegimeName, FlatRegimeParams]
 
 # Immutable templates, used internally
@@ -72,16 +121,19 @@
 type PeriodToRegimeToVArr = MappingProxyType[int, MappingProxyType[RegimeName, FloatND]]
 
 
+@runtime_checkable
 class UserFunction(Protocol):
     """A function provided by the user.
 
-    Only used for type checking.
+    Used for both type checking and beartype runtime checks on perimeter
+    constructors. Any callable satisfies this protocol structurally.
 
     """
 
     def __call__(self, *args: Any, **kwargs: Any) -> Any: ...  # noqa: ANN401
 
 
+@runtime_checkable
 class InternalUserFunction(Protocol):
     """The internal representation of a function provided by the user.
 
@@ -91,15 +143,18 @@ class InternalUserFunction(Protocol):
 
     def __call__(
         self,
-        *args: Array | float,
-        **kwargs: Array | float,
-    ) -> Array: ...
+        *args: FloatND | IntND | BoolND | float | MappingLeaf | SequenceLeaf,
+        **kwargs: FloatND | IntND | BoolND | float | MappingLeaf | SequenceLeaf,
+    ) -> FloatND | IntND | BoolND: ...
 
 
+@runtime_checkable
 class RegimeTransitionFunction(Protocol):
-    """The regime transition function provided by the user.
+    """The processed regime transition function for the solve phase.
 
-    Returns an array of transition probabilities indexed by regime ID.
+    Wraps the user's `next_regime` function so its output is a mapping of
+    target regime name to a transition-probability array, rather than a
+    raw array indexed by regime id.
 
     Only used for type checking.
 
@@ -107,15 +162,18 @@ class RegimeTransitionFunction(Protocol):
 
     def __call__(
         self,
-        *args: Array | float,
-        **kwargs: Array | float,
-    ) -> Float1D: ...
+        *args: FloatND | IntND | BoolND | float | MappingLeaf | SequenceLeaf,
+        **kwargs: FloatND | IntND | BoolND | float | MappingLeaf | SequenceLeaf,
+    ) -> MappingProxyType[RegimeName, FloatND]: ...
 
 
+@runtime_checkable
 class VmappedRegimeTransitionFunction(Protocol):
-    """The vmapped regime transition function.
+    """The processed regime transition function for the simulate phase.
 
-    Returns a 2D array of transition probabilities with shape [n_regimes, n_subjects].
+    The `vmap`-over-subjects counterpart of `RegimeTransitionFunction`:
+    same mapping output, with each probability array carrying a leading
+    per-subject axis.
 
     Only used for type checking.
 
@@ -123,11 +181,12 @@ class VmappedRegimeTransitionFunction(Protocol):
 
     def __call__(
         self,
-        *args: Array | float,
-        **kwargs: Array | float,
-    ) -> FloatND: ...
+        *args: FloatND | IntND | BoolND | float | MappingLeaf | SequenceLeaf,
+        **kwargs: FloatND | IntND | BoolND | float | MappingLeaf | SequenceLeaf,
+    ) -> MappingProxyType[RegimeName, FloatND]: ...
 
 
+@runtime_checkable
 class QAndFFunction(Protocol):
     """The function that computes Q and F.
 
@@ -145,6 +204,7 @@ def __call__(
     ) -> tuple[FloatND, BoolND]: ...
 
 
+@runtime_checkable
 class MaxQOverAFunction(Protocol):
     """The function that maximizes Q over all actions.
 
@@ -157,11 +217,12 @@ class MaxQOverAFunction(Protocol):
 
     def __call__(
         self,
-        next_regime_to_V_arr: MappingProxyType[RegimeName, Array],
+        next_regime_to_V_arr: MappingProxyType[RegimeName, FloatND],
         **kwargs: Any,  # noqa: ANN401
-    ) -> Array: ...
+    ) -> FloatND: ...
 
 
+@runtime_checkable
 class ArgmaxQOverAFunction(Protocol):
     """The function that finds the argmax of Q over all actions.
 
@@ -174,11 +235,12 @@ class ArgmaxQOverAFunction(Protocol):
 
     def __call__(
         self,
-        next_regime_to_V_arr: MappingProxyType[RegimeName, Array],
+        next_regime_to_V_arr: MappingProxyType[RegimeName, FloatND],
         **kwargs: Any,  # noqa: ANN401
-    ) -> tuple[Array, Array]: ...
+    ) -> tuple[IntND, FloatND]: ...
 
 
+@runtime_checkable
 class StochasticNextFunction(Protocol):
     """The function that simulates the next state of a stochastic variable.
 
@@ -186,9 +248,10 @@ class StochasticNextFunction(Protocol):
 
     """
 
-    def __call__(self, **kwargs: Array) -> Array: ...
+    def __call__(self, **kwargs: FloatND | IntND) -> FloatND | IntND: ...
 
 
+@runtime_checkable
 class NextStateSimulationFunction(Protocol):
     """The function that computes the next states during the simulation.
 
@@ -199,17 +262,25 @@ class NextStateSimulationFunction(Protocol):
 
     def __call__(
         self,
-        **kwargs: Array | Period | Age,
+        **kwargs: FloatND | IntND | Period | Age | MappingLeaf | SequenceLeaf,
     ) -> MappingProxyType[
         RegimeName, MappingProxyType[str, DiscreteState | ContinuousState]
     ]: ...
 
 
+@runtime_checkable
 class ActiveFunction(Protocol):
     """Function that determines if a regime is active at a given age.
 
+    The single positional argument is the age value emitted by the
+    `AgeGrid`; its runtime type is `int` for annual grids and `float`
+    for sub-annual ones. The argument is typed as `Any` so model
+    authors can pin the annotation to whichever concrete type matches
+    their grid (`int` or `float`) without tripping contravariance
+    checks at the assignment site.
+
     Only used for type checking.
 
     """
 
-    def __call__(self, age: float, /) -> bool: ...
+    def __call__(self, age: Any, /) -> bool: ...  # noqa: ANN401
diff --git a/src/lcm/utils/containers.py b/src/lcm/utils/containers.py
index 0c1682d14..346ebe88d 100644
--- a/src/lcm/utils/containers.py
+++ b/src/lcm/utils/containers.py
@@ -5,6 +5,8 @@
 from types import MappingProxyType
 from typing import Any, TypeVar, cast
 
+from lcm.params import UserMappingLeaf, UserSequenceLeaf
+
 T = TypeVar("T")
 
 
@@ -68,11 +70,11 @@ def find_duplicates(*containers: Iterable[T]) -> set[T]:
     return {v for v, count in counts.items() if count > 1}
 
 
-def get_field_names_and_values(dc: type) -> MappingProxyType[str, Any]:
+def get_field_names_and_values(dc: object) -> MappingProxyType[str, Any]:
     """Return the fields of a dataclass.
 
     Args:
-        dc: The dataclass to get the fields of.
+        dc: The dataclass class or instance to get the fields of.
 
     Returns:
         An immutable mapping with the field names as keys and the field values as
@@ -80,7 +82,10 @@ def get_field_names_and_values(dc: type) -> MappingProxyType[str, Any]:
 
     """
     return MappingProxyType(
-        {field.name: getattr(dc, field.name, None) for field in fields(dc)}
+        {
+            field.name: getattr(dc, field.name, None)
+            for field in fields(dc)  # ty: ignore[invalid-argument-type]
+        }
     )
 
 
@@ -118,10 +123,7 @@ def first_non_none(*args: T | None) -> T:
 
 def _make_immutable(value: Any) -> Any:  # noqa: ANN401
     """Recursively convert a value to its immutable equivalent."""
-    from lcm.params import MappingLeaf  # noqa: PLC0415
-    from lcm.params.sequence_leaf import SequenceLeaf  # noqa: PLC0415
-
-    if isinstance(value, (MappingLeaf, SequenceLeaf)):
+    if isinstance(value, (UserMappingLeaf, UserSequenceLeaf)):
         return value  # already immutable by construction
     if isinstance(value, (MappingProxyType, tuple, frozenset)):
         return value
@@ -136,12 +138,9 @@ def _make_immutable(value: Any) -> Any:  # noqa: ANN401
 
 def _make_mutable(value: Any) -> Any:  # noqa: ANN401, PLR0911
     """Recursively convert a value to its mutable equivalent."""
-    from lcm.params import MappingLeaf  # noqa: PLC0415
-    from lcm.params.sequence_leaf import SequenceLeaf  # noqa: PLC0415
-
-    if isinstance(value, MappingLeaf):
+    if isinstance(value, UserMappingLeaf):
         return {k: _make_mutable(v) for k, v in value.data.items()}
-    if isinstance(value, SequenceLeaf):
+    if isinstance(value, UserSequenceLeaf):
         return [_make_mutable(v) for v in value.data]
     if isinstance(value, (set, list)):
         return value
diff --git a/src/lcm/utils/dispatchers.py b/src/lcm/utils/dispatchers.py
index 22367d641..6c2b47841 100644
--- a/src/lcm/utils/dispatchers.py
+++ b/src/lcm/utils/dispatchers.py
@@ -2,14 +2,14 @@
 from collections.abc import Callable
 from functools import partial
 from types import MappingProxyType
-from typing import Literal, TypeVar, cast
+from typing import Any, Literal, TypeVar, cast
 
 import jax
 import jax.numpy as jnp
-from jax import Array, vmap
+from jax import vmap
 
 from lcm.exceptions import FunctionDispatchError
-from lcm.typing import ActionName, Float1D, FloatND, StateName
+from lcm.typing import ActionName, BoolND, FloatND, IntND, StateName
 from lcm.utils.containers import find_duplicates
 from lcm.utils.functools import allow_args, allow_only_kwargs
 
@@ -17,10 +17,12 @@
     "FunctionWithArrayReturn",
     bound=Callable[
         ...,
-        Array
-        | tuple[Array, Array]
-        | MappingProxyType[str, Array]
-        | MappingProxyType[str, MappingProxyType[str, Array]],
+        FloatND
+        | IntND
+        | BoolND
+        | tuple[FloatND | IntND | BoolND, FloatND | IntND | BoolND]
+        | MappingProxyType[str, FloatND | IntND]
+        | MappingProxyType[str, MappingProxyType[str, FloatND | IntND]],
     ],
 )
 
@@ -145,17 +147,12 @@ def vmap_1d(
             in_axes_for_vmap[p] = 0
 
         vmapped = vmap(func, in_axes=in_axes_for_vmap)
-    vmapped.__signature__ = signature
+    vmapped.__signature__ = signature  # ty: ignore[invalid-assignment]
 
     if callable_with == "only_kwargs":
         out = allow_only_kwargs(vmapped, enforce=False)
-    elif callable_with == "only_args":
-        out = vmapped
     else:
-        raise ValueError(
-            f"Invalid callable_with option: {callable_with}. Possible options are "
-            "('only_args', 'only_kwargs')",
-        )
+        out = vmapped
 
     return cast("FunctionWithArrayReturn", out)
 
@@ -242,11 +239,19 @@ def _base_productmap_batched(
         if param.kind == inspect.Parameter.POSITIONAL_ONLY:
             raise FunctionDispatchError(
                 "Positional-only parameters are not allowed in dispatched functions. "
-                f"The parameter '{name}' to the function {func.__name__} "
+                f"The parameter '{name}' to the function "
+                f"{getattr(func, '__name__', repr(func))} "
                 "is POSITIONAL_ONLY."
             )
 
-    def batched_vmap(**kwargs: FloatND) -> FloatND:
+    def batched_vmap(**kwargs: Any) -> Any:  # noqa: ANN401
+        # `batched_vmap` is a generic helper: it accepts whatever values the
+        # composed `func` expects (canonical JAX arrays in the production
+        # pipeline, but also Python scalars, non-canonical-dtype arrays, or
+        # `MappingProxyType` containers in callers that wrap their own pytrees)
+        # and returns whatever `func` returns. Beartype shouldn't constrain
+        # the shape here — the wrapped `func` is responsible for its own
+        # contract.
         non_array_kwargs = {
             key: val for key, val in kwargs.items() if key not in product_axes
         }
@@ -259,8 +264,9 @@ def map_one_more(
             loop_func: FunctionWithArrayReturn, axis: str
         ) -> FunctionWithArrayReturn:
             def func_mapped_over_one_more_axis(
-                *already_mapped_args: Float1D, **already_mapped_kwargs: Float1D
-            ) -> FloatND:
+                *already_mapped_args: Any,  # noqa: ANN401
+                **already_mapped_kwargs: Any,  # noqa: ANN401
+            ) -> Any:  # noqa: ANN401
                 return jax.lax.map(
                     lambda axis_i: loop_func(
                         *already_mapped_args, **{axis: axis_i}, **already_mapped_kwargs
diff --git a/src/lcm/utils/error_handling.py b/src/lcm/utils/error_handling.py
index b9c7e9b25..ffe0c338a 100644
--- a/src/lcm/utils/error_handling.py
+++ b/src/lcm/utils/error_handling.py
@@ -9,7 +9,6 @@
 import jax
 import jax.numpy as jnp
 import pandas as pd
-from jax import Array
 
 from lcm.ages import AgeGrid
 from lcm.exceptions import (
@@ -23,29 +22,34 @@
     FlatRegimeParams,
     FloatND,
     InternalParams,
+    IntND,
     RegimeName,
     ScalarFloat,
     ScalarInt,
     StateName,
+    StateOrActionName,
 )
 
 # Genuine circular import: model.py imports from this module at module level.
-# Safe because Model is only used at runtime in validate_transition_probs,
-# which is never called during module initialisation.
+# The `model` parameter of `validate_transition_probs` is annotated with the
+# fully-qualified string `"lcm.model.Model"` so the beartype claw resolves it
+# by importing `lcm.model` at first call — long after the import cycle settles
+# — rather than at module-init time. Importing `lcm.model` here keeps `lcm` a
+# bound name for the type checker.
 if TYPE_CHECKING:
-    from lcm.model import Model
+    import lcm.model
 
 
 def validate_V(
     *,
-    V_arr: Array,
+    V_arr: FloatND,
     age: float | ScalarInt | ScalarFloat,
     regime_name: RegimeName | None = None,
     partial_solution: object = None,
     compute_intermediates: Callable | None = None,
     state_action_space: StateActionSpace | None = None,
-    next_regime_to_V_arr: MappingProxyType | None = None,
-    internal_params: Mapping | None = None,
+    next_regime_to_V_arr: MappingProxyType[RegimeName, FloatND] | None = None,
+    internal_params: FlatRegimeParams | None = None,
     period: int | None = None,
 ) -> None:
     """Validate the value function array for NaN values.
@@ -128,8 +132,8 @@ def _enrich_with_diagnostics(
     exc: InvalidValueFunctionError,
     compute_intermediates: Callable,
     state_action_space: StateActionSpace,
-    next_regime_to_V_arr: MappingProxyType | None,
-    internal_params: Mapping | None,
+    next_regime_to_V_arr: MappingProxyType[RegimeName, FloatND] | None,
+    internal_params: FlatRegimeParams | None,
     regime_name: RegimeName,
     age: float,
     period: int | None,
@@ -284,12 +288,13 @@ def _format_diagnostic_summary(summary: dict[str, Any]) -> str:
 
 def validate_regime_transition_probs(
     *,
-    regime_transition_probs: MappingProxyType[str, Array],
+    regime_transition_probs: MappingProxyType[RegimeName, FloatND],
     active_regimes_next_period: tuple[RegimeName, ...],
     regime_name: RegimeName,
     age: float | ScalarInt | ScalarFloat,
     next_age: float | ScalarInt | ScalarFloat,
-    state_action_values: MappingProxyType[str, Array] | None = None,
+    state_action_values: MappingProxyType[StateOrActionName, FloatND | IntND]
+    | None = None,
 ) -> None:
     """Validate regime transition probabilities.
 
@@ -352,8 +357,9 @@ def validate_regime_transition_probs(
 
 def _format_sum_violation(
     *,
-    sum_all: Array,
-    state_action_values: MappingProxyType[str, Array] | None = None,
+    sum_all: FloatND,
+    state_action_values: MappingProxyType[StateOrActionName, FloatND | IntND]
+    | None = None,
 ) -> str:
     """Format a human-readable description of probability sum violations.
 
@@ -480,7 +486,7 @@ def _validate_regime_transition_single(
     filtered_params = {k: v for k, v in regime_params.items() if k in accepted_params}
 
     # Collect only grid variables the transition function accepts
-    grids: dict[str, Array] = {
+    grids: dict[StateOrActionName, FloatND | IntND] = {
         k: v for k, v in state_action_space.states.items() if k in accepted_params
     } | {k: v for k, v in state_action_space.actions.items() if k in accepted_params}
 
@@ -488,36 +494,40 @@ def _validate_regime_transition_single(
     grid_var_names = list(grids.keys())
     grid_arrays = list(grids.values())
 
+    # Pin to int32: a Python-int `period` traced through `jax.vmap` becomes
+    # int64 under x64, breaking any int32 `period` contract downstream.
+    period_int32 = jnp.int32(period)
+
     if grid_arrays:
         mesh = jnp.meshgrid(*grid_arrays, indexing="ij")
         flat_arrays = [m.ravel() for m in mesh]
 
         def _call(
-            *args: Array,
+            *args: FloatND | IntND,
             _names: list[str] = grid_var_names,
             _params: dict = filtered_params,
             _func: object = regime_transition_func,
-            _period: int = period,
+            _period: ScalarInt = period_int32,
             _age: ScalarInt | ScalarFloat = ages.values[period],  # noqa: PD011
-        ) -> MappingProxyType[str, Array]:
+        ) -> MappingProxyType[RegimeName, FloatND]:
             kwargs = dict(zip(_names, args, strict=True))
             return _func(  # ty: ignore[call-non-callable]
                 **kwargs, **_params, period=_period, age=_age
             )
 
-        regime_transition_probs: MappingProxyType[str, Array] = jax.vmap(_call)(
-            *flat_arrays
-        )
+        regime_transition_probs: MappingProxyType[RegimeName, FloatND] = jax.vmap(
+            _call
+        )(*flat_arrays)
         point = dict(zip(grid_var_names, flat_arrays, strict=True))
     else:
-        regime_transition_probs: MappingProxyType[str, Array] = (  # ty: ignore[invalid-assignment]
+        regime_transition_probs: MappingProxyType[RegimeName, FloatND] = (
             regime_transition_func(  # ty: ignore[call-non-callable]
                 **filtered_params,
-                period=period,
+                period=period_int32,
                 age=ages.values[period],  # noqa: PD011
             )
         )
-        point: dict[str, Array] = {}
+        point: dict[StateOrActionName, FloatND | IntND] = {}
 
     validate_regime_transition_probs(
         regime_transition_probs=regime_transition_probs,
@@ -540,7 +550,7 @@ def _call(
 def _validate_no_reachable_incomplete_targets(
     *,
     internal_regimes: MappingProxyType[RegimeName, InternalRegime],
-    regime_transition_probs: MappingProxyType[str, Array],
+    regime_transition_probs: MappingProxyType[RegimeName, FloatND],
     active_regimes_next_period: tuple[RegimeName, ...],
     regime_name: RegimeName,
     age: float | ScalarInt | ScalarFloat,
@@ -740,7 +750,7 @@ def _extract_bare_names(slice_node: ast.expr) -> list[str] | None:
 def validate_transition_probs(
     *,
     probs: FloatND,
-    model: Model,
+    model: lcm.model.Model,
     regime_name: RegimeName,
     state_name: StateName,
 ) -> None: ...
@@ -750,7 +760,7 @@ def validate_transition_probs(
 def validate_transition_probs(
     *,
     probs: FloatND,
-    model: Model,
+    model: lcm.model.Model,
     regime_name: RegimeName,
     state_name: StateName,
     target_regime_name: RegimeName,
@@ -761,7 +771,7 @@ def validate_transition_probs(
 def validate_transition_probs(
     *,
     probs: FloatND,
-    model: Model,
+    model: lcm.model.Model,
     regime_name: RegimeName,
 ) -> None: ...
 
@@ -769,7 +779,7 @@ def validate_transition_probs(
 def validate_transition_probs(
     *,
     probs: FloatND,
-    model: Model,
+    model: lcm.model.Model,
     regime_name: RegimeName,
     state_name: StateName | None = None,
     target_regime_name: RegimeName | None = None,
@@ -918,7 +928,7 @@ def _build_expected_shape(
     indexing_params: list[str],
     n_outcomes: int,
     grids: dict[str, DiscreteGrid],
-    model: Model,
+    model: lcm.model.Model,
 ) -> tuple[int, ...]:
     """Compute expected shape for a transition probability array."""
     shape: list[int] = []
diff --git a/src/lcm/utils/functools.py b/src/lcm/utils/functools.py
index 1b8ffa2ef..001eb369c 100644
--- a/src/lcm/utils/functools.py
+++ b/src/lcm/utils/functools.py
@@ -5,6 +5,18 @@
 
 ReturnType = TypeVar("ReturnType")
 
+# `functools.wraps` copies `__annotate__` (PEP 649), which would push the
+# wrapped function's per-parameter annotations onto the generic
+# `(*args: Any, **kwargs: Any)` wrappers below. Beartype claws those
+# wrappers as part of the `lcm` package, sees the inherited annotations,
+# and starts enforcing user-model types on a forwarding wrapper that has
+# no business policing them. Use a reduced assignment set to keep
+# `__name__` / `__qualname__` / `__doc__` for debugging while leaving the
+# wrappers' own `(*args: Any, **kwargs: Any)` annotations intact.
+_WRAPPER_ASSIGNMENTS_NO_ANNOTATIONS: tuple[str, ...] = tuple(
+    name for name in functools.WRAPPER_ASSIGNMENTS if name != "__annotate__"
+)
+
 
 def allow_only_kwargs(
     func: Callable[..., ReturnType], *, enforce: bool = True
@@ -34,7 +46,7 @@ def allow_only_kwargs(
     ]
     new_signature = signature.replace(parameters=new_parameters)
 
-    @functools.wraps(func)
+    @functools.wraps(func, assigned=_WRAPPER_ASSIGNMENTS_NO_ANNOTATIONS)
     def func_with_only_kwargs(*args: Any, **kwargs: Any) -> ReturnType:
         if args:
             raise ValueError(
@@ -119,7 +131,7 @@ def allow_args(func: Callable[..., ReturnType]) -> Callable[..., ReturnType]:
     ]
     new_signature = signature.replace(parameters=new_parameters)
 
-    @functools.wraps(func)
+    @functools.wraps(func, assigned=_WRAPPER_ASSIGNMENTS_NO_ANNOTATIONS)
     def allow_args_wrapper(*args: Any, **kwargs: Any) -> ReturnType:
         # Check if the total number of arguments matches the function signature
         if len(args) + len(kwargs) != len(parameters):
diff --git a/src/lcm/variables.py b/src/lcm/variables.py
index 64eec92cb..81c1fbbbe 100644
--- a/src/lcm/variables.py
+++ b/src/lcm/variables.py
@@ -27,6 +27,19 @@
     from lcm.regime import Regime
 
 
+def _bind_forward_refs(*, regime_cls: type) -> None:
+    """Bind `Regime` into this module's globals.
+
+    The package claw rewrites string annotations on `from_regime`,
+    `get_grids`, and similar helpers into runtime forward references
+    resolved against this module's globals. `lcm.__init__` calls this
+    helper once `Regime` is loaded so the refs resolve at call time
+    without depending on an ad-hoc assignment from outside the module.
+    """
+    global Regime  # noqa: PLW0603
+    Regime = regime_cls  # ty: ignore[invalid-assignment]
+
+
 @dataclasses.dataclass(frozen=True)
 class VariableInfo:
     """Kind/topology/shock tags for one state or action variable."""
diff --git a/src/lcm_examples/mahler_yum_2024/_model.py b/src/lcm_examples/mahler_yum_2024/_model.py
index a237877d9..65c8b79c3 100644
--- a/src/lcm_examples/mahler_yum_2024/_model.py
+++ b/src/lcm_examples/mahler_yum_2024/_model.py
@@ -308,11 +308,11 @@ def savings_constraint(
     return net_income + (wealth) * r >= (saving)
 
 
-def alive_is_active(age: int, final_age_alive: float) -> bool:
+def alive_is_active(age: int, final_age_alive: int) -> bool:
     return age <= final_age_alive
 
 
-def dead_is_active(age: int, initial_age: float) -> bool:
+def dead_is_active(age: int, initial_age: int) -> bool:
     return age > initial_age
 
 
@@ -320,7 +320,7 @@ def dead_is_active(age: int, initial_age: float) -> bool:
 
 ALIVE_REGIME = Regime(
     transition=MarkovTransition(next_regime),
-    active=partial(alive_is_active, final_age_alive=ages.values[-2]),
+    active=partial(alive_is_active, final_age_alive=int(ages.values[-2])),
     states={
         "wealth": LinSpacedGrid(start=0, stop=49, n_points=50),
         "health": DiscreteGrid(Health),
@@ -382,7 +382,7 @@ def dead_utility(discount_type: DiscreteState) -> FloatND:  # noqa: ARG001
 
 DEAD_REGIME = Regime(
     transition=None,
-    active=partial(dead_is_active, initial_age=ages.values[0]),
+    active=partial(dead_is_active, initial_age=int(ages.values[0])),
     states={
         "discount_type": DiscreteGrid(DiscountType),
     },
diff --git a/tests/data/regression_tests/f32/mahler_yum_simulation.pkl b/tests/data/regression_tests/f32/mahler_yum_simulation.pkl
index 3b2191c53..413a1213b 100644
Binary files a/tests/data/regression_tests/f32/mahler_yum_simulation.pkl and b/tests/data/regression_tests/f32/mahler_yum_simulation.pkl differ
diff --git a/tests/data/regression_tests/f32/mortality_simulation.pkl b/tests/data/regression_tests/f32/mortality_simulation.pkl
index c2c8965f4..123d5f888 100644
Binary files a/tests/data/regression_tests/f32/mortality_simulation.pkl and b/tests/data/regression_tests/f32/mortality_simulation.pkl differ
diff --git a/tests/data/regression_tests/f32/precautionary_savings_simulation.pkl b/tests/data/regression_tests/f32/precautionary_savings_simulation.pkl
index 71d2a6e5c..fb1e069b9 100644
Binary files a/tests/data/regression_tests/f32/precautionary_savings_simulation.pkl and b/tests/data/regression_tests/f32/precautionary_savings_simulation.pkl differ
diff --git a/tests/data/regression_tests/f64/mahler_yum_simulation.pkl b/tests/data/regression_tests/f64/mahler_yum_simulation.pkl
index c72a5baae..3a2484840 100644
Binary files a/tests/data/regression_tests/f64/mahler_yum_simulation.pkl and b/tests/data/regression_tests/f64/mahler_yum_simulation.pkl differ
diff --git a/tests/data/regression_tests/f64/mortality_simulation.pkl b/tests/data/regression_tests/f64/mortality_simulation.pkl
index 257224bee..4c4ffe308 100644
Binary files a/tests/data/regression_tests/f64/mortality_simulation.pkl and b/tests/data/regression_tests/f64/mortality_simulation.pkl differ
diff --git a/tests/data/regression_tests/f64/precautionary_savings_simulation.pkl b/tests/data/regression_tests/f64/precautionary_savings_simulation.pkl
index b24163be2..3853de29f 100644
Binary files a/tests/data/regression_tests/f64/precautionary_savings_simulation.pkl and b/tests/data/regression_tests/f64/precautionary_savings_simulation.pkl differ
diff --git a/tests/data/regression_tests/generate_benchmark_data.py b/tests/data/regression_tests/generate_benchmark_data.py
index 748ddd516..b4f4eb4b9 100644
--- a/tests/data/regression_tests/generate_benchmark_data.py
+++ b/tests/data/regression_tests/generate_benchmark_data.py
@@ -62,7 +62,7 @@ def _generate_precautionary_savings(data_dir: Path) -> None:
             "age": jnp.full(n_subjects, 20.0),
             "wealth": jnp.full(n_subjects, 5.0),
             "income": jnp.full(n_subjects, 0.0),
-            "regime": jnp.zeros(n_subjects, dtype=jnp.int32),
+            "regime_id": jnp.zeros(n_subjects, dtype=jnp.int32),
         },
         period_to_regime_to_V_arr=None,
         seed=12345,
@@ -82,7 +82,7 @@ def _generate_mortality(data_dir: Path) -> None:
         initial_conditions={
             "age": jnp.full(n_subjects, 40.0),
             "wealth": jnp.full(n_subjects, 100.0),
-            "regime": jnp.zeros(n_subjects, dtype=jnp.int32),
+            "regime_id": jnp.zeros(n_subjects, dtype=jnp.int32),
         },
         period_to_regime_to_V_arr=None,
         seed=12345,
@@ -102,7 +102,7 @@ def _generate_mahler_yum(data_dir: Path) -> None:
     params = {"alive": common_params}
     initial_conditions = {
         **initial_states,
-        "regime": jnp.full(
+        "regime_id": jnp.full(
             n_subjects,
             model.regime_names_to_ids["alive"],
             dtype=jnp.int32,
diff --git a/tests/data/regression_tests/simulation.pkl b/tests/data/regression_tests/simulation.pkl
index 9dbe57257..305d19b2c 100644
Binary files a/tests/data/regression_tests/simulation.pkl and b/tests/data/regression_tests/simulation.pkl differ
diff --git a/tests/data/shocks/simulation_lognormal.pkl b/tests/data/shocks/simulation_lognormal.pkl
index 14c501599..efc477579 100644
Binary files a/tests/data/shocks/simulation_lognormal.pkl and b/tests/data/shocks/simulation_lognormal.pkl differ
diff --git a/tests/data/shocks/simulation_normal.pkl b/tests/data/shocks/simulation_normal.pkl
index 6d5a01f9d..8e9d616c3 100644
Binary files a/tests/data/shocks/simulation_normal.pkl and b/tests/data/shocks/simulation_normal.pkl differ
diff --git a/tests/data/shocks/simulation_rouwenhorst.pkl b/tests/data/shocks/simulation_rouwenhorst.pkl
index 7dc0961e2..cdb9eafe1 100644
Binary files a/tests/data/shocks/simulation_rouwenhorst.pkl and b/tests/data/shocks/simulation_rouwenhorst.pkl differ
diff --git a/tests/data/shocks/simulation_tauchen.pkl b/tests/data/shocks/simulation_tauchen.pkl
index 6dbec3bc1..004c24214 100644
Binary files a/tests/data/shocks/simulation_tauchen.pkl and b/tests/data/shocks/simulation_tauchen.pkl differ
diff --git a/tests/data/shocks/simulation_uniform.pkl b/tests/data/shocks/simulation_uniform.pkl
index aade3cc8a..d4eff882c 100644
Binary files a/tests/data/shocks/simulation_uniform.pkl and b/tests/data/shocks/simulation_uniform.pkl differ
diff --git a/tests/regime_building/test_create_regime_params_template.py b/tests/regime_building/test_create_regime_params_template.py
index 62d5a5cb4..1f8fa361a 100644
--- a/tests/regime_building/test_create_regime_params_template.py
+++ b/tests/regime_building/test_create_regime_params_template.py
@@ -19,10 +19,10 @@ def test_create_params_without_shocks(binary_category_class):
         transition=lambda: 0,
         functions={"utility": lambda a, b, c: None},  # noqa: ARG005
     )
-    got = create_regime_params_template(regime)  # ty: ignore[invalid-argument-type]
+    got = create_regime_params_template(regime)
     assert got == ensure_containers_are_immutable(
         {
-            "H": {"discount_factor": "float"},
+            "H": {"discount_factor": "FloatND"},
             "utility": {"c": "no_annotation_found"},
             "next_b": {},
             "next_regime": {},
@@ -45,7 +45,7 @@ def custom_H(utility: float, E_next_V: float) -> float:
         },
         functions={"utility": lambda a, b, c: None, "H": custom_H},  # noqa: ARG005
     )
-    got = create_regime_params_template(regime)  # ty: ignore[invalid-argument-type]
+    got = create_regime_params_template(regime)
     assert got == ensure_containers_are_immutable(
         {"H": {}, "utility": {"c": "no_annotation_found"}}
     )
@@ -67,7 +67,7 @@ def test_default_H_with_state_named_discount_factor_is_allowed():
         functions={"utility": lambda a, discount_factor: None},  # noqa: ARG005
         transition=lambda discount_factor: discount_factor,
     )
-    got = create_regime_params_template(regime)  # ty: ignore[invalid-argument-type]
+    got = create_regime_params_template(regime)
     assert got == ensure_containers_are_immutable(
         {
             "H": {},
@@ -95,7 +95,7 @@ def custom_H(utility: float, E_next_V: float, wealth: float) -> float:
         states={"wealth": None},
         functions={"utility": lambda a, wealth: None, "H": custom_H},  # noqa: ARG005
     )
-    got = create_regime_params_template(regime)  # ty: ignore[invalid-argument-type]
+    got = create_regime_params_template(regime)
     assert got == ensure_containers_are_immutable({"H": {}, "utility": {}})
 
 
@@ -124,7 +124,7 @@ def beta_delta_h(
             "H": SolveSimulateFunctionPair(solve=exponential_h, simulate=beta_delta_h),
         },
     )
-    got = create_regime_params_template(regime)  # ty: ignore[invalid-argument-type]
+    got = create_regime_params_template(regime)
     assert set(got["H"]) == {"discount_factor", "beta", "delta"}
 
 
@@ -141,10 +141,10 @@ def test_regular_function_taking_state_as_argument_no_error(binary_category_clas
         transition=lambda: 0,
         functions={"utility": lambda a, wealth, risk_aversion: None},  # noqa: ARG005
     )
-    got = create_regime_params_template(regime)  # ty: ignore[invalid-argument-type]
+    got = create_regime_params_template(regime)
     assert got == ensure_containers_are_immutable(
         {
-            "H": {"discount_factor": "float"},
+            "H": {"discount_factor": "FloatND"},
             "utility": {"risk_aversion": "no_annotation_found"},
             "next_wealth": {},
             "next_regime": {},
@@ -180,10 +180,10 @@ def next_wealth(wealth: float, next_aime: float) -> float:
         transition=lambda: 0,
         functions={"utility": lambda a, wealth, aime: None},  # noqa: ARG005
     )
-    got = create_regime_params_template(regime)  # ty: ignore[invalid-argument-type]
+    got = create_regime_params_template(regime)
     assert got == ensure_containers_are_immutable(
         {
-            "H": {"discount_factor": "float"},
+            "H": {"discount_factor": "FloatND"},
             "utility": {},
             "next_wealth": {},
             "next_aime": {},
diff --git a/tests/regime_building/test_process_params.py b/tests/regime_building/test_process_params.py
index 093df0a9d..14afc2e2b 100644
--- a/tests/regime_building/test_process_params.py
+++ b/tests/regime_building/test_process_params.py
@@ -1,14 +1,23 @@
 """Tests for process_params function."""
 
 from types import MappingProxyType
+from typing import cast
 
 import pytest
 
 from lcm.exceptions import InvalidNameError, InvalidParamsError
+from lcm.interfaces import InternalRegime
 from lcm.params.processing import (
     create_params_template,
     process_params,
 )
+from lcm.typing import ParamsTemplate
+from lcm.utils.containers import ensure_containers_are_immutable
+
+
+def _as_template(plain: dict) -> ParamsTemplate:
+    """Deep-freeze a plain nested dict into a `ParamsTemplate` for tests."""
+    return cast("ParamsTemplate", ensure_containers_are_immutable(plain))
 
 
 def _expected_flat_keys(params_template, regime):
@@ -22,16 +31,18 @@ def _expected_flat_keys(params_template, regime):
 @pytest.fixture
 def params_template():
     """Fixture providing a params_template with two regimes and two functions each."""
-    return {
-        "regime_0": {
-            "fun_0": {"arg_0": float, "arg_1": float},
-            "fun_1": {"arg_0": float, "arg_1": 1.0},
-        },
-        "regime_1": {
-            "fun_0": {"arg_0": float, "arg_1": float},
-            "fun_1": {"arg_0": float, "arg_1": 1.0},
-        },
-    }
+    return _as_template(
+        {
+            "regime_0": {
+                "fun_0": {"arg_0": "float", "arg_1": "float"},
+                "fun_1": {"arg_0": "float", "arg_1": "float"},
+            },
+            "regime_1": {
+                "fun_0": {"arg_0": "float", "arg_1": "float"},
+                "fun_1": {"arg_0": "float", "arg_1": "float"},
+            },
+        }
+    )
 
 
 def test_params_at_function_level(params_template):
@@ -164,16 +175,22 @@ def test_ambiguous_model_regime_level(params_template):
         process_params(params=params, params_template=params_template)
 
 
-class MockRegime:
-    """Mock regime with regime_params_template for testing create_params_template."""
+class MockRegime(InternalRegime):
+    """Mock InternalRegime exposing only `regime_params_template`.
 
-    def __init__(self, regime_params_template: dict) -> None:
-        self._regime_params_template = regime_params_template
+    Inherits from `InternalRegime` so `isinstance(x, InternalRegime)`
+    holds at `create_params_template`'s beartype-checked perimeter, but
+    bypasses `InternalRegime.__init__` to keep test fixtures minimal —
+    `create_params_template` only reads `regime_params_template`.
 
-    @property
-    def regime_params_template(self) -> MappingProxyType:
-        """Return regime_params_template as MappingProxyType."""
-        return MappingProxyType(self._regime_params_template)
+    """
+
+    def __init__(self, regime_params_template: dict) -> None:
+        object.__setattr__(
+            self,
+            "regime_params_template",
+            MappingProxyType(regime_params_template),
+        )
 
 
 def test_function_params_no_qname_separator():
@@ -184,7 +201,7 @@ def test_function_params_no_qname_separator():
         ),
     }
     with pytest.raises(InvalidNameError):
-        create_params_template(internal_regimes)  # ty: ignore[invalid-argument-type]
+        create_params_template(MappingProxyType(internal_regimes))
 
 
 def test_regime_name_no_qname_separator():
@@ -195,7 +212,7 @@ def test_regime_name_no_qname_separator():
         ),
     }
     with pytest.raises(InvalidNameError):
-        create_params_template(internal_regimes)  # ty: ignore[invalid-argument-type]
+        create_params_template(MappingProxyType(internal_regimes))
 
 
 def test_function_name_no_qname_separator():
@@ -206,7 +223,7 @@ def test_function_name_no_qname_separator():
         ),
     }
     with pytest.raises(InvalidNameError):
-        create_params_template(internal_regimes)  # ty: ignore[invalid-argument-type]
+        create_params_template(MappingProxyType(internal_regimes))
 
 
 def test_regime_function_names_disjoint():
@@ -218,7 +235,7 @@ def test_regime_function_names_disjoint():
         ),
     }
     with pytest.raises(InvalidNameError):
-        create_params_template(internal_regimes)  # ty: ignore[invalid-argument-type]
+        create_params_template(MappingProxyType(internal_regimes))
 
 
 def test_regime_argument_names_disjoint():
@@ -230,7 +247,7 @@ def test_regime_argument_names_disjoint():
         ),
     }
     with pytest.raises(InvalidNameError):
-        create_params_template(internal_regimes)  # ty: ignore[invalid-argument-type]
+        create_params_template(MappingProxyType(internal_regimes))
 
 
 def test_missing_parameter_raises_error(params_template):
@@ -271,19 +288,21 @@ def test_passing_same_params_to_regimes_with_different_templates():
     """
     # Template for a non-terminal regime with functions that have parameters
     alive_template = {
-        "discount_factor": float,
-        "utility": {"beta_mean": float, "beta_std": float},
-        "cons_util": {"sigma": float, "bb": float, "kappa": float},
-        "next_health": {"probs_array": float},
+        "H": {"discount_factor": "float"},
+        "utility": {"beta_mean": "float", "beta_std": "float"},
+        "cons_util": {"sigma": "float", "bb": "float", "kappa": "float"},
+        "next_health": {"probs_array": "float"},
     }
 
     # Template for a terminal regime - empty (no H, no transitions)
-    dead_template: dict[str, type] = {}
+    dead_template: dict[str, dict[str, str]] = {}
 
-    params_template = {
-        "alive": alive_template,
-        "dead": dead_template,
-    }
+    params_template = _as_template(
+        {
+            "alive": alive_template,
+            "dead": dead_template,
+        }
+    )
 
     # User's shared params dict - has all parameters for the alive regime
     shared_params = {
@@ -303,7 +322,7 @@ def test_passing_same_params_to_regimes_with_different_templates():
     # InvalidParamsError is raised because dead__cons_util__*, dead__utility__*,
     # etc. are not in the template
     with pytest.raises(InvalidParamsError, match="Unknown keys"):
-        process_params(params=params, params_template=params_template)  # ty: ignore[invalid-argument-type]
+        process_params(params=params, params_template=params_template)
 
 
 def test_shock_params_via_regular_params():
@@ -313,13 +332,15 @@ def test_shock_params_via_regular_params():
     state name (e.g., "adjustment_cost"), so users can pass them via regular params.
     """
     # Template includes ShockGrid params under the state name
-    params_template = {
-        "working_life": {
-            "discount_factor": float,
-            "utility": {"param": float},
-            "adjustment_cost": {"start": float, "stop": float},
-        },
-    }
+    params_template = _as_template(
+        {
+            "working_life": {
+                "H": {"discount_factor": "float"},
+                "utility": {"param": "float"},
+                "adjustment_cost": {"start": "float", "stop": "float"},
+            },
+        }
+    )
 
     params = {
         "working_life": {
@@ -332,6 +353,6 @@ def test_shock_params_via_regular_params():
         },
     }
 
-    result = process_params(params=params, params_template=params_template)  # ty: ignore[invalid-argument-type]
+    result = process_params(params=params, params_template=params_template)
     assert result["working_life"]["adjustment_cost__start"] == 0
     assert result["working_life"]["adjustment_cost__stop"] == 1
diff --git a/tests/regime_building/test_regime_processing.py b/tests/regime_building/test_regime_processing.py
index 7c60a9a51..aa3357fef 100644
--- a/tests/regime_building/test_regime_processing.py
+++ b/tests/regime_building/test_regime_processing.py
@@ -37,7 +37,7 @@ def next_c(a, b):
         functions={"utility": utility},
     )
 
-    got = Variables.from_regime(regime_mock)  # ty: ignore[invalid-argument-type]
+    got = Variables.from_regime(regime_mock)
 
     assert isinstance(got, Variables)
     assert set(got) == {"a", "c"}
@@ -60,7 +60,7 @@ def next_c(a, b):
         functions={"utility": lambda _c: None},
     )
 
-    got = get_grids(regime_mock)  # ty: ignore[invalid-argument-type]
+    got = get_grids(regime_mock)
     assert isinstance(got["a"], DiscreteGrid)
     assert got["a"].categories == ("cat0", "cat1")
     assert got["a"].codes == (0, 1)
@@ -95,7 +95,7 @@ def next_state(a, b):
         functions={"utility": lambda _c: None},
     )
 
-    got = get_grids(regime_mock)  # ty: ignore[invalid-argument-type]
+    got = get_grids(regime_mock)
     assert list(got.keys()) == ["c", "b", "e", "d", "f", "a"]
 
 
diff --git a/tests/regime_mock.py b/tests/regime_mock.py
index 84157d35d..5da6c9138 100644
--- a/tests/regime_mock.py
+++ b/tests/regime_mock.py
@@ -1,41 +1,67 @@
-from dataclasses import dataclass, field
+from types import MappingProxyType
 from typing import Literal, cast
 
 from lcm.grids import Grid
 from lcm.interfaces import SolveSimulateFunctionPair
-from lcm.regime import _default_H
+from lcm.regime import Regime, _default_H
 from lcm.regime_building.validation import collect_state_transitions
 from lcm.typing import UserFunction
 
 
-@dataclass
-class RegimeMock:
+class RegimeMock(Regime):
     """A regime mock for testing the params_template creation functions.
 
-    This dataclass has the same attributes as the Regime dataclass, but does not perform
-    any checks, which helps us test the params_template creation functions in isolation.
+    Inherits from `Regime` so `isinstance(x, Regime)` holds at the
+    beartype-checked perimeter of `create_regime_params_template` and
+    friends, but bypasses `Regime.__init__`'s validation by writing
+    fields directly via `object.__setattr__`. Tests use this to supply
+    partial / loosely-typed configurations that the real `Regime`
+    constructor would reject.
 
     """
 
-    n_periods: int | None = None
-    actions: dict[str, Grid | None] | None = None
-    states: dict[str, Grid | None] | None = None
-    state_transitions: dict[str, UserFunction | None] = field(default_factory=dict)
-    constraints: dict[str, UserFunction] = field(default_factory=dict)
-    transition: UserFunction | None = None
-    functions: dict[str, UserFunction] = field(default_factory=dict)
+    def __init__(
+        self,
+        *,
+        n_periods: int | None = None,
+        actions: dict[str, Grid | None] | None = None,
+        states: dict[str, Grid | None] | None = None,
+        state_transitions: dict[str, UserFunction | None] | None = None,
+        constraints: dict[str, UserFunction] | None = None,
+        transition: UserFunction | None = None,
+        functions: dict[str, UserFunction] | None = None,
+    ) -> None:
+        object.__setattr__(self, "n_periods", n_periods)
+        object.__setattr__(self, "actions", actions if actions is not None else {})
+        object.__setattr__(self, "states", states if states is not None else {})
+        object.__setattr__(
+            self,
+            "state_transitions",
+            state_transitions if state_transitions is not None else {},
+        )
+        object.__setattr__(
+            self, "constraints", constraints if constraints is not None else {}
+        )
+        object.__setattr__(self, "transition", transition)
+        object.__setattr__(
+            self, "functions", functions if functions is not None else {}
+        )
+        # Match Regime's defaults for fields RegimeMock callers don't touch
+        object.__setattr__(self, "active", lambda _age: True)
+        object.__setattr__(self, "derived_categoricals", MappingProxyType({}))
+        object.__setattr__(self, "description", "")
+        # `Regime.__post_init__` injects the default `H` for non-terminal
+        # regimes; mirror that here.
+        if not self.terminal and "H" not in self.functions:
+            object.__setattr__(self, "functions", {**self.functions, "H": _default_H})
 
     @property
     def terminal(self) -> bool:
         return self.transition is None
 
-    def __post_init__(self) -> None:
-        if not self.terminal and "H" not in self.functions:
-            self.functions = {**self.functions, "H": _default_H}
-
     def get_all_functions(
         self, phase: Literal["solve", "simulate"] = "solve"
-    ) -> dict[str, UserFunction]:
+    ) -> MappingProxyType[str, UserFunction]:
         """Get all regime functions including utility, constraints, and transitions."""
         result: dict[str, UserFunction] = {}
         for name, func in self.functions.items():
@@ -54,4 +80,4 @@ def get_all_functions(
             )
         if self.transition:
             result["next_regime"] = self.transition
-        return result
+        return MappingProxyType(result)
diff --git a/tests/simulation/test_initial_conditions.py b/tests/simulation/test_initial_conditions.py
index d54c56db1..2d44a9e35 100644
--- a/tests/simulation/test_initial_conditions.py
+++ b/tests/simulation/test_initial_conditions.py
@@ -21,8 +21,10 @@
     ScalarInt,
 )
 
-_ACTIVE = 0
-_TERMINAL = 1
+# Regime-id codes are canonical `int32`; `validate_initial_conditions` is an
+# internal function that receives already-canonicalized initial conditions.
+_ACTIVE = jnp.int32(0)
+_TERMINAL = jnp.int32(1)
 
 
 @pytest.fixture
@@ -112,7 +114,7 @@ def test_validate_initial_conditions_valid_input(
             "age": jnp.array([0.0, 0.0]),
             "wealth": jnp.array([10.0, 50.0]),
             "health": jnp.array([0, 1]),
-            "regime": jnp.array([_ACTIVE, _ACTIVE]),
+            "regime_id": jnp.array([_ACTIVE, _ACTIVE]),
         },
         internal_regimes=model.internal_regimes,
         regime_names_to_ids=model.regime_names_to_ids,
@@ -132,7 +134,7 @@ def test_validate_initial_conditions_missing_state(
             initial_conditions={
                 "age": jnp.array([0.0, 0.0]),
                 "wealth": jnp.array([10.0, 50.0]),
-                "regime": jnp.array([_ACTIVE, _ACTIVE]),
+                "regime_id": jnp.array([_ACTIVE, _ACTIVE]),
             },
             internal_regimes=model.internal_regimes,
             regime_names_to_ids=model.regime_names_to_ids,
@@ -152,7 +154,7 @@ def test_validate_initial_conditions_extra_state(
                 "wealth": jnp.array([10.0]),
                 "health": jnp.array([0]),
                 "unknown": jnp.array([1.0]),
-                "regime": jnp.array([_ACTIVE]),
+                "regime_id": jnp.array([_ACTIVE]),
             },
             internal_regimes=model.internal_regimes,
             regime_names_to_ids=model.regime_names_to_ids,
@@ -171,7 +173,7 @@ def test_validate_initial_conditions_inconsistent_lengths(
                 "age": jnp.array([0.0, 0.0]),
                 "wealth": jnp.array([10.0, 20.0]),
                 "health": jnp.array([0]),
-                "regime": jnp.array([_ACTIVE, _ACTIVE]),
+                "regime_id": jnp.array([_ACTIVE, _ACTIVE]),
             },
             internal_regimes=model.internal_regimes,
             regime_names_to_ids=model.regime_names_to_ids,
@@ -190,7 +192,7 @@ def test_validate_initial_conditions_invalid_discrete_value(
                 "age": jnp.array([0.0]),
                 "wealth": jnp.array([10.0]),
                 "health": jnp.array([5]),
-                "regime": jnp.array([_ACTIVE]),
+                "regime_id": jnp.array([_ACTIVE]),
             },
             internal_regimes=model.internal_regimes,
             regime_names_to_ids=model.regime_names_to_ids,
@@ -209,7 +211,7 @@ def test_validate_initial_conditions_invalid_regime_id(
                 "age": jnp.array([0.0]),
                 "wealth": jnp.array([10.0]),
                 "health": jnp.array([0]),
-                "regime": jnp.array([99]),
+                "regime_id": jnp.array([99]),
             },
             internal_regimes=model.internal_regimes,
             regime_names_to_ids=model.regime_names_to_ids,
@@ -228,7 +230,7 @@ def test_validate_initial_conditions_invalid_age_values(
                 "age": jnp.array([0.0, 99.0]),
                 "wealth": jnp.array([10.0, 50.0]),
                 "health": jnp.array([0, 1]),
-                "regime": jnp.array([_ACTIVE, _ACTIVE]),
+                "regime_id": jnp.array([_ACTIVE, _ACTIVE]),
             },
             internal_regimes=model.internal_regimes,
             regime_names_to_ids=model.regime_names_to_ids,
@@ -313,7 +315,7 @@ def test_infeasible_initial_states_detected():
             initial_conditions={
                 "age": jnp.array([0.0]),
                 "wealth": jnp.array([0.25]),
-                "regime": jnp.array([_working_life]),
+                "regime_id": jnp.array([_working_life]),
             },
             period_to_regime_to_V_arr=None,
         )
@@ -339,7 +341,7 @@ def test_on_grid_state_but_combination_infeasible():
             initial_conditions={
                 "age": jnp.array([0.0]),
                 "wealth": jnp.array([0.3]),
-                "regime": jnp.array([_working_life]),
+                "regime_id": jnp.array([_working_life]),
             },
             period_to_regime_to_V_arr=None,
         )
@@ -360,7 +362,7 @@ def test_extrapolated_initial_states_accepted():
         initial_conditions={
             "age": jnp.array([0.0]),
             "wealth": jnp.array([1.0]),
-            "regime": jnp.array([_working_life]),
+            "regime_id": jnp.array([_working_life]),
         },
         period_to_regime_to_V_arr=None,
     )
@@ -381,7 +383,7 @@ def test_on_grid_initial_states_accepted():
         initial_conditions={
             "age": jnp.array([0.0]),
             "wealth": jnp.array([5.0]),
-            "regime": jnp.array([_working_life]),
+            "regime_id": jnp.array([_working_life]),
         },
         period_to_regime_to_V_arr=None,
     )
@@ -403,7 +405,7 @@ def test_irreg_spaced_grid_with_runtime_points():
             params=params,
             initial_conditions={
                 "wealth": jnp.array([0.3]),
-                "regime": jnp.array([_working_life]),
+                "regime_id": jnp.array([_working_life]),
             },
             period_to_regime_to_V_arr=None,
         )
@@ -421,7 +423,7 @@ def test_missing_age_error_message(
             initial_conditions={
                 "wealth": jnp.array([10.0]),
                 "health": jnp.array([0]),
-                "regime": jnp.array([_ACTIVE]),
+                "regime_id": jnp.array([_ACTIVE]),
             },
             internal_regimes=model.internal_regimes,
             regime_names_to_ids=model.regime_names_to_ids,
@@ -560,7 +562,7 @@ def test_subject_in_inactive_regime_at_starting_age() -> None:
             initial_conditions={
                 "age": jnp.array([0.0]),
                 "wealth": jnp.array([10.0]),
-                "regime": jnp.array([_dead]),
+                "regime_id": jnp.array([_dead]),
             },
             internal_regimes=model.internal_regimes,
             regime_names_to_ids=model.regime_names_to_ids,
@@ -581,7 +583,7 @@ def test_all_subjects_in_regime_with_fewer_states() -> None:
         initial_conditions={
             "age": jnp.array([2.0, 2.0]),
             "wealth": jnp.array([10.0, 50.0]),
-            "regime": jnp.array([_dead, _dead]),
+            "regime_id": jnp.array([_dead, _dead]),
         },
         internal_regimes=model.internal_regimes,
         regime_names_to_ids=model.regime_names_to_ids,
@@ -607,7 +609,7 @@ def test_mixed_regimes_all_union_states_provided() -> None:
             "age": jnp.array([0.0, 2.0]),
             "wealth": jnp.array([10.0, 50.0]),
             "health": jnp.array([0, 0]),
-            "regime": jnp.array([_alive, _dead]),
+            "regime_id": jnp.array([_alive, _dead]),
         },
         internal_regimes=model.internal_regimes,
         regime_names_to_ids=model.regime_names_to_ids,
@@ -628,7 +630,7 @@ def test_constraint_not_checked_for_unused_regime() -> None:
         initial_conditions={
             "age": jnp.array([2.0]),
             "wealth": jnp.array([40.0]),
-            "regime": jnp.array([_dead]),
+            "regime_id": jnp.array([_dead]),
         },
         internal_regimes=model.internal_regimes,
         regime_names_to_ids=model.regime_names_to_ids,
@@ -650,7 +652,7 @@ def test_constraint_checked_for_starting_regime() -> None:
             initial_conditions={
                 "age": jnp.array([0.0]),
                 "wealth": jnp.array([40.0]),
-                "regime": jnp.array([_alive]),
+                "regime_id": jnp.array([_alive]),
             },
             internal_regimes=model.internal_regimes,
             regime_names_to_ids=model.regime_names_to_ids,
@@ -676,7 +678,7 @@ def test_mixed_regimes_constraint_only_checked_for_starting_regime() -> None:
             initial_conditions={
                 "age": jnp.array([0.0, 2.0]),
                 "wealth": jnp.array([40.0, 40.0]),
-                "regime": jnp.array([_alive, _dead]),
+                "regime_id": jnp.array([_alive, _dead]),
             },
             internal_regimes=model.internal_regimes,
             regime_names_to_ids=model.regime_names_to_ids,
diff --git a/tests/simulation/test_simulate.py b/tests/simulation/test_simulate.py
index 66d62f225..4802f21a8 100644
--- a/tests/simulation/test_simulate.py
+++ b/tests/simulation/test_simulate.py
@@ -89,14 +89,14 @@ def test_simulate_using_raw_inputs(simulate_inputs):
         initial_conditions={
             "wealth": jnp.array([1.0, 50.400803]),
             "age": jnp.array([0.0, 0.0]),
-            "regime": jnp.array(
+            "regime_id": jnp.array(
                 [simulate_inputs["regime_names_to_ids"]["working_life"]] * 2
             ),
         },
         logger=get_logger(log_level="off"),
         **simulate_inputs,
     )
-    got = result.to_dataframe().query('regime == "working_life"')
+    got = result.to_dataframe().query('regime_name == "working_life"')
 
     assert (got["labor_supply"] == "retire").all()
     assert_array_almost_equal(got["consumption"], [1.0, 50.400803])
@@ -148,12 +148,12 @@ def test_simulate_using_model_methods(
         initial_conditions={
             "wealth": jnp.array([20.0, 150, 250, 320]),
             "age": jnp.array([18.0, 18.0, 18.0, 18.0]),
-            "regime": jnp.array([RegimeId.working_life] * 4),
+            "regime_id": jnp.array([RegimeId.working_life] * 4),
         },
     )
     df = result.to_dataframe(
         additional_targets=["utility", "borrowing_constraint"]
-    ).query('regime == "working_life"')
+    ).query('regime_name == "working_life"')
 
     # Check expected columns
     expected_cols = {
@@ -166,7 +166,7 @@ def test_simulate_using_model_methods(
         "utility",
         "borrowing_constraint",
         "subject_id",
-        "regime",
+        "regime_name",
     }
     assert expected_cols == set(df.columns)
 
@@ -194,18 +194,18 @@ def test_simulate_with_only_discrete_actions():
     )
 
     model = get_model(n_periods=3)
-    params = get_params(n_periods=3, wage=1.5, discount_factor=1, interest_rate=0)
+    params = get_params(n_periods=3, wage=1.5, discount_factor=1.0, interest_rate=0.0)
 
     result = model.simulate(
         params=params,
         initial_conditions={
-            "wealth": jnp.array([0, 2]),
+            "wealth": jnp.array([0.0, 2.0]),
             "age": jnp.array([50.0, 50.0]),
-            "regime": jnp.array([DiscreteRegimeId.working_life] * 2),
+            "regime_id": jnp.array([DiscreteRegimeId.working_life] * 2),
         },
         period_to_regime_to_V_arr=None,
     )
-    got = result.to_dataframe().query('regime == "working_life"')
+    got = result.to_dataframe().query('regime_name == "working_life"')
 
     # Expected: sorted by (subject_id, period)
     # Subject 0: wealth=low -> works, low consumption; wealth=high -> retires, high
@@ -265,12 +265,12 @@ def test_effect_of_discount_factor_on_last_period():
             initial_conditions={
                 "wealth": initial_wealth,
                 "age": jnp.array([18.0, 18.0, 18.0]),
-                "regime": jnp.array([RegimeId.working_life] * 3),
+                "regime_id": jnp.array([RegimeId.working_life] * 3),
             },
             period_to_regime_to_V_arr=None,
         )
         .to_dataframe()
-        .query('regime == "working_life"')
+        .query('regime_name == "working_life"')
     )
 
     df_high = (
@@ -279,12 +279,12 @@ def test_effect_of_discount_factor_on_last_period():
             initial_conditions={
                 "wealth": initial_wealth,
                 "age": jnp.array([18.0, 18.0, 18.0]),
-                "regime": jnp.array([RegimeId.working_life] * 3),
+                "regime_id": jnp.array([RegimeId.working_life] * 3),
             },
             period_to_regime_to_V_arr=None,
         )
         .to_dataframe()
-        .query('regime == "working_life"')
+        .query('regime_name == "working_life"')
     )
 
     # Higher beta (more patient) should lead to higher value in later periods
@@ -330,12 +330,12 @@ def test_effect_of_disutility_of_work():
             initial_conditions={
                 "wealth": initial_wealth,
                 "age": jnp.array([18.0, 18.0, 18.0]),
-                "regime": jnp.array([RegimeId.working_life] * 3),
+                "regime_id": jnp.array([RegimeId.working_life] * 3),
             },
             period_to_regime_to_V_arr=None,
         )
         .to_dataframe()
-        .query('regime == "working_life"')
+        .query('regime_name == "working_life"')
     )
 
     df_high = (
@@ -344,12 +344,12 @@ def test_effect_of_disutility_of_work():
             initial_conditions={
                 "wealth": initial_wealth,
                 "age": jnp.array([18.0, 18.0, 18.0]),
-                "regime": jnp.array([RegimeId.working_life] * 3),
+                "regime_id": jnp.array([RegimeId.working_life] * 3),
             },
             period_to_regime_to_V_arr=None,
         )
         .to_dataframe()
-        .query('regime == "working_life"')
+        .query('regime_name == "working_life"')
     )
 
     # Merge results for easy comparison
@@ -377,14 +377,14 @@ def test_to_dataframe_use_labels_parameter():
         initial_conditions={
             "wealth": jnp.array([20.0, 50.0]),
             "age": jnp.array([18.0, 18.0]),
-            "regime": jnp.array([RegimeId.working_life] * 2),
+            "regime_id": jnp.array([RegimeId.working_life] * 2),
         },
         period_to_regime_to_V_arr=None,
     )
 
     # use_labels=True (default): discrete columns are Categorical with string labels
     df_labels = result.to_dataframe()
-    for col in ["regime", "labor_supply"]:
+    for col in ["regime_name", "labor_supply"]:
         assert df_labels[col].dtype.name == "category", f"{col} should be categorical"
     assert set(df_labels["labor_supply"].cat.categories) == {"work", "retire"}
 
@@ -405,7 +405,7 @@ def regression_simulation_result():
         initial_conditions={
             "wealth": jnp.array([20.0, 50.0]),
             "age": jnp.array([18.0, 18.0]),
-            "regime": jnp.array([RegimeId.working_life] * 2),
+            "regime_id": jnp.array([RegimeId.working_life] * 2),
         },
         period_to_regime_to_V_arr=None,
     )
@@ -450,7 +450,7 @@ def test_additional_targets_all_with_stochastic_transitions():
             "health": jnp.array([Health.good, Health.bad]),
             "partner": jnp.array([PartnerStatus.single, PartnerStatus.partnered]),
             "age": jnp.array([40.0, 40.0]),
-            "regime": jnp.array([StochasticRegimeId.working_life] * 2),
+            "regime_id": jnp.array([StochasticRegimeId.working_life] * 2),
         },
         period_to_regime_to_V_arr=None,
     )
@@ -468,7 +468,7 @@ def test_additional_targets_all_with_stochastic_transitions():
 
 def test_retrieve_actions():
     got = _lookup_values_from_indices(
-        flat_indices=jnp.array([0, 3, 7]),
+        flat_indices=jnp.array([0, 3, 7], dtype=jnp.int32),
         grids=MappingProxyType(
             {"a": jnp.linspace(0, 1, 5), "b": jnp.linspace(10, 20, 6)}
         ),
@@ -488,7 +488,7 @@ def test_simulation_result_pickle_roundtrip(tmp_path: Path):
         initial_conditions={
             "wealth": jnp.array([20.0, 50.0]),
             "age": jnp.array([18.0, 18.0]),
-            "regime": jnp.array([RegimeId.working_life] * 2),
+            "regime_id": jnp.array([RegimeId.working_life] * 2),
         },
         period_to_regime_to_V_arr=None,
     )
diff --git a/tests/simulation/test_simulate_aot.py b/tests/simulation/test_simulate_aot.py
index 698256e4e..ee63aac17 100644
--- a/tests/simulation/test_simulate_aot.py
+++ b/tests/simulation/test_simulate_aot.py
@@ -21,6 +21,7 @@
 
 from lcm import Model
 from lcm.ages import AgeGrid
+from lcm.exceptions import ModelInitializationError
 from tests.test_models.deterministic.regression import (
     RegimeId,
     dead,
@@ -51,7 +52,7 @@ def _build_initial_conditions(*, n_subjects: int) -> dict[str, Array]:
     return {
         "wealth": wealths,
         "age": jnp.full((n_subjects,), 18.0),
-        "regime": jnp.array([RegimeId.working_life] * n_subjects),
+        "regime_id": jnp.array([RegimeId.working_life] * n_subjects),
     }
 
 
@@ -63,8 +64,8 @@ def test_n_subjects_validation_rejects_non_positive(invalid: int) -> None:
 
 
 def test_n_subjects_validation_rejects_non_int() -> None:
-    """`Model(n_subjects=1.5)` raises `TypeError`."""
-    with pytest.raises(TypeError, match="n_subjects"):
+    """`Model(n_subjects=1.5)` raises `ModelInitializationError`."""
+    with pytest.raises(ModelInitializationError, match="n_subjects"):
         _build_test_model(n_periods=3, n_subjects=1.5)  # ty: ignore[invalid-argument-type]
 
 
diff --git a/tests/solution/test_beta_delta.py b/tests/solution/test_beta_delta.py
index d36b3d898..bcaaafc0f 100644
--- a/tests/solution/test_beta_delta.py
+++ b/tests/solution/test_beta_delta.py
@@ -185,7 +185,7 @@ def test_beta_delta_consumption(label, beta, delta):
     initial_conditions = {
         "age": initial_age,
         "wealth": initial_wealth,
-        "regime": jnp.array([RegimeId.working]),
+        "regime_id": jnp.array([RegimeId.working]),
     }
 
     if label == "naive_phase_variant":
@@ -227,7 +227,7 @@ def test_beta_delta_consumption(label, beta, delta):
             period_to_regime_to_V_arr=None,
         )
 
-    df = result.to_dataframe().query('regime == "working"')
+    df = result.to_dataframe().query('regime_name == "working"')
     got_c0 = df.loc[df["age"] == 0, "consumption"].iloc[0]
     got_c1 = df.loc[df["age"] == 1, "consumption"].iloc[0]
 
diff --git a/tests/solution/test_solve_brute.py b/tests/solution/test_solve_brute.py
index 5051c751c..3151e9e7a 100644
--- a/tests/solution/test_solve_brute.py
+++ b/tests/solution/test_solve_brute.py
@@ -7,7 +7,7 @@
 
 from lcm.ages import AgeGrid
 from lcm.grids import Grid
-from lcm.interfaces import StateActionSpace
+from lcm.interfaces import InternalRegime, StateActionSpace
 from lcm.regime_building.max_Q_over_a import get_max_Q_over_a
 from lcm.regime_building.ndimage import map_coordinates
 from lcm.solution.solve_brute import solve
@@ -23,21 +23,30 @@ class SolveFunctionsMock:
     compute_intermediates: dict = dataclasses.field(default_factory=dict)
 
 
-@dataclasses.dataclass(frozen=True)
-class InternalRegimeMock:
+class InternalRegimeMock(InternalRegime):
     """Mock InternalRegime with only the attributes required by solve().
 
-    The solve() function only accesses:
-    - _base_state_action_space: StateActionSpace object
-    - state_action_space(): method returning the state-action space
-    - solve_functions.max_Q_over_a: dict mapping period to max_Q_over_a function
-    - active_periods: list of periods the regime is active
+    Inherits from `InternalRegime` so `isinstance(x, InternalRegime)` holds at
+    the beartype-checked perimeter of `solve()`, but bypasses the dataclass
+    `__init__` so tests can supply only the attributes `solve()` reads:
+    - `_base_state_action_space`: StateActionSpace object
+    - `state_action_space()`: method returning the state-action space
+    - `solve_functions.max_Q_over_a`: dict mapping period to max_Q_over_a function
+    - `active_periods`: list of periods the regime is active
     """
 
-    _base_state_action_space: StateActionSpace
-    solve_functions: SolveFunctionsMock
-    active_periods: list[int]
-    grids: MappingProxyType[StateOrActionName, Grid]
+    def __init__(
+        self,
+        *,
+        _base_state_action_space: StateActionSpace,
+        solve_functions: SolveFunctionsMock,
+        active_periods: list[int],
+        grids: MappingProxyType[StateOrActionName, Grid],
+    ) -> None:
+        object.__setattr__(self, "_base_state_action_space", _base_state_action_space)
+        object.__setattr__(self, "solve_functions", solve_functions)
+        object.__setattr__(self, "active_periods", active_periods)
+        object.__setattr__(self, "grids", grids)
 
     def state_action_space(self, regime_params):  # noqa: ARG002
         return self._base_state_action_space
@@ -134,7 +143,7 @@ def _Q_and_F(
     solution = solve(
         internal_params=MappingProxyType({"default": internal_params}),
         ages=AgeGrid(start=0, stop=2, step="Y"),
-        internal_regimes={"default": internal_regime},  # ty: ignore[invalid-argument-type]
+        internal_regimes=MappingProxyType({"default": internal_regime}),
         logger=get_logger(log_level="off"),
         enable_jit=False,
     )
@@ -195,7 +204,7 @@ def _Q_and_F(a, c, b, d, next_regime_to_V_arr, period, age):  # noqa: ARG001
     got = solve(
         internal_params=MappingProxyType({"default": MappingProxyType({})}),
         ages=AgeGrid(start=0, stop=2, step="Y"),
-        internal_regimes={"default": internal_regime},  # ty: ignore[invalid-argument-type]
+        internal_regimes=MappingProxyType({"default": internal_regime}),
         logger=get_logger(log_level="off"),
         enable_jit=False,
     )
diff --git a/tests/test_ages.py b/tests/test_ages.py
index 62e9b7cf1..d80d1cc3f 100644
--- a/tests/test_ages.py
+++ b/tests/test_ages.py
@@ -150,7 +150,7 @@ def test_model_with_quarterly_steps():
         initial_conditions={
             "wealth": jnp.array([50.0, 100.0, 150.0]),
             "age": jnp.array([18.0, 18.0, 18.0]),
-            "regime": jnp.array([RegimeId.working_life] * 3),
+            "regime_id": jnp.array([RegimeId.working_life] * 3),
         },
         period_to_regime_to_V_arr=None,
     )
@@ -161,7 +161,7 @@ def test_model_with_quarterly_steps():
     assert set(df["age"].unique()) == {18.0, 18.25, 18.5, 18.75, 19.0}
 
     # Check working/retired regimes only have ages < 19
-    non_dead_df = df.query('regime != "dead"')
+    non_dead_df = df.query('regime_name != "dead"')
     assert all(non_dead_df["age"] < 19)
 
 
@@ -293,7 +293,7 @@ def test_model_with_integer_ages():
         initial_conditions={
             "wealth": jnp.array([50.0, 100.0, 150.0]),
             "age": jnp.array([40, 40, 40]),
-            "regime": jnp.array([RegimeId.working_life] * 3),
+            "regime_id": jnp.array([RegimeId.working_life] * 3),
         },
         period_to_regime_to_V_arr=None,
     )
diff --git a/tests/test_argmax.py b/tests/test_argmax.py
index 7ef50c6e1..5fb14d9e7 100644
--- a/tests/test_argmax.py
+++ b/tests/test_argmax.py
@@ -13,7 +13,7 @@
 
 
 def test_argmax_1d_with_mask():
-    a = jnp.arange(10)
+    a = jnp.arange(10, dtype=jnp.int32)
     mask = jnp.array([1, 0, 0, 1, 1, 0, 0, 0, 0, 0], dtype=bool)
     _argmax, _max = jitted_argmax(a, where=mask, initial=-1)
     assert _argmax == 4
@@ -21,7 +21,7 @@ def test_argmax_1d_with_mask():
 
 
 def test_argmax_2d_with_mask():
-    a = jnp.arange(10).reshape(2, 5)
+    a = jnp.arange(10, dtype=jnp.int32).reshape(2, 5)
     mask = jnp.array([1, 0, 0, 1, 1, 0, 0, 0, 0, 0], dtype=bool).reshape(a.shape)
 
     _argmax, _max = jitted_argmax(a, axis=None, where=mask, initial=-1)
@@ -38,14 +38,14 @@ def test_argmax_2d_with_mask():
 
 
 def test_argmax_1d_no_mask():
-    a = jnp.arange(10)
+    a = jnp.arange(10, dtype=jnp.int32)
     _argmax, _max = jitted_argmax(a)
     assert _argmax == 9
     assert _max == 9
 
 
 def test_argmax_2d_no_mask():
-    a = jnp.arange(10).reshape(2, 5)
+    a = jnp.arange(10, dtype=jnp.int32).reshape(2, 5)
 
     _argmax, _max = jitted_argmax(a, axis=None)
     assert _argmax == 9
@@ -65,7 +65,7 @@ def test_argmax_2d_no_mask():
 
 
 def test_argmax_3d_no_mask():
-    a = jnp.arange(24).reshape(2, 3, 4)
+    a = jnp.arange(24, dtype=jnp.int32).reshape(2, 3, 4)
 
     _argmax, _max = jitted_argmax(a, axis=None)
     assert _argmax == 23
@@ -107,37 +107,37 @@ def test_argmax_with_ties():
 
 
 def test_move_axes_to_back_1d():
-    a = jnp.arange(4)
+    a = jnp.arange(4, dtype=jnp.int32)
     got = _move_axes_to_back(a, axes=(0,))
     assert_array_equal(got, a)
 
 
 def test_move_axes_to_back_2d():
-    a = jnp.arange(4).reshape(2, 2)
+    a = jnp.arange(4, dtype=jnp.int32).reshape(2, 2)
     got = _move_axes_to_back(a, axes=(0,))
     assert_array_equal(got, a.transpose(1, 0))
 
 
 def test_move_axes_to_back_3d():
     # 2 dimensions in back
-    a = jnp.arange(8).reshape(2, 2, 2)
+    a = jnp.arange(8, dtype=jnp.int32).reshape(2, 2, 2)
     got = _move_axes_to_back(a, axes=(0, 1))
     assert_array_equal(got, a.transpose(2, 0, 1))
 
     # 2 dimensions in front
-    a = jnp.arange(8).reshape(2, 2, 2)
+    a = jnp.arange(8, dtype=jnp.int32).reshape(2, 2, 2)
     got = _move_axes_to_back(a, axes=(1,))
     assert_array_equal(got, a.transpose(0, 2, 1))
 
 
 def test_flatten_last_n_axes_1d():
-    a = jnp.arange(4)
+    a = jnp.arange(4, dtype=jnp.int32)
     got = _flatten_last_n_axes(a, n=1)
     assert_array_equal(got, a)
 
 
 def test_flatten_last_n_axes_2d():
-    a = jnp.arange(4).reshape(2, 2)
+    a = jnp.arange(4, dtype=jnp.int32).reshape(2, 2)
 
     got = _flatten_last_n_axes(a, n=1)
     assert_array_equal(got, a)
@@ -147,7 +147,7 @@ def test_flatten_last_n_axes_2d():
 
 
 def test_flatten_last_n_axes_3d():
-    a = jnp.arange(8).reshape(2, 2, 2)
+    a = jnp.arange(8, dtype=jnp.int32).reshape(2, 2, 2)
 
     got = _flatten_last_n_axes(a, n=1)
     assert_array_equal(got, a)
diff --git a/tests/test_beartype_claw.py b/tests/test_beartype_claw.py
new file mode 100644
index 000000000..6aae29229
--- /dev/null
+++ b/tests/test_beartype_claw.py
@@ -0,0 +1,139 @@
+"""The beartype claw is live on the entire `lcm` package.
+
+The claw uses `INTERNAL_CONF`, so type violations in internal helpers
+surface as beartype's own `BeartypeCallHintViolation`. User-facing
+constructors (`Model`, `Regime`, `MarkovTransition`, every grid and shock,
+`@categorical`, `as_leaf`) carry their own explicit `@beartype(conf=...)`
+decorators that map violations to the relevant project exception
+(`ModelInitializationError`, `RegimeInitializationError`,
+`GridInitializationError`, `InvalidParamsError`); those decorators stack
+on top of the claw and win at the user boundary.
+
+Each `test_claw_checks_*` test calls an internal function with one argument
+of the wrong type, chosen so the call would return cleanly if the function
+were *not* instrumented — the violation is what proves the claw is
+installed. Each `test_*_with_bad_arg_raises_project_exception` test
+confirms that an ill-typed argument to a public constructor surfaces as
+the project exception, not as `BeartypeCallHintViolation`.
+"""
+
+from types import MappingProxyType
+
+import jax.numpy as jnp
+import numpy as np
+import pytest
+from beartype.roar import BeartypeCallHintViolation
+
+from lcm import AgeGrid, LinSpacedGrid, Model, Regime
+from lcm.exceptions import (
+    GridInitializationError,
+    ModelInitializationError,
+    RegimeInitializationError,
+)
+from lcm.interfaces import _build_regime_sharding
+from lcm.model import _validate_log_args
+from lcm.regime import _default_H
+from lcm.simulation.simulate import _compute_starting_periods
+from lcm.solution.solve_brute import _log_per_period_stats
+from lcm.state_action_space import _validate_all_states_present
+from lcm.utils.error_handling import validate_regime_transition_probs
+
+
+def test_claw_checks_lcm_simulation() -> None:
+    """Type-violating arguments to internal `lcm.simulation` helpers raise."""
+    with pytest.raises(BeartypeCallHintViolation):
+        _compute_starting_periods(
+            initial_ages=np.array([25.0]),  # ty: ignore[invalid-argument-type]
+            ages=AgeGrid(start=25, stop=75, step="Y"),
+        )
+
+
+def test_claw_checks_lcm_solution() -> None:
+    """Type-violating arguments to internal `lcm.solution` helpers raise."""
+    with pytest.raises(BeartypeCallHintViolation):
+        _log_per_period_stats(
+            logger="not a logger",  # ty: ignore[invalid-argument-type]
+            diagnostic_rows=[],
+            mins=jnp.array([]),
+            maxs=jnp.array([]),
+            means=jnp.array([]),
+        )
+
+
+def test_claw_checks_lcm_utils_error_handling() -> None:
+    """Type-violating arguments to `lcm.utils.error_handling` helpers raise."""
+    with pytest.raises(BeartypeCallHintViolation):
+        validate_regime_transition_probs(
+            regime_transition_probs={"working": jnp.array([1.0])},  # ty: ignore[invalid-argument-type]
+            active_regimes_next_period=("working",),
+            regime_name="working",
+            age=50.0,
+            next_age=51.0,
+        )
+
+
+def test_claw_checks_lcm_state_action_space() -> None:
+    """Type-violating arguments to `lcm.state_action_space` helpers raise."""
+    with pytest.raises(BeartypeCallHintViolation):
+        _validate_all_states_present(
+            provided_states="",  # ty: ignore[invalid-argument-type]
+            required_state_names=set(),
+        )
+
+
+def test_claw_checks_lcm_interfaces() -> None:
+    """Type-violating arguments to `lcm.interfaces` helpers raise."""
+    with pytest.raises(BeartypeCallHintViolation):
+        _build_regime_sharding(
+            grids=MappingProxyType({}),
+            n_devices="not an int",  # ty: ignore[invalid-argument-type]
+        )
+
+
+def test_claw_checks_lcm_regime() -> None:
+    """Type-violating arguments to `lcm.regime` helpers raise."""
+    with pytest.raises(BeartypeCallHintViolation):
+        _default_H(
+            utility=np.array([1.0]),  # ty: ignore[invalid-argument-type]
+            E_next_V=jnp.array([1.0]),
+            discount_factor=jnp.array([0.95]),
+        )
+
+
+def test_regime_with_bad_arg_raises_project_exception() -> None:
+    """A bad `Regime` argument surfaces as `RegimeInitializationError`."""
+    with pytest.raises(RegimeInitializationError):
+        Regime(
+            transition=None,
+            states={"wealth": LinSpacedGrid(start=1.0, stop=10.0, n_points=3)},
+            functions="not a mapping",  # ty: ignore[invalid-argument-type]
+        )
+
+
+def test_claw_checks_lcm_model() -> None:
+    """Type-violating arguments to internal `lcm.model` helpers raise."""
+    with pytest.raises(BeartypeCallHintViolation):
+        _validate_log_args(
+            log_level="progress",
+            log_path=123,  # ty: ignore[invalid-argument-type]
+        )
+
+
+def test_model_with_bad_arg_raises_project_exception() -> None:
+    """A bad `Model` argument surfaces as `ModelInitializationError`."""
+    with pytest.raises(ModelInitializationError):
+        Model(
+            ages=AgeGrid(start=25, stop=75, step="Y"),
+            regimes="not a mapping",  # ty: ignore[invalid-argument-type]
+            regime_id_class=int,
+        )
+
+
+def test_linspaced_grid_with_bad_arg_raises_project_exception() -> None:
+    """A bad `LinSpacedGrid` argument surfaces as `GridInitializationError`."""
+    with pytest.raises(GridInitializationError):
+        LinSpacedGrid(
+            start="not a number",  # ty: ignore[invalid-argument-type]
+            stop=10.0,
+            n_points=3,
+        )
diff --git a/tests/test_chained_state_transitions.py b/tests/test_chained_state_transitions.py
index 8bc4dc645..2f22bdac6 100644
--- a/tests/test_chained_state_transitions.py
+++ b/tests/test_chained_state_transitions.py
@@ -107,7 +107,7 @@ def test_simulate_with_chained_transitions_yields_expected_next_wealth() -> None
         "age": jnp.array([0.0, 0.0]),
         "aime": jnp.array([0.0, 1.0]),
         "wealth": jnp.array([2.0, 3.0]),
-        "regime": jnp.array([_RegimeId.active, _RegimeId.active]),
+        "regime_id": jnp.array([_RegimeId.active, _RegimeId.active]),
     }
 
     df = (
@@ -117,7 +117,7 @@ def test_simulate_with_chained_transitions_yields_expected_next_wealth() -> None
             period_to_regime_to_V_arr=None,
         )
         .to_dataframe()
-        .query('regime == "active"')
+        .query('regime_name == "active"')
         .sort_values(["subject_id", "period"])
         .reset_index(drop=True)
     )
diff --git a/tests/test_dispatchers.py b/tests/test_dispatchers.py
index 08b64b266..947acd8b6 100644
--- a/tests/test_dispatchers.py
+++ b/tests/test_dispatchers.py
@@ -2,6 +2,7 @@
 
 import jax.numpy as jnp
 import pytest
+from beartype.roar import BeartypeCallHintViolation
 from numpy.testing import assert_array_almost_equal as aaae
 
 from lcm.exceptions import FunctionDispatchError
@@ -216,13 +217,13 @@ def test_spacemap_all_arguments_mapped(
     [
         (
             "Same argument provided more than once in actions or states variables",
-            ["a", "b"],
-            ["a", "c", "d"],
+            ("a", "b"),
+            ("a", "c", "d"),
         ),
         (
             "Same argument provided more than once in actions or states variables",
-            ["a", "a", "b"],
-            ["c", "d"],
+            ("a", "a", "b"),
+            ("c", "d"),
         ),
     ],
 )
@@ -246,15 +247,18 @@ def func(a, b, c):
 
 
 def test_vmap_1d_error():
+    def func(a):
+        return a
+
     with pytest.raises(ValueError, match=r"Same argument provided more than once."):
-        vmap_1d(func=None, variables=["a", "a"])  # ty: ignore[invalid-argument-type]
+        vmap_1d(func=func, variables=("a", "a"))
 
 
 def test_vmap_1d_callable_with_only_args():
     def func(a):
         return a
 
-    vmapped = vmap_1d(func=func, variables=["a"], callable_with="only_args")  # ty: ignore[invalid-argument-type]
+    vmapped = vmap_1d(func=func, variables=("a",), callable_with="only_args")
     a = jnp.array([1, 2])
     # check that the function works with positional arguments
     aaae(vmapped(a), a)
@@ -270,7 +274,7 @@ def test_vmap_1d_callable_with_only_kwargs():
     def func(a):
         return a
 
-    vmapped = vmap_1d(func=func, variables=["a"], callable_with="only_kwargs")  # ty: ignore[invalid-argument-type]
+    vmapped = vmap_1d(func=func, variables=("a",), callable_with="only_kwargs")
     a = jnp.array([1, 2])
     # check that the function works with keyword arguments
     aaae(vmapped(a=a), a)
@@ -283,11 +287,10 @@ def func(a):
 
 
 def test_vmap_1d_callable_with_invalid():
+    """`callable_with` rejects anything outside the documented literal options."""
+
     def func(a):
         return a
 
-    with pytest.raises(
-        ValueError,
-        match=r"Invalid callable_with option: invalid. Possible options are",
-    ):
-        vmap_1d(func=func, variables=["a"], callable_with="invalid")  # ty: ignore[invalid-argument-type]
+    with pytest.raises(BeartypeCallHintViolation):
+        vmap_1d(func=func, variables=("a",), callable_with="invalid")  # ty: ignore[invalid-argument-type]
diff --git a/tests/test_distributed.py b/tests/test_distributed.py
index 891a89f24..460fffeae 100644
--- a/tests/test_distributed.py
+++ b/tests/test_distributed.py
@@ -166,7 +166,7 @@ def test_simulation_running_on_multiple_cpus(correct_distributed_model):
             "wealth": jnp.full(36, 100.0),
             "type1": jnp.full(36, 1),
             "type2": jnp.full(36, 1),
-            "regime": jnp.zeros(36, dtype=jnp.int32),
+            "regime_id": jnp.zeros(36, dtype=jnp.int32),
         },
         period_to_regime_to_V_arr=None,
         seed=12345,
@@ -201,7 +201,7 @@ def test_simulation_error_if_not_multiple(correct_distributed_model):
                 "wealth": jnp.full(5, 100.0),
                 "type1": jnp.full(5, 1),
                 "type2": jnp.full(5, 1),
-                "regime": jnp.zeros(5, dtype=jnp.int32),
+                "regime_id": jnp.zeros(5, dtype=jnp.int32),
             },
             period_to_regime_to_V_arr=None,
             seed=12345,
diff --git a/tests/test_dtypes.py b/tests/test_dtypes.py
index d0083dab0..3fd74a872 100644
--- a/tests/test_dtypes.py
+++ b/tests/test_dtypes.py
@@ -52,8 +52,8 @@ def test_safe_to_int_dtype_raises_on_array_overflow():
     # under `jax_enable_x64=False` and trips JAX's own overflow guard before
     # `safe_to_int_dtype` ever sees the value.
     arr = np.asarray([1, 2, 2**32], dtype=np.int64)
-    with pytest.raises(ValueError, match="regime"):
-        safe_to_int_dtype(arr, name="regime")
+    with pytest.raises(ValueError, match="regime_id"):
+        safe_to_int_dtype(arr, name="regime_id")
 
 
 def test_safe_to_int_dtype_raises_on_underflow():
diff --git a/tests/test_economic_validation.py b/tests/test_economic_validation.py
index a3c2a1d36..85ab313b1 100644
--- a/tests/test_economic_validation.py
+++ b/tests/test_economic_validation.py
@@ -29,7 +29,7 @@ def _simulate(shock_type, *, sigma, rho=0.0, mu=0.0):
             "wealth": jnp.full(_N_SUBJECTS, 5.0),
             "income": jnp.full(_N_SUBJECTS, unconditional_mean),
             "age": jnp.full(_N_SUBJECTS, 20.0),
-            "regime": jnp.array([RegimeId.alive] * _N_SUBJECTS),
+            "regime_id": jnp.array([RegimeId.alive] * _N_SUBJECTS),
         },
         period_to_regime_to_V_arr=None,
         seed=_SEED,
@@ -38,7 +38,7 @@ def _simulate(shock_type, *, sigma, rho=0.0, mu=0.0):
 
 
 def _mean_wealth_in_final_alive_period(df):
-    rows = df[(df["period"] == _N_PERIODS - 2) & (df["regime"] == "alive")]
+    rows = df[(df["period"] == _N_PERIODS - 2) & (df["regime_name"] == "alive")]
     assert len(rows) > 0, "No rows in final alive period"
     return rows["wealth"].mean()
 
@@ -48,7 +48,7 @@ def test_deterministic_when_sigma_zero(shock_type):
     rho = 0.5 if shock_type in ("rouwenhorst", "tauchen") else 0.0
     df = _simulate(shock_type, sigma=_SIGMA_ZERO, rho=rho)
 
-    alive_df = df[df["regime"] == "alive"]
+    alive_df = df[df["regime_name"] == "alive"]
     for period in alive_df["period"].unique():
         period_data = alive_df[alive_df["period"] == period]
         assert period_data["wealth"].std() == pytest.approx(0, abs=_DETERMINISTIC_ATOL)
diff --git a/tests/test_error_handling_invalid_vf.py b/tests/test_error_handling_invalid_vf.py
index 9b9fe1d39..e86ba7d2a 100644
--- a/tests/test_error_handling_invalid_vf.py
+++ b/tests/test_error_handling_invalid_vf.py
@@ -183,7 +183,7 @@ def test_simulate_model_with_nan_value_function_array_raises_error(
         "wealth": jnp.array([0.9, 1.0]),
         "health": jnp.array([1.0, 1.0]),
         "age": jnp.array([0.0, 0.0]),
-        "regime": jnp.array([RegimeId.non_terminal] * 2),
+        "regime_id": jnp.array([RegimeId.non_terminal] * 2),
     }
 
     with pytest.raises(InvalidValueFunctionError):
@@ -202,7 +202,7 @@ def test_simulate_model_with_inf_value_function_array_does_not_raise_error(
         "wealth": jnp.array([1.5, 2.0]),
         "health": jnp.array([1.0, 1.0]),
         "age": jnp.array([0.0, 0.0]),
-        "regime": jnp.array([RegimeId.non_terminal] * 2),
+        "regime_id": jnp.array([RegimeId.non_terminal] * 2),
     }
 
     # This should not raise an error. Subject 1 (wealth=2.0, health=1.0) triggers the
diff --git a/tests/test_fgp_discretization.py b/tests/test_fgp_discretization.py
index 103d7fb58..4b2bc3eaa 100644
--- a/tests/test_fgp_discretization.py
+++ b/tests/test_fgp_discretization.py
@@ -287,7 +287,12 @@ def test_mixture_cdf_spot_check():
     """Spot-check mixture CDF against manual scipy-style computation."""
     x = jnp.array([0.0, 0.1, -0.1])
     got = _mixture_cdf(
-        x=x, p1=FGP_P1, mu1=FGP_MU1, sigma1=FGP_SIGMA1, mu2=FGP_MU2, sigma2=FGP_SIGMA2
+        x=x,
+        p1=jnp.asarray(FGP_P1),
+        mu1=jnp.asarray(FGP_MU1),
+        sigma1=jnp.asarray(FGP_SIGMA1),
+        mu2=jnp.asarray(FGP_MU2),
+        sigma2=jnp.asarray(FGP_SIGMA2),
     )
 
     expected = FGP_P1 * jax_cdf((x - FGP_MU1) / FGP_SIGMA1) + (1 - FGP_P1) * jax_cdf(
diff --git a/tests/test_fgp_model.py b/tests/test_fgp_model.py
index 0d0edc3ec..301107819 100644
--- a/tests/test_fgp_model.py
+++ b/tests/test_fgp_model.py
@@ -45,7 +45,7 @@ def _simulate(shock_type):
             "wealth": jnp.full(_N_SUBJECTS, 5.0),
             "income": jnp.zeros(_N_SUBJECTS),
             "age": jnp.full(_N_SUBJECTS, 20.0),
-            "regime": jnp.array([RegimeId.alive] * _N_SUBJECTS),
+            "regime_id": jnp.array([RegimeId.alive] * _N_SUBJECTS),
         },
         period_to_regime_to_V_arr=None,
         seed=_SEED,
@@ -92,7 +92,7 @@ def test_model_simulates(shock_type):
 def test_simulated_income_moments(shock_type):
     """Simulated income moments are in the right ballpark."""
     df = _simulate(shock_type)
-    alive_df = df[df["regime"] == "alive"]
+    alive_df = df[df["regime_name"] == "alive"]
 
     # Income on the grid should have mean near 0 and std near sigma_y
     income_vals = alive_df["income"].to_numpy()
@@ -109,8 +109,8 @@ def test_rouwenhorst_income_moments_closer_to_theory():
     df_r = _simulate("rouwenhorst")
     df_t = _simulate("tauchen")
 
-    r_var = df_r[df_r["regime"] == "alive"]["income"].var()
-    t_var = df_t[df_t["regime"] == "alive"]["income"].var()
+    r_var = df_r[df_r["regime_name"] == "alive"]["income"].var()
+    t_var = df_t[df_t["regime_name"] == "alive"]["income"].var()
 
     # Both should be in the right ballpark; Rouwenhorst should be at least as close
     r_err = abs(r_var - expected_var)
diff --git a/tests/test_float_dtype_invariants.py b/tests/test_float_dtype_invariants.py
index 7e4f14fca..4b031e50c 100644
--- a/tests/test_float_dtype_invariants.py
+++ b/tests/test_float_dtype_invariants.py
@@ -1,7 +1,7 @@
 """Float dtypes follow `canonical_float_dtype()` across pylcm boundaries."""
 
 from collections.abc import Callable
-from types import MappingProxyType
+from typing import cast
 
 import jax.numpy as jnp
 import numpy as np
@@ -12,13 +12,24 @@
 from lcm.params import MappingLeaf
 from lcm.params.processing import process_params
 from lcm.params.sequence_leaf import SequenceLeaf
-from lcm.simulation.initial_conditions import build_initial_states
+from lcm.simulation.initial_conditions import (
+    build_initial_states,
+    canonicalize_initial_conditions,
+)
+from lcm.typing import ParamsTemplate
+from lcm.utils.containers import ensure_containers_are_immutable
 from tests.test_models.deterministic.regression import (
     RegimeId,
     get_model,
     get_params,
 )
 
+
+def _as_template(plain: dict) -> ParamsTemplate:
+    """Deep-freeze a plain nested dict into a `ParamsTemplate` for tests."""
+    return cast("ParamsTemplate", ensure_containers_are_immutable(plain))
+
+
 # These tests deliberately pass `float64` inputs to verify the cast at
 # the barrier. Re-allow the JAX truncation warning that the
 # project-wide filter (see `pyproject.toml`) promotes to an error —
@@ -28,18 +39,25 @@
 )
 
 
-def test_build_initial_states_casts_user_float64_to_canonical(x64_disabled: None):
-    """A float64 continuous initial state lands at `canonical_float_dtype()`."""
+def test_canonicalize_initial_conditions_casts_user_float64_to_canonical(
+    x64_disabled: None,
+):
+    """A float64 continuous initial state lands at `canonical_float_dtype()`.
+
+    `canonicalize_initial_conditions` is pylcm's simulation input boundary:
+    user arrays of any dtype are cast to their canonical pylcm dtype before
+    validation and the simulate stack see them.
+    """
     model = get_model(n_periods=3)
-    initial_states = {
+    initial_conditions = {
         "wealth": np.asarray([20.0, 50.0], dtype=np.float64),
         "age": np.asarray([18.0, 18.0], dtype=np.float64),
     }
-    states_per_regime = build_initial_states(
-        initial_states=initial_states,  # ty: ignore[invalid-argument-type]
+    canonical = canonicalize_initial_conditions(
+        initial_conditions=initial_conditions,
         internal_regimes=model.internal_regimes,
     )
-    assert states_per_regime["working_life"]["wealth"].dtype == canonical_float_dtype()
+    assert canonical["wealth"].dtype == canonical_float_dtype()
 
 
 def test_build_initial_states_casts_user_int_to_canonical(x64_disabled: None):
@@ -94,48 +112,48 @@ def test_process_params_casts_float64_array_to_canonical_under_no_x64(
     silently truncates to `float32` under no-x64 at construction time, so a
     JAX-built input would never reach the helper as `float64`.
     """
-    template = MappingProxyType({"regime_a": MappingProxyType({"schedule": "Array"})})
+    template = _as_template({"regime_a": {"fun": {"schedule": "Array"}}})
     user_params = {
-        "regime_a": {"schedule": np.asarray([0.1, 0.2, 0.3], dtype=np.float64)}
+        "regime_a": {"fun": {"schedule": np.asarray([0.1, 0.2, 0.3], dtype=np.float64)}}
     }
 
     out = process_params(
-        params=user_params,  # ty: ignore[invalid-argument-type]
-        params_template=template,  # ty: ignore[invalid-argument-type]
+        params=user_params,
+        params_template=template,
     )
 
-    schedule = out["regime_a"]["schedule"]
-    assert schedule.dtype == jnp.float32
+    schedule = out["regime_a"]["fun__schedule"]
+    assert schedule.dtype == jnp.float32  # ty: ignore[unresolved-attribute]
 
 
 def test_process_params_casts_python_float_to_canonical(x64_disabled: None):
     """A Python `float` param leaf is cast to `canonical_float_dtype()`."""
-    template = MappingProxyType(
-        {"regime_a": MappingProxyType({"discount_factor": "float"})}
-    )
-    user_params = {"regime_a": {"discount_factor": 0.95}}
+    template = _as_template({"regime_a": {"fun": {"discount_factor": "float"}}})
+    user_params = {"regime_a": {"fun": {"discount_factor": 0.95}}}
 
     out = process_params(
         params=user_params,
-        params_template=template,  # ty: ignore[invalid-argument-type]
+        params_template=template,
     )
 
-    discount_factor = out["regime_a"]["discount_factor"]
-    np.testing.assert_allclose(float(discount_factor), 0.95, rtol=1e-6)
-    assert discount_factor.dtype == canonical_float_dtype()
+    discount_factor = out["regime_a"]["fun__discount_factor"]
+    np.testing.assert_allclose(float(discount_factor), 0.95, rtol=1e-6)  # ty: ignore[invalid-argument-type]
+    assert discount_factor.dtype == canonical_float_dtype()  # ty: ignore[unresolved-attribute]
 
 
 def test_process_params_float_array_overflow_raises_with_qualified_name(
     x64_disabled: None,
 ):
     """An out-of-float32 float64 array raises naming the qualified leaf."""
-    template = MappingProxyType({"regime_a": MappingProxyType({"schedule": "Array"})})
-    user_params = {"regime_a": {"schedule": np.asarray([0.0, 1e40], dtype=np.float64)}}
+    template = _as_template({"regime_a": {"fun": {"schedule": "Array"}}})
+    user_params = {
+        "regime_a": {"fun": {"schedule": np.asarray([0.0, 1e40], dtype=np.float64)}}
+    }
 
     with pytest.raises(OverflowError, match="schedule"):
         process_params(
-            params=user_params,  # ty: ignore[invalid-argument-type]
-            params_template=template,  # ty: ignore[invalid-argument-type]
+            params=user_params,
+            params_template=template,
         )
 
 
@@ -152,7 +170,7 @@ def test_simulate_state_pool_dtype_stable_across_periods(x64_disabled: None):
     initial = {
         "wealth": jnp.asarray([20.0, 50.0, 80.0]),
         "age": jnp.asarray([18.0, 18.0, 18.0]),
-        "regime": jnp.asarray([RegimeId.working_life] * 3),
+        "regime_id": jnp.asarray([RegimeId.working_life] * 3),
     }
 
     result = model.simulate(
@@ -234,27 +252,27 @@ def test_process_params_casts_float_array_inside_mapping_leaf_to_canonical(
     key: str, x64_disabled: None
 ):
     """`MappingLeaf` float arrays land at `canonical_float_dtype()`."""
-    template = MappingProxyType(
-        {"regime_a": MappingProxyType({"sched": "MappingLeaf"})}
-    )
+    template = _as_template({"regime_a": {"fun": {"sched": "MappingLeaf"}}})
     user_params = {
         "regime_a": {
-            "sched": MappingLeaf(
-                {
-                    "low": np.asarray([0.1, 0.2], dtype=np.float64),
-                    "high": np.asarray([0.5, 0.7], dtype=np.float64),
-                }
-            )
+            "fun": {
+                "sched": MappingLeaf(
+                    {
+                        "low": np.asarray([0.1, 0.2], dtype=np.float64),
+                        "high": np.asarray([0.5, 0.7], dtype=np.float64),
+                    }
+                )
+            }
         }
     }
 
     out = process_params(
         params=user_params,
-        params_template=template,  # ty: ignore[invalid-argument-type]
+        params_template=template,
     )
 
     assert (
-        out["regime_a"]["sched"].data[key].dtype  # ty: ignore[unresolved-attribute]
+        out["regime_a"]["fun__sched"].data[key].dtype  # ty: ignore[unresolved-attribute, invalid-argument-type]
         == jnp.float32
     )
 
@@ -264,26 +282,26 @@ def test_process_params_casts_float_array_inside_sequence_leaf_to_canonical(
     index: int, x64_disabled: None
 ):
     """`SequenceLeaf` float arrays land at `canonical_float_dtype()`."""
-    template = MappingProxyType(
-        {"regime_a": MappingProxyType({"sched": "SequenceLeaf"})}
-    )
+    template = _as_template({"regime_a": {"fun": {"sched": "SequenceLeaf"}}})
     user_params = {
         "regime_a": {
-            "sched": SequenceLeaf(
-                [
-                    np.asarray([0.1, 0.2], dtype=np.float64),
-                    np.asarray([0.5, 0.7], dtype=np.float64),
-                ]
-            )
+            "fun": {
+                "sched": SequenceLeaf(
+                    [
+                        np.asarray([0.1, 0.2], dtype=np.float64),
+                        np.asarray([0.5, 0.7], dtype=np.float64),
+                    ]
+                )
+            }
         }
     }
 
     out = process_params(
         params=user_params,
-        params_template=template,  # ty: ignore[invalid-argument-type]
+        params_template=template,
     )
 
     assert (
-        out["regime_a"]["sched"].data[index].dtype  # ty: ignore[unresolved-attribute]
+        out["regime_a"]["fun__sched"].data[index].dtype  # ty: ignore[unresolved-attribute, invalid-argument-type]
         == jnp.float32
     )
diff --git a/tests/test_function_representation.py b/tests/test_function_representation.py
index 4c6827a89..9d00c791f 100644
--- a/tests/test_function_representation.py
+++ b/tests/test_function_representation.py
@@ -58,7 +58,7 @@ def test_function_evaluator_with_one_continuous_variable():
     func = partial(evaluator, next_V_arr=next_V_arr)
 
     # test the evaluator
-    got = func(next_wealth=0.5)
+    got = func(next_wealth=jnp.asarray(0.5))
     expected = 0.5 * jnp.pi + 2
     assert jnp.allclose(got, expected)
 
@@ -146,8 +146,8 @@ def test_function_evaluator(binary_discrete_grid):
     out = evaluator(
         next_retired=1,
         next_insured=0,
-        next_wealth=600,
-        next_human_capital=1.5,
+        next_wealth=jnp.asarray(600.0),
+        next_human_capital=jnp.asarray(1.5),
         next_V_arr=next_V_arr,
     )
 
@@ -169,7 +169,7 @@ def test_get_coordinate_finder():
         grid=LinSpacedGrid(start=0, stop=10, n_points=21),
     )
     find_coordinate = partial(find_coordinate)
-    calculated = find_coordinate(wealth=5.75)
+    calculated = find_coordinate(wealth=jnp.asarray(5.75))
     assert calculated == 11.5
 
 
@@ -224,7 +224,7 @@ def test_get_function_evaluator_illustrative():
     # partial the function values into the evaluator
     f = partial(evaluator, values_name=values)
 
-    got = f(prefix_a=0.25)
+    got = f(prefix_a=jnp.asarray(0.25))
     expected = jnp.pi * 0.25 + 2
 
     assert jnp.allclose(got, expected)
@@ -245,10 +245,10 @@ def test_get_coordinate_finder_illustrative():
         in_name="a",
         grid=LinSpacedGrid(start=0, stop=1, n_points=3),
     )
-    assert find_coordinate(a=0) == 0
-    assert find_coordinate(a=0.5) == 1
-    assert find_coordinate(a=1) == 2
-    assert find_coordinate(a=0.25) == 0.5
+    assert find_coordinate(a=jnp.asarray(0.0)) == 0
+    assert find_coordinate(a=jnp.asarray(0.5)) == 1
+    assert find_coordinate(a=jnp.asarray(1.0)) == 2
+    assert find_coordinate(a=jnp.asarray(0.25)) == 0.5
 
 
 @pytest.mark.illustrative
@@ -349,9 +349,9 @@ def test_function_evaluator_performs_linear_extrapolation():
     func = partial(evaluator, next_V_arr=next_V_arr)
 
     # test the evaluator on values outside the grid
-    wealth_outside_of_grid = [-0.5, 3.5, 10]
+    wealth_outside_of_grid = [-0.5, 3.5, 10.0]
     # We expect linear extrapolation
     expected = jnp.pi * jnp.array(wealth_outside_of_grid) + 2
 
-    got = jnp.array([func(next_wealth=w) for w in wealth_outside_of_grid])
+    got = jnp.array([func(next_wealth=jnp.asarray(w)) for w in wealth_outside_of_grid])
     assert jnp.allclose(got, expected)
diff --git a/tests/test_grids.py b/tests/test_grids.py
index ec652503c..2db7c6e61 100644
--- a/tests/test_grids.py
+++ b/tests/test_grids.py
@@ -252,20 +252,20 @@ class Cat:
 
 
 def test_lin_spaced_grid_rejects_non_numeric_start():
-    """Non-numeric `start` is rejected at the boundary cast."""
-    with pytest.raises((TypeError, ValueError)):
+    """Non-numeric `start` raises `GridInitializationError`."""
+    with pytest.raises(GridInitializationError, match="start"):
         LinSpacedGrid(start="a", stop=1, n_points=10)  # ty: ignore[invalid-argument-type]
 
 
 def test_lin_spaced_grid_rejects_non_numeric_stop():
-    """Non-numeric `stop` is rejected at the boundary cast."""
-    with pytest.raises((TypeError, ValueError)):
+    """Non-numeric `stop` raises `GridInitializationError`."""
+    with pytest.raises(GridInitializationError, match="stop"):
         LinSpacedGrid(start=1, stop="a", n_points=10)  # ty: ignore[invalid-argument-type]
 
 
 def test_lin_spaced_grid_rejects_non_numeric_n_points():
-    """Non-numeric `n_points` is rejected at the boundary cast."""
-    with pytest.raises((TypeError, ValueError)):
+    """Non-numeric `n_points` raises `GridInitializationError`."""
+    with pytest.raises(GridInitializationError, match="n_points"):
         LinSpacedGrid(start=1, stop=2, n_points="a")  # ty: ignore[invalid-argument-type]
 
 
@@ -363,7 +363,7 @@ def test_irreg_spaced_grid_invalid_too_few_points():
 
 
 def test_irreg_spaced_grid_invalid_non_numeric():
-    with pytest.raises(GridInitializationError, match="must be int or float"):
+    with pytest.raises(GridInitializationError, match="points"):
         IrregSpacedGrid(points=(1.0, "a", 3.0))  # ty: ignore[invalid-argument-type]
 
 
@@ -559,13 +559,13 @@ def test_piecewise_lin_spaced_grid_invalid_numeric_gap():
 
 def test_piecewise_lin_spaced_grid_invalid_not_tuple():
     """Pieces must be a tuple."""
-    with pytest.raises(GridInitializationError, match="must be a tuple"):
+    with pytest.raises(GridInitializationError, match="pieces"):
         PiecewiseLinSpacedGrid(pieces=[Piece(interval="[1, 4]", n_points=3)])  # ty: ignore[invalid-argument-type]
 
 
 def test_piecewise_lin_spaced_grid_invalid_not_piece():
     """Each element in pieces must be a Piece object."""
-    with pytest.raises(GridInitializationError, match="must be a Piece"):
+    with pytest.raises(GridInitializationError, match="pieces"):
         PiecewiseLinSpacedGrid(
             pieces=({"interval": "[1, 4]", "n_points": 3},)  # ty: ignore[invalid-argument-type]
         )
@@ -753,15 +753,15 @@ def test_piecewise_boundary_conditions(grid_cls, boundary_style: str):
         expected_coord_at = 5.0 if is_lin else 2.0
 
     # Test value just below boundary -> piece 0
-    coord_below = float(grid.get_coordinate(below_boundary))
+    coord_below = float(grid.get_coordinate(jnp.asarray(below_boundary)))
     assert coord_below < expected_coord_at
 
     # Test value exactly at boundary
-    coord_at = float(grid.get_coordinate(boundary))
+    coord_at = float(grid.get_coordinate(jnp.asarray(boundary)))
     assert coord_at == pytest.approx(expected_coord_at)
 
     # Test value just above boundary -> piece 1
-    coord_above = float(grid.get_coordinate(above_boundary))
+    coord_above = float(grid.get_coordinate(jnp.asarray(above_boundary)))
     assert coord_above > expected_coord_at
 
 
diff --git a/tests/test_heterogeneous_initial_ages.py b/tests/test_heterogeneous_initial_ages.py
index 43a251da5..47cee4356 100644
--- a/tests/test_heterogeneous_initial_ages.py
+++ b/tests/test_heterogeneous_initial_ages.py
@@ -22,7 +22,7 @@ def test_simulation_with_heterogeneous_initial_ages():
         initial_conditions={
             "age": jnp.array([40.0, 60.0]),
             "wealth": jnp.array([50.0, 50.0]),
-            "regime": jnp.array([RegimeId.working_life] * 2),
+            "regime_id": jnp.array([RegimeId.working_life] * 2),
         },
         period_to_regime_to_V_arr=None,
     )
diff --git a/tests/test_int_dtype_invariants.py b/tests/test_int_dtype_invariants.py
index 4c31f4864..a84a81fa4 100644
--- a/tests/test_int_dtype_invariants.py
+++ b/tests/test_int_dtype_invariants.py
@@ -1,6 +1,7 @@
 """Integer dtypes are pinned to int32 across pylcm regardless of x64 mode."""
 
 from types import MappingProxyType
+from typing import cast
 
 import jax.numpy as jnp
 import numpy as np
@@ -17,6 +18,8 @@
     build_initial_states,
 )
 from lcm.simulation.transitions import _advance_states_for_subjects
+from lcm.typing import ParamsTemplate
+from lcm.utils.containers import ensure_containers_are_immutable
 from tests.test_models.deterministic.regression import (
     RegimeId,
     dead,
@@ -25,6 +28,12 @@
     working_life,
 )
 
+
+def _as_template(plain: dict) -> ParamsTemplate:
+    """Deep-freeze a plain nested dict into a `ParamsTemplate` for tests."""
+    return cast("ParamsTemplate", ensure_containers_are_immutable(plain))
+
+
 # These tests deliberately pass `int64` inputs to verify the cast at
 # the barrier. Re-allow the JAX truncation warning that the
 # project-wide filter (see `pyproject.toml`) promotes to an error —
@@ -105,71 +114,80 @@ def test_advance_states_for_subjects_keeps_same_dtype_round_trip() -> None:
 
 def test_process_params_casts_python_int_to_int32() -> None:
     """A Python `int` param leaf is cast to `jnp.int32`."""
-    template = MappingProxyType({"regime_a": MappingProxyType({"final_age": "int"})})
-    user_params = {"regime_a": {"final_age": 65}}
+    template = _as_template({"regime_a": {"fun": {"final_age": "int"}}})
+    user_params = {"regime_a": {"fun": {"final_age": 65}}}
 
     out = process_params(
         params=user_params,
-        params_template=template,  # ty: ignore[invalid-argument-type]
+        params_template=template,
     )
 
-    final_age = out["regime_a"]["final_age"]
-    assert int(final_age) == 65
-    assert final_age.dtype == jnp.int32
+    final_age = out["regime_a"]["fun__final_age"]
+    assert int(final_age) == 65  # ty: ignore[invalid-argument-type]
+    assert final_age.dtype == jnp.int32  # ty: ignore[unresolved-attribute]
 
 
 def test_process_params_casts_int64_array_to_int32() -> None:
-    """A `jnp.int64` array param leaf is normalised to `jnp.int32`."""
-    template = MappingProxyType({"regime_a": MappingProxyType({"schedule": "Array"})})
-    user_params = {"regime_a": {"schedule": jnp.asarray([0, 1, 2], dtype=jnp.int64)}}
+    """An `int64` array param leaf is normalised to `jnp.int32`.
+
+    Built with `np.asarray`: a JAX `int64` array is rejected at the beartype
+    boundary (`_ParamsLeaf` pins JAX integer leaves to `int32`), so the
+    realistic raw-data path for a wider-dtype input is a numpy array.
+    """
+    template = _as_template({"regime_a": {"fun": {"schedule": "Array"}}})
+    user_params = {
+        "regime_a": {"fun": {"schedule": np.asarray([0, 1, 2], dtype=np.int64)}}
+    }
 
     out = process_params(
         params=user_params,
-        params_template=template,  # ty: ignore[invalid-argument-type]
+        params_template=template,
     )
 
-    schedule = out["regime_a"]["schedule"]
-    assert schedule.dtype == jnp.int32
+    schedule = out["regime_a"]["fun__schedule"]
+    assert schedule.dtype == jnp.int32  # ty: ignore[unresolved-attribute]
 
 
 def test_process_params_int_array_overflow_raises_with_qualified_name() -> None:
     """An out-of-int32-range int array surfaces the param's qualified name."""
-    template = MappingProxyType({"regime_a": MappingProxyType({"big_param": "Array"})})
+    template = _as_template({"regime_a": {"fun": {"big_param": "Array"}}})
     # Numpy here: under `jax_enable_x64=False`, `jnp.asarray(..., dtype=int64)`
     # of an out-of-int32 value raises before our helper sees it.
-    user_params = {"regime_a": {"big_param": np.asarray([0, 2**40], dtype=np.int64)}}
+    user_params = {
+        "regime_a": {"fun": {"big_param": np.asarray([0, 2**40], dtype=np.int64)}}
+    }
 
     with pytest.raises(ValueError, match="big_param"):
         process_params(
-            params=user_params,  # ty: ignore[invalid-argument-type]
-            params_template=template,  # ty: ignore[invalid-argument-type]
+            params=user_params,
+            params_template=template,
         )
 
 
 @pytest.mark.parametrize("key", ["low", "high"])
 def test_process_params_casts_int_array_inside_mapping_leaf_to_int32(key: str) -> None:
     """`MappingLeaf` int arrays land at `jnp.int32` after params processing."""
-    template = MappingProxyType(
-        {"regime_a": MappingProxyType({"sched": "MappingLeaf"})}
-    )
+    template = _as_template({"regime_a": {"fun": {"sched": "MappingLeaf"}}})
     user_params = {
         "regime_a": {
-            "sched": MappingLeaf(
-                {
-                    "low": jnp.asarray([0, 1], dtype=jnp.int64),
-                    "high": jnp.asarray([10, 20], dtype=jnp.int64),
-                }
-            )
+            "fun": {
+                "sched": MappingLeaf(
+                    {
+                        "low": jnp.asarray([0, 1], dtype=jnp.int64),
+                        "high": jnp.asarray([10, 20], dtype=jnp.int64),
+                    }
+                )
+            }
         }
     }
 
     out = process_params(
         params=user_params,
-        params_template=template,  # ty: ignore[invalid-argument-type]
+        params_template=template,
     )
 
     assert (
-        out["regime_a"]["sched"].data[key].dtype  # ty: ignore[unresolved-attribute]
+        out["regime_a"]["fun__sched"].data[key].dtype  # ty: ignore[unresolved-attribute, invalid-argument-type]
         == jnp.int32
     )
 
@@ -179,27 +197,27 @@ def test_process_params_casts_int_array_inside_sequence_leaf_to_int32(
     index: int,
 ) -> None:
     """`SequenceLeaf` int arrays land at `jnp.int32` after params processing."""
-    template = MappingProxyType(
-        {"regime_a": MappingProxyType({"sched": "SequenceLeaf"})}
-    )
+    template = _as_template({"regime_a": {"fun": {"sched": "SequenceLeaf"}}})
     user_params = {
         "regime_a": {
-            "sched": SequenceLeaf(
-                [
-                    jnp.asarray([0, 1], dtype=jnp.int64),
-                    jnp.asarray([10, 20], dtype=jnp.int64),
-                ]
-            )
+            "fun": {
+                "sched": SequenceLeaf(
+                    [
+                        jnp.asarray([0, 1], dtype=jnp.int64),
+                        jnp.asarray([10, 20], dtype=jnp.int64),
+                    ]
+                )
+            }
         }
     }
 
     out = process_params(
         params=user_params,
-        params_template=template,  # ty: ignore[invalid-argument-type]
+        params_template=template,
     )
 
     assert (
-        out["regime_a"]["sched"].data[index].dtype  # ty: ignore[unresolved-attribute]
+        out["regime_a"]["fun__sched"].data[index].dtype  # ty: ignore[unresolved-attribute, invalid-argument-type]
         == jnp.int32
     )
 
@@ -226,11 +244,11 @@ def test_simulate_accepts_int64_regime_initial_condition_and_round_trips() -> No
     }
     initial_conditions_int32 = {
         **common,
-        "regime": jnp.asarray([RegimeId.working_life] * 4, dtype=jnp.int32),
+        "regime_id": jnp.asarray([RegimeId.working_life] * 4, dtype=jnp.int32),
     }
     initial_conditions_int64 = {
         **common,
-        "regime": jnp.asarray([RegimeId.working_life] * 4, dtype=jnp.int64),
+        "regime_id": jnp.asarray([RegimeId.working_life] * 4, dtype=jnp.int64),
     }
 
     df_int32 = model.simulate(
diff --git a/tests/test_mapping_leaf.py b/tests/test_mapping_leaf.py
index 2e439ca61..21a3dde5b 100644
--- a/tests/test_mapping_leaf.py
+++ b/tests/test_mapping_leaf.py
@@ -5,7 +5,14 @@
 import jax.numpy as jnp
 import pytest
 
-from lcm.params import MappingLeaf, SequenceLeaf, as_leaf
+from lcm.exceptions import InvalidParamsError
+from lcm.params import (
+    MappingLeaf,
+    SequenceLeaf,
+    UserMappingLeaf,
+    UserSequenceLeaf,
+    as_leaf,
+)
 from lcm.utils.containers import (
     ensure_containers_are_immutable,
     ensure_containers_are_mutable,
@@ -152,28 +159,36 @@ def test_flatten_regime_namespace_treats_mapping_leaf_as_leaf():
 
 
 def test_as_leaf_mapping():
+    """`as_leaf` of a Mapping returns the boundary `UserMappingLeaf` variant."""
     result = as_leaf({"a": 1})
-    assert isinstance(result, MappingLeaf)
+    assert isinstance(result, UserMappingLeaf)
+    assert not isinstance(result, MappingLeaf)
     assert result.data == {"a": 1}
 
 
 def test_as_leaf_mapping_proxy():
+    """`as_leaf` of a `MappingProxyType` returns the boundary `UserMappingLeaf`."""
     result = as_leaf(MappingProxyType({"a": 1}))
-    assert isinstance(result, MappingLeaf)
+    assert isinstance(result, UserMappingLeaf)
+    assert not isinstance(result, MappingLeaf)
 
 
 def test_as_leaf_list():
+    """`as_leaf` of a list returns the boundary `UserSequenceLeaf` variant."""
     result = as_leaf([1, 2, 3])
-    assert isinstance(result, SequenceLeaf)
+    assert isinstance(result, UserSequenceLeaf)
+    assert not isinstance(result, SequenceLeaf)
     assert result.data == (1, 2, 3)
 
 
 def test_as_leaf_tuple():
+    """`as_leaf` of a tuple returns the boundary `UserSequenceLeaf` variant."""
     result = as_leaf((1, 2))
-    assert isinstance(result, SequenceLeaf)
+    assert isinstance(result, UserSequenceLeaf)
+    assert not isinstance(result, SequenceLeaf)
     assert result.data == (1, 2)
 
 
 def test_as_leaf_rejects_int():
-    with pytest.raises(TypeError, match="as_leaf"):
+    with pytest.raises(InvalidParamsError, match="as_leaf"):
         as_leaf(42)  # ty: ignore[no-matching-overload]
diff --git a/tests/test_model.py b/tests/test_model.py
index 8897fe431..53f5ad910 100644
--- a/tests/test_model.py
+++ b/tests/test_model.py
@@ -23,7 +23,7 @@
 
 def test_regime_invalid_states():
     """Regime rejects non-dict states argument."""
-    with pytest.raises(RegimeInitializationError, match="states must be a mapping"):
+    with pytest.raises(RegimeInitializationError, match="states"):
         Regime(
             transition=lambda: 0,
             states="health",  # ty: ignore[invalid-argument-type]
@@ -35,7 +35,7 @@ def test_regime_invalid_states():
 
 def test_regime_invalid_actions():
     """Regime rejects non-dict actions argument."""
-    with pytest.raises(RegimeInitializationError, match="actions must be a mapping"):
+    with pytest.raises(RegimeInitializationError, match="actions"):
         Regime(
             transition=lambda: 0,
             states={},
@@ -47,9 +47,7 @@ def test_regime_invalid_actions():
 
 def test_regime_invalid_functions():
     """Regime rejects non-dict functions argument."""
-    with pytest.raises(
-        RegimeInitializationError, match="functions must each be a mapping"
-    ):
+    with pytest.raises(RegimeInitializationError, match="functions"):
         Regime(
             transition=lambda: 0,
             states={},
@@ -61,10 +59,7 @@ def test_regime_invalid_functions():
 
 def test_regime_invalid_functions_values():
     """Regime rejects non-callable function values."""
-    with pytest.raises(
-        RegimeInitializationError,
-        match=r"function values must be a callable, but is 0.",
-    ):
+    with pytest.raises(RegimeInitializationError, match="functions"):
         Regime(
             states={},
             actions={},
@@ -76,9 +71,7 @@ def test_regime_invalid_functions_values():
 
 def test_regime_invalid_functions_keys():
     """Regime rejects non-string function keys."""
-    with pytest.raises(
-        RegimeInitializationError, match=r"function keys must be a strings, but is 0."
-    ):
+    with pytest.raises(RegimeInitializationError, match="functions"):
         Regime(
             states={},
             actions={},
@@ -90,9 +83,7 @@ def test_regime_invalid_functions_keys():
 
 def test_regime_invalid_actions_values():
     """Regime rejects non-grid action values."""
-    with pytest.raises(
-        RegimeInitializationError, match=r"actions value 0 must be an LCM grid."
-    ):
+    with pytest.raises(RegimeInitializationError, match="actions"):
         Regime(
             states={},
             actions={"exercise": 0},  # ty: ignore[invalid-argument-type]
@@ -104,9 +95,7 @@ def test_regime_invalid_actions_values():
 
 def test_regime_invalid_states_values():
     """Regime rejects non-grid state values."""
-    with pytest.raises(
-        RegimeInitializationError, match=r"states value 0 must be an LCM grid."
-    ):
+    with pytest.raises(RegimeInitializationError, match="states"):
         Regime(
             states={"health": 0},  # ty: ignore[invalid-argument-type]
             actions={},
@@ -118,10 +107,7 @@ def test_regime_invalid_states_values():
 
 def test_regime_invalid_utility():
     """Regime rejects non-callable utility argument."""
-    with pytest.raises(
-        RegimeInitializationError,
-        match=(r"function values must be a callable, but is 0"),
-    ):
+    with pytest.raises(RegimeInitializationError, match="functions"):
         Regime(
             states={},
             actions={},
@@ -151,10 +137,7 @@ def test_regime_overlapping_states_actions(binary_category_class):
 
 def test_regime_transition_must_be_callable():
     """Regime rejects non-callable transition."""
-    with pytest.raises(
-        RegimeInitializationError,
-        match="transition must be callable or None",
-    ):
+    with pytest.raises(RegimeInitializationError, match="transition"):
         Regime(
             states={},
             actions={},
diff --git a/tests/test_models/shock_grids.py b/tests/test_models/shock_grids.py
index 32f005ce4..6213f8619 100644
--- a/tests/test_models/shock_grids.py
+++ b/tests/test_models/shock_grids.py
@@ -213,7 +213,7 @@ def get_multi_regime_params(
     shock_params = _SHOCK_PARAMS[distribution_type]
     health_probs = jnp.full((2, 2), fill_value=0.5)
     regime_params = {
-        "discount_factor": 1,
+        "discount_factor": 1.0,
         "next_health": {"probs_array": health_probs},
         "income": shock_params,
     }
@@ -240,7 +240,7 @@ def get_params(
 ):
     return {
         "alive": {
-            "discount_factor": 1,
+            "discount_factor": 1.0,
             "next_health": {"probs_array": jnp.full((2, 2), fill_value=0.5)},
             "income": _SHOCK_PARAMS[distribution_type],
         },
diff --git a/tests/test_ndimage.py b/tests/test_ndimage.py
index 5bb379086..c4ce07b8f 100644
--- a/tests/test_ndimage.py
+++ b/tests/test_ndimage.py
@@ -26,7 +26,7 @@
 from tests.conftest import DECIMAL_PRECISION, X64_ENABLED
 
 # Use 64-bit dtypes when x64 is enabled, 32-bit otherwise
-TEST_DTYPES = [np.int64, np.float64] if X64_ENABLED else [np.int32, np.float32]
+TEST_DTYPES = [np.int32, np.float64] if X64_ENABLED else [np.int32, np.float32]
 
 jax_map_coordinates = partial(jax.scipy.ndimage.map_coordinates, order=1, cval=0)
 scipy_map_coordinates = partial(scipy.ndimage.map_coordinates, order=1, cval=0)
diff --git a/tests/test_ndimage_unit.py b/tests/test_ndimage_unit.py
index 516a5aec2..f70903acc 100644
--- a/tests/test_ndimage_unit.py
+++ b/tests/test_ndimage_unit.py
@@ -12,8 +12,11 @@
 
 
 def test_map_coordinates_wrong_input_dimensions():
-    values = jnp.arange(2)  # ndim = 1
-    coordinates = [jnp.array([0]), jnp.array([1])]  # len = 2
+    values = jnp.arange(2, dtype=jnp.int32)  # ndim = 1
+    coordinates = [
+        jnp.array([0], dtype=jnp.int32),
+        jnp.array([1], dtype=jnp.int32),
+    ]  # len = 2
     with pytest.raises(ValueError, match="coordinates must be a sequence of length"):
         map_coordinates(values, coordinates)
 
@@ -29,7 +32,7 @@ def test_map_coordinates_extrapolation():
 
 
 def test_nonempty_sum():
-    a = jnp.arange(3)
+    a = jnp.arange(3, dtype=jnp.int32)
 
     expected = a + a + a
     got = _sum_all([a, a, a])
@@ -38,7 +41,7 @@ def test_nonempty_sum():
 
 
 def test_nonempty_prod():
-    a = jnp.arange(3)
+    a = jnp.arange(3, dtype=jnp.int32)
 
     expected = a * a * a
     got = _multiply_all([a, a, a])
@@ -75,7 +78,7 @@ def test_linear_indices_and_weights_inside_domain():
 
 
 def test_linear_indices_and_weights_outside_domain():
-    coordinates = jnp.array([-1, 2])
+    coordinates = jnp.array([-1.0, 2.0])
 
     (idx_low, weight_low), (idx_high, weight_high) = _compute_indices_and_weights(
         coordinates, input_size=2
diff --git a/tests/test_next_state.py b/tests/test_next_state.py
index f89bad620..08f51649b 100644
--- a/tests/test_next_state.py
+++ b/tests/test_next_state.py
@@ -102,7 +102,7 @@ class MockCategory:
 
 
 def test_create_stochastic_next_func():
-    labels = jnp.arange(2)
+    labels = jnp.arange(2, dtype=jnp.int32)
     got_func = _create_discrete_stochastic_next_func(
         target="t", next_state_name="next_a", labels=labels
     )
diff --git a/tests/test_pandas_utils.py b/tests/test_pandas_utils.py
index 17bd50a23..e406e8015 100644
--- a/tests/test_pandas_utils.py
+++ b/tests/test_pandas_utils.py
@@ -128,11 +128,34 @@ class HealthAlt:
         _build_discrete_grid_lookup(regimes)
 
 
+def test_regime_name_column_maps_to_regime_id_codes():
+    """`regime_name` column (strings) yields a `regime_id` dict entry of int codes."""
+    model = get_basic_model()
+    df = pd.DataFrame(
+        {
+            "regime_name": ["working_life", "retirement"],
+            "health": ["bad", "good"],
+            "wealth": [10.0, 50.0],
+            "age": [25.0, 25.0],
+        }
+    )
+    conditions = initial_conditions_from_dataframe(
+        df=df,
+        regimes=model.regimes,
+        regime_names_to_ids=model.regime_names_to_ids,
+    )
+    assert jnp.array_equal(
+        conditions["regime_id"],
+        jnp.array([BasicRegimeId.working_life, BasicRegimeId.retirement]),
+    )
+    assert "regime_name" not in conditions
+
+
 def test_continuous_states_and_age():
     model = get_basic_model()
     df = pd.DataFrame(
         {
-            "regime": ["working_life", "working_life"],
+            "regime_name": ["working_life", "working_life"],
             "health": ["bad", "good"],
             "wealth": [10.0, 50.0],
             "age": [25.0, 35.0],
@@ -144,7 +167,7 @@ def test_continuous_states_and_age():
         regime_names_to_ids=model.regime_names_to_ids,
     )
     assert jnp.array_equal(
-        conditions["regime"],
+        conditions["regime_id"],
         jnp.array([BasicRegimeId.working_life, BasicRegimeId.working_life]),
     )
     assert jnp.allclose(conditions["wealth"], jnp.array([10.0, 50.0]))
@@ -155,7 +178,7 @@ def test_categorical_string_labels():
     model = get_basic_model()
     df = pd.DataFrame(
         {
-            "regime": ["working_life", "retirement"],
+            "regime_name": ["working_life", "retirement"],
             "health": ["bad", "good"],
             "wealth": [10.0, 50.0],
             "age": [25.0, 25.0],
@@ -167,7 +190,7 @@ def test_categorical_string_labels():
         regime_names_to_ids=model.regime_names_to_ids,
     )
     assert jnp.array_equal(
-        conditions["regime"],
+        conditions["regime_id"],
         jnp.array([BasicRegimeId.working_life, BasicRegimeId.retirement]),
     )
     assert jnp.array_equal(conditions["health"], jnp.array([Health.bad, Health.good]))
@@ -178,7 +201,7 @@ def test_categorical_pd_categorical_column():
     health_dtype = Health.to_categorical_dtype()  # ty: ignore[unresolved-attribute]
     df = pd.DataFrame(
         {
-            "regime": ["working_life", "working_life"],
+            "regime_name": ["working_life", "working_life"],
             "health": pd.Categorical(["good", "bad"], dtype=health_dtype),
             "wealth": [10.0, 50.0],
             "age": [25.0, 25.0],
@@ -196,7 +219,7 @@ def test_multi_regime():
     model = get_basic_model()
     df = pd.DataFrame(
         {
-            "regime": ["working_life", "retirement", "working_life"],
+            "regime_name": ["working_life", "retirement", "working_life"],
             "health": ["good", "bad", "good"],
             "wealth": [10.0, 50.0, 30.0],
             "age": [25.0, 25.0, 25.0],
@@ -208,7 +231,7 @@ def test_multi_regime():
         regime_names_to_ids=model.regime_names_to_ids,
     )
     assert jnp.array_equal(
-        conditions["regime"],
+        conditions["regime_id"],
         jnp.array(
             [
                 BasicRegimeId.working_life,
@@ -223,7 +246,7 @@ def test_multi_regime():
 def test_missing_regime_column_raises():
     model = get_basic_model()
     df = pd.DataFrame({"wealth": [10.0]})
-    with pytest.raises(ValueError, match="'regime' column"):
+    with pytest.raises(ValueError, match="'regime_name' column"):
         initial_conditions_from_dataframe(
             df=df,
             regimes=model.regimes,
@@ -235,7 +258,7 @@ def test_invalid_regime_name_raises():
     model = get_basic_model()
     df = pd.DataFrame(
         {
-            "regime": ["working_life", "nonexistent"],
+            "regime_name": ["working_life", "nonexistent"],
             "wealth": [10.0, 50.0],
         }
     )
@@ -251,7 +274,7 @@ def test_invalid_category_label_raises():
     model = get_basic_model()
     df = pd.DataFrame(
         {
-            "regime": ["working_life"],
+            "regime_name": ["working_life"],
             "health": ["excellent"],
             "wealth": [10.0],
             "age": [25.0],
@@ -268,7 +291,7 @@ def test_invalid_category_label_raises():
 def test_empty_dataframe_raises():
     model = get_basic_model()
     df = pd.DataFrame(
-        {"regime": pd.Series([], dtype=str), "wealth": pd.Series([], dtype=float)}
+        {"regime_name": pd.Series([], dtype=str), "wealth": pd.Series([], dtype=float)}
     )
     with pytest.raises(ValueError, match="empty"):
         initial_conditions_from_dataframe(
@@ -282,7 +305,7 @@ def test_unknown_column_raises():
     model = get_basic_model()
     df = pd.DataFrame(
         {
-            "regime": ["working_life"],
+            "regime_name": ["working_life"],
             "health": ["bad"],
             "wealth": [10.0],
             "age": [25.0],
@@ -301,7 +324,7 @@ def test_missing_state_column_raises():
     model = get_basic_model()
     df = pd.DataFrame(
         {
-            "regime": ["working_life"],
+            "regime_name": ["working_life"],
             "age": [25.0],
             # missing "health" and "wealth"
         }
@@ -319,7 +342,7 @@ def test_shock_state_columns_accepted():
     model = get_shock_model(n_periods=4, distribution_type="uniform")
     df = pd.DataFrame(
         {
-            "regime": ["alive", "alive"],
+            "regime_name": ["alive", "alive"],
             "wealth": [2.0, 4.0],
             "health": ["bad", "good"],
             "income": [0.3, 0.7],
@@ -333,7 +356,7 @@ def test_shock_state_columns_accepted():
     )
     assert jnp.allclose(conditions["income"], jnp.array([0.3, 0.7]))
     assert jnp.allclose(conditions["wealth"], jnp.array([2.0, 4.0]))
-    assert "regime" in conditions
+    assert "regime_id" in conditions
 
 
 def test_shock_state_columns_required():
@@ -341,7 +364,7 @@ def test_shock_state_columns_required():
     model = get_shock_model(n_periods=4, distribution_type="uniform")
     df = pd.DataFrame(
         {
-            "regime": ["alive", "alive"],
+            "regime_name": ["alive", "alive"],
             "wealth": [2.0, 4.0],
             "health": ["bad", "good"],
             "age": [0.0, 0.0],
@@ -372,7 +395,7 @@ def test_round_trip_with_discrete_model():
     raw_conditions = {
         "wealth": jnp.array([DiscreteWealth.low, DiscreteWealth.high]),
         "age": jnp.array([50.0, 50.0]),
-        "regime": jnp.array([RegimeId.working_life, RegimeId.working_life]),
+        "regime_id": jnp.array([RegimeId.working_life, RegimeId.working_life]),
     }
     result_raw = model.simulate(
         params=params,
@@ -383,7 +406,7 @@ def test_round_trip_with_discrete_model():
     # DataFrame approach
     df = pd.DataFrame(
         {
-            "regime": ["working_life", "working_life"],
+            "regime_name": ["working_life", "working_life"],
             "wealth": ["low", "high"],
             "age": [50.0, 50.0],
         }
@@ -472,7 +495,7 @@ def test_initial_conditions_heterogeneous_health_grids() -> None:
     model = _get_heterogeneous_health_model()
     df = pd.DataFrame(
         {
-            "regime": ["pre65", "pre65", "post65", "post65"],
+            "regime_name": ["pre65", "pre65", "post65", "post65"],
             "health": ["disabled", "good", "bad", "good"],
             "wealth": [10.0, 50.0, 30.0, 70.0],
             "age": [50.0, 50.0, 70.0, 70.0],
@@ -488,7 +511,7 @@ def test_initial_conditions_heterogeneous_health_grids() -> None:
     assert jnp.array_equal(result["health"], jnp.array([0, 2, 0, 1]))
     assert jnp.allclose(result["wealth"], jnp.array([10.0, 50.0, 30.0, 70.0]))
     assert jnp.array_equal(
-        result["regime"],
+        result["regime_id"],
         jnp.array(
             [
                 _HetRegimeId.pre65,
@@ -552,7 +575,7 @@ def _utility_without_status(wealth: float) -> float:
 
     df = pd.DataFrame(
         {
-            "regime": ["with_status", "with_status", "without_status"],
+            "regime_name": ["with_status", "with_status", "without_status"],
             "wealth": [10.0, 20.0, 30.0],
             "status": ["low", "high", pd.NA],
             "age": [50.0, 51.0, 50.0],
@@ -616,7 +639,7 @@ def _retiree_utility(wealth: float) -> float:
 
     df = pd.DataFrame(
         {
-            "regime": ["earner", "earner", "retiree"],
+            "regime_name": ["earner", "earner", "retiree"],
             "wealth": [10.0, 20.0, 30.0],
             "income": [0.3, 0.7, float("nan")],
             "age": [50.0, 51.0, 50.0],
@@ -704,7 +727,7 @@ def test_heterogeneous_health_solve_simulate() -> None:
     model = _get_heterogeneous_health_model()
     df = pd.DataFrame(
         {
-            "regime": ["pre65", "pre65", "post65", "post65"],
+            "regime_name": ["pre65", "pre65", "post65", "post65"],
             "health": ["disabled", "good", "bad", "good"],
             "wealth": [10.0, 50.0, 30.0, 70.0],
             "age": [50.0, 50.0, 70.0, 70.0],
@@ -732,7 +755,9 @@ def test_heterogeneous_health_solve_simulate() -> None:
     assert list(period_0["health"]) == ["disabled", "good"]
 
     # Period 2: post65 subjects have correct health labels
-    period_2 = out.query("period == 2 and regime == 'post65'").sort_values("subject_id")
+    period_2 = out.query("period == 2 and regime_name == 'post65'").sort_values(
+        "subject_id"
+    )
     assert list(period_2["health"]) == ["bad", "good"]
 
 
@@ -741,7 +766,7 @@ def test_heterogeneous_health_simulate_use_labels_false() -> None:
     model = _get_heterogeneous_health_model()
     df = pd.DataFrame(
         {
-            "regime": ["pre65", "post65"],
+            "regime_name": ["pre65", "post65"],
             "health": ["disabled", "good"],
             "wealth": [10.0, 70.0],
             "age": [50.0, 70.0],
@@ -1444,8 +1469,8 @@ def test_convert_series_function_level_series() -> None:
         regime_names_to_ids=model.regime_names_to_ids,
     )
     arr = result["working_life"]["next_partner__probs_array"]
-    assert arr.shape == (3, 2, 2, 2)
-    assert float(arr[0, 0, 0, 0]) == pytest.approx(1.0)
+    assert arr.shape == (3, 2, 2, 2)  # ty: ignore[unresolved-attribute]
+    assert float(arr[0, 0, 0, 0]) == pytest.approx(1.0)  # ty: ignore[not-subscriptable]
 
 
 def test_convert_series_model_level_scalar_passthrough() -> None:
@@ -1485,7 +1510,7 @@ def test_convert_series_regime_level_series() -> None:
         regime_names_to_ids=model.regime_names_to_ids,
     )
     arr = result["working_life"]["next_partner__probs_array"]
-    assert arr.shape == (3, 2, 2, 2)
+    assert arr.shape == (3, 2, 2, 2)  # ty: ignore[unresolved-attribute]
 
 
 def test_convert_series_mixed_dict() -> None:
@@ -1512,20 +1537,20 @@ def test_convert_series_mixed_dict() -> None:
     )
     assert result["working_life"]["H__discount_factor"] == 0.95
     assert result["working_life"]["utility__disutility_of_work"] == 0.5
-    assert result["working_life"]["next_partner__probs_array"].shape == (3, 2, 2, 2)
+    assert result["working_life"]["next_partner__probs_array"].shape == (3, 2, 2, 2)  # ty: ignore[unresolved-attribute]
     assert result["working_life"]["next_wealth__interest_rate"] == 0.05
-    np.testing.assert_allclose(
+    np.testing.assert_allclose(  # ty: ignore[no-matching-overload]
         result["working_life"]["labor_income__wage"], jnp.array([10.0])
     )
 
 
 def test_convert_series_mapping_leaf() -> None:
-    """Series inside a MappingLeaf is converted."""
-    from lcm.params import MappingLeaf  # noqa: PLC0415
+    """Series inside a `UserMappingLeaf` is converted in place."""
+    from lcm.params import UserMappingLeaf  # noqa: PLC0415
 
     model = get_stochastic_model(3)
     series = _build_partner_probs_series(model)
-    leaf = MappingLeaf({"sub_key": series})
+    leaf = UserMappingLeaf({"sub_key": series})
     params = {
         "working_life": {
             "next_partner": {"probs_array": leaf},
@@ -1541,19 +1566,19 @@ def test_convert_series_mapping_leaf() -> None:
         regime_names_to_ids=model.regime_names_to_ids,
     )
     converted_leaf = result["working_life"]["next_partner__probs_array"]
-    assert isinstance(converted_leaf, MappingLeaf)
+    assert isinstance(converted_leaf, UserMappingLeaf)
     arr = converted_leaf.data["sub_key"]
-    assert arr.shape == (3, 2, 2, 2)
+    assert arr.shape == (3, 2, 2, 2)  # ty: ignore[unresolved-attribute]
 
 
 def test_convert_series_nested_mapping_leaf() -> None:
-    """Series inside nested MappingLeaf is recursively converted."""
-    from lcm.params import MappingLeaf  # noqa: PLC0415
+    """Series inside nested `UserMappingLeaf` is recursively converted."""
+    from lcm.params import UserMappingLeaf  # noqa: PLC0415
 
     model = get_stochastic_model(3)
     series = _build_partner_probs_series(model)
-    inner = MappingLeaf({"sub": series})
-    outer = MappingLeaf({"inner_leaf": inner})
+    inner = UserMappingLeaf({"sub": series})
+    outer = UserMappingLeaf({"inner_leaf": inner})
     params = {
         "working_life": {
             "next_partner": {"probs_array": outer},
@@ -1569,11 +1594,11 @@ def test_convert_series_nested_mapping_leaf() -> None:
         regime_names_to_ids=model.regime_names_to_ids,
     )
     converted = result["working_life"]["next_partner__probs_array"]
-    assert isinstance(converted, MappingLeaf)
+    assert isinstance(converted, UserMappingLeaf)
     inner_converted = converted.data["inner_leaf"]
-    assert isinstance(inner_converted, MappingLeaf)
+    assert isinstance(inner_converted, UserMappingLeaf)
     assert not isinstance(inner_converted.data["sub"], pd.Series)
-    assert inner_converted.data["sub"].shape == (3, 2, 2, 2)
+    assert inner_converted.data["sub"].shape == (3, 2, 2, 2)  # ty: ignore[unresolved-attribute]
 
 
 def test_convert_series_unknown_param_raises() -> None:
@@ -1656,7 +1681,7 @@ def test_convert_series_with_derived_categoricals() -> None:
         regime_names_to_ids=model.regime_names_to_ids,
     )
     arr = result["retirement"]["next_partner__probs_array"]
-    assert arr.shape == (3, 2, 2, 2)
+    assert arr.shape == (3, 2, 2, 2)  # ty: ignore[unresolved-attribute]
 
 
 def test_convert_series_per_target_transition() -> None:
@@ -1736,7 +1761,7 @@ def _next_wealth(wealth: float) -> float:
         regime_names_to_ids=model.regime_names_to_ids,
     )
     arr = result["working"]["to_working_next_health__probs_array"]
-    assert arr.shape == (3, 2, 2)
+    assert arr.shape == (3, 2, 2)  # ty: ignore[unresolved-attribute]
 
 
 def test_build_outcome_mapping_qualified_func_name() -> None:
@@ -1834,8 +1859,8 @@ def _next_wealth_sc(wealth: float) -> float:
         ages=model.ages,
         regime_names_to_ids=model.regime_names_to_ids,
     )
-    assert result_both["regime_a"]["utility__rates"].shape == (2,)
-    assert result_both["regime_b"]["utility__rates"].shape == (3,)
+    assert result_both["regime_a"]["utility__rates"].shape == (2,)  # ty: ignore[unresolved-attribute]
+    assert result_both["regime_b"]["utility__rates"].shape == (3,)  # ty: ignore[unresolved-attribute]
 
 
 def test_convert_series_runtime_grid_param() -> None:
@@ -1876,16 +1901,16 @@ class _RId:
         ages=model.ages,
         regime_names_to_ids=model.regime_names_to_ids,
     )
-    np.testing.assert_allclose(result["alive"]["wealth__points"], sr.to_numpy())
+    np.testing.assert_allclose(result["alive"]["wealth__points"], sr.to_numpy())  # ty: ignore[no-matching-overload]
 
 
 def test_convert_series_sequence_leaf_traversal() -> None:
-    """Series inside a SequenceLeaf should be converted to JAX arrays."""
-    from lcm.params.sequence_leaf import SequenceLeaf  # noqa: PLC0415
+    """Series inside a `UserSequenceLeaf` should be converted to JAX arrays."""
+    from lcm.params.sequence_leaf import UserSequenceLeaf  # noqa: PLC0415
 
     model = get_stochastic_model(3)
     sr = pd.Series([10.0])
-    leaf = SequenceLeaf((sr, 42))
+    leaf = UserSequenceLeaf((sr, 42))
     params = {"working_life": {"labor_income": {"wage": leaf}}}
     internal = broadcast_to_template(
         params=params, template=model._params_template, required=False
@@ -1897,9 +1922,9 @@ def test_convert_series_sequence_leaf_traversal() -> None:
         regime_names_to_ids=model.regime_names_to_ids,
     )
     converted = result["working_life"]["labor_income__wage"]
-    assert isinstance(converted, SequenceLeaf)
+    assert isinstance(converted, UserSequenceLeaf)
     assert not isinstance(converted.data[0], pd.Series)
-    np.testing.assert_allclose(converted.data[0], jnp.array([10.0]))
+    np.testing.assert_allclose(converted.data[0], jnp.array([10.0]))  # ty: ignore[no-matching-overload]
 
 
 def test_resolve_categoricals_conflict_raises() -> None:
@@ -2022,7 +2047,7 @@ def _health_probs_cross(
     arr = result["pre65"]["to_post65_next_health__health_trans_probs_cross"]
     # Shape: (n_ages=2, n_source_health=3, n_target_health=2)
     # n_ages=2 because AgeGrid has ages [0, 1]; missing age 1 is NaN-filled.
-    assert arr.shape == (2, 3, 2)
+    assert arr.shape == (2, 3, 2)  # ty: ignore[unresolved-attribute]
 
 
 def test_resolve_categoricals_includes_derived_when_no_regime_name() -> None:
diff --git a/tests/test_persistence.py b/tests/test_persistence.py
index d99ac6d5a..917f29b49 100644
--- a/tests/test_persistence.py
+++ b/tests/test_persistence.py
@@ -5,6 +5,7 @@
 import jax.numpy as jnp
 import pytest
 
+import lcm
 from lcm import (
     AgeGrid,
     LinSpacedGrid,
@@ -15,10 +16,26 @@
     categorical,
     load_snapshot,
 )
+from lcm import persistence as _persistence
+from lcm import variables as _variables
 from lcm.persistence import _get_platform, load_solution, save_solution
+from lcm.simulation.result import SimulationResult as _PublicSimulationResult
 from lcm.typing import ContinuousAction, ContinuousState, FloatND, ScalarInt
 
 
+def test_forward_refs_bound_after_import() -> None:
+    """`Model` and `SimulationResult` are present in `lcm.persistence`'s globals.
+
+    The package claw rewrites their string annotations on `save_simulate_snapshot`
+    into runtime forward references resolved against this module's globals at
+    call time. Missing the binding leaves those calls failing with
+    `BeartypeCallHintForwardRefException`.
+    """
+    assert _persistence.Model is lcm.Model
+    assert _persistence.SimulationResult is _PublicSimulationResult
+    assert _variables.Regime is lcm.Regime
+
+
 @categorical(ordered=False)
 class _RegimeId:
     working: ScalarInt
@@ -71,7 +88,7 @@ def _initial_conditions():
     return {
         "wealth": jnp.array([2.0, 3.0]),
         "age": jnp.array([0.0, 0.0]),
-        "regime": jnp.array([_RegimeId.working] * 2),
+        "regime_id": jnp.array([_RegimeId.working] * 2),
     }
 
 
diff --git a/tests/test_random.py b/tests/test_random.py
index 8e4396e22..437686113 100644
--- a/tests/test_random.py
+++ b/tests/test_random.py
@@ -1,14 +1,23 @@
+import jax
 import jax.numpy as jnp
 
 from lcm.simulation.random import generate_simulation_keys
 
 
 def test_generate_simulation_keys():
-    key = jnp.arange(2, dtype="uint32")  # PRNG dtype
+    """Each per-name key stream split from a seed key is mutually distinct."""
+    key = jax.random.key(0)
     stochastic_next_functions = ["a", "b"]
     got = generate_simulation_keys(
         key=key, names=stochastic_next_functions, n_initial_states=1
     )
-    # assert that all generated keys are different from each other
-    matrix = jnp.array([key, got[0], got[1]["key_a"][0], got[1]["key_b"][0]])
+    # Compare the raw key data: distinct keys give a rank-2 matrix of key rows.
+    matrix = jnp.array(
+        [
+            jax.random.key_data(key),
+            jax.random.key_data(got[0]),
+            jax.random.key_data(got[1]["key_a"][0]),
+            jax.random.key_data(got[1]["key_b"][0]),
+        ]
+    )
     assert jnp.linalg.matrix_rank(matrix) == 2
diff --git a/tests/test_regime.py b/tests/test_regime.py
index 94b9e768b..4c322452a 100644
--- a/tests/test_regime.py
+++ b/tests/test_regime.py
@@ -167,7 +167,7 @@ def test_regime_requires_utility_in_functions():
 
 def test_active_validation_rejects_non_callable():
     """Active attribute must be a callable."""
-    with pytest.raises(RegimeInitializationError, match="must be a callable"):
+    with pytest.raises(RegimeInitializationError, match="active"):
         Regime(
             transition=next_wealth,
             functions={"utility": utility},
@@ -179,10 +179,7 @@ def test_active_validation_rejects_non_callable():
 
 
 def test_markov_transition_rejects_non_callable():
-    with pytest.raises(
-        RegimeInitializationError,
-        match="MarkovTransition requires a callable",
-    ):
+    with pytest.raises(RegimeInitializationError, match="func"):
         MarkovTransition(func=42)  # ty: ignore[invalid-argument-type]
 
 
@@ -196,8 +193,8 @@ def test_identity_transition_call():
 def test_identity_transition_discrete():
     """Identity transition works for discrete states."""
     identity = _IdentityTransition("education", annotation=DiscreteState)
-    result = identity(education=jnp.array(1))
-    assert result == jnp.array(1)
+    result = identity(education=jnp.array(1, dtype=jnp.int32))
+    assert result == jnp.array(1, dtype=jnp.int32)
 
 
 def test_identity_transition_name():
diff --git a/tests/test_regime_state_mismatch.py b/tests/test_regime_state_mismatch.py
index 20e22fed2..d3d0fea8f 100644
--- a/tests/test_regime_state_mismatch.py
+++ b/tests/test_regime_state_mismatch.py
@@ -190,12 +190,12 @@ def next_regime(age: float) -> ScalarInt:
         initial_conditions={
             "age": jnp.array([0.0, 0.0]),
             "wealth": jnp.array([20.0, 80.0]),
-            "regime": jnp.array([_RegimeId.alive] * 2),
+            "regime_id": jnp.array([_RegimeId.alive] * 2),
         },
         period_to_regime_to_V_arr=period_to_regime_to_V_arr,
     )
     df = result.to_dataframe(use_labels=False)
-    dead_rows = df[df["regime"] == "dead"]
+    dead_rows = df[df["regime_name"] == "dead"]
     valid_codes = {float(HeirPresent.no), float(HeirPresent.yes)}
     assert not dead_rows.empty, "Expected some rows in the dead regime"
     assert dead_rows["heir_present"].isin(valid_codes).all()
@@ -282,12 +282,12 @@ def utility_dead(wealth: ContinuousState, heir_present: DiscreteState) -> FloatN
         initial_conditions={
             "age": jnp.array([0.0, 0.0]),
             "wealth": jnp.array([20.0, 80.0]),
-            "regime": jnp.array([_RegimeId.alive] * 2),
+            "regime_id": jnp.array([_RegimeId.alive] * 2),
         },
         period_to_regime_to_V_arr=period_to_regime_to_V_arr,
     )
     df = result.to_dataframe(use_labels=False)
-    dead_rows = df[df["regime"] == "dead"]
+    dead_rows = df[df["regime_name"] == "dead"]
     valid_codes = {float(HeirPresent.no), float(HeirPresent.yes)}
     assert not dead_rows.empty, "Expected some rows in the dead regime"
     assert dead_rows["heir_present"].isin(valid_codes).all()
@@ -369,13 +369,13 @@ def test_per_target_dict_transitions():
         initial_conditions={
             "age": jnp.zeros(n_subjects),
             "health": initial_health,
-            "regime": jnp.array([RegimeId.working_life] * n_subjects),
+            "regime_id": jnp.array([RegimeId.working_life] * n_subjects),
         },
         period_to_regime_to_V_arr=period_to_regime_to_V_arr,
     )
     df = result.to_dataframe(use_labels=False)
 
-    retired_rows = df[df["regime"] == "retirement"]
+    retired_rows = df[df["regime_name"] == "retirement"]
     valid_retired_codes = {float(HealthRetirement.bad), float(HealthRetirement.good)}
     assert not retired_rows.empty, "Expected some rows in the retired regime"
     assert retired_rows["health"].isin(valid_retired_codes).all(), (
diff --git a/tests/test_regression_test.py b/tests/test_regression_test.py
index a6a47168e..887f0edf2 100644
--- a/tests/test_regression_test.py
+++ b/tests/test_regression_test.py
@@ -59,7 +59,7 @@ def test_regression_test():
         initial_conditions={
             "wealth": jnp.array([5.0, 20, 40, 70]),
             "age": jnp.array([18.0, 18.0, 18.0, 18.0]),
-            "regime": jnp.array([RegimeId.working_life] * 4),
+            "regime_id": jnp.array([RegimeId.working_life] * 4),
         },
         period_to_regime_to_V_arr=None,
     ).to_dataframe()
@@ -101,7 +101,7 @@ def test_regression_precautionary_savings():
             "age": jnp.full(n_subjects, 20.0),
             "wealth": jnp.full(n_subjects, 5.0),
             "income": jnp.full(n_subjects, 0.0),
-            "regime": jnp.zeros(n_subjects, dtype=jnp.int32),
+            "regime_id": jnp.zeros(n_subjects, dtype=jnp.int32),
         },
         period_to_regime_to_V_arr=None,
         seed=12345,
@@ -134,7 +134,7 @@ def test_regression_mortality():
         initial_conditions={
             "age": jnp.full(n_subjects, 40.0),
             "wealth": jnp.full(n_subjects, 100.0),
-            "regime": jnp.zeros(n_subjects, dtype=jnp.int32),
+            "regime_id": jnp.zeros(n_subjects, dtype=jnp.int32),
         },
         period_to_regime_to_V_arr=None,
         seed=12345,
@@ -176,7 +176,7 @@ def test_regression_mahler_yum():
     params = {"alive": common_params}
     initial_conditions = {
         **initial_states,
-        "regime": jnp.full(
+        "regime_id": jnp.full(
             n_subjects,
             model.regime_names_to_ids["alive"],
             dtype=jnp.int32,
@@ -283,7 +283,7 @@ def test_model_with_different_grid_types(grid_type: str):
         initial_conditions={
             "wealth": jnp.array([5.0, 20, 40, 70]),
             "age": jnp.array([18.0, 18.0, 18.0, 18.0]),
-            "regime": jnp.array([RegimeId.working_life] * 4),
+            "regime_id": jnp.array([RegimeId.working_life] * 4),
         },
         period_to_regime_to_V_arr=None,
     )
diff --git a/tests/test_runtime_params.py b/tests/test_runtime_params.py
index 58fdfc27c..aba678941 100644
--- a/tests/test_runtime_params.py
+++ b/tests/test_runtime_params.py
@@ -300,7 +300,7 @@ def test_simulate_with_runtime_action_grid_no_nan() -> None:
         "alive": {"consumption": {"points": jnp.linspace(0.1, 5.0, 5)}},
     }
     initial_conditions = {
-        "regime": jnp.array([RegimeId.alive, RegimeId.alive, RegimeId.alive]),
+        "regime_id": jnp.array([RegimeId.alive, RegimeId.alive, RegimeId.alive]),
         "age": jnp.array([0.0, 0.0, 0.0]),
         "wealth": jnp.array([2.0, 5.0, 9.0]),
     }
diff --git a/tests/test_shock_grids.py b/tests/test_shock_grids.py
index 256247f03..be478e049 100644
--- a/tests/test_shock_grids.py
+++ b/tests/test_shock_grids.py
@@ -35,11 +35,11 @@ def test_model_with_shock(distribution_type):
     got_simulate = model.simulate(
         params=params,
         initial_conditions={
-            "health": jnp.asarray([0, 0]),
-            "income": jnp.asarray([0, 0]),
-            "wealth": jnp.asarray([1, 1]),
+            "health": jnp.asarray([0, 0], dtype=jnp.int32),
+            "income": jnp.asarray([0.0, 0.0]),
+            "wealth": jnp.asarray([1.0, 1.0]),
             "age": jnp.asarray([0.0, 0.0]),
-            "regime": jnp.array([RegimeId.alive] * 2),
+            "regime_id": jnp.array([RegimeId.alive] * 2),
         },
         period_to_regime_to_V_arr=got_solve,
         seed=42,
@@ -84,16 +84,16 @@ def test_model_with_cross_regime_shocks(distribution_type: str) -> None:
     result = model.simulate(
         params=params,
         initial_conditions={
-            "health": jnp.zeros(2, dtype=int),
+            "health": jnp.zeros(2, dtype=jnp.int32),
             "income": jnp.zeros(2),
             "wealth": jnp.ones(2),
             "age": jnp.zeros(2),
-            "regime": jnp.full(2, MultiRegimeId.work, dtype=int),
+            "regime_id": jnp.full(2, MultiRegimeId.work, dtype=jnp.int32),
         },
         period_to_regime_to_V_arr=None,
         seed=42,
     ).to_dataframe()
-    assert set(result["regime"]) >= {"work", "retire"}
+    assert set(result["regime_name"]) >= {"work", "retire"}
 
 
 _GRID_CLASSES_WITH_GH_KWARG = [
diff --git a/tests/test_single_feasible_action.py b/tests/test_single_feasible_action.py
index 08b61ef7a..f35a58d07 100644
--- a/tests/test_single_feasible_action.py
+++ b/tests/test_single_feasible_action.py
@@ -232,7 +232,7 @@ def test_simulate_with_constrained_action_grid(wealth_lo, consumption_lo, label)
     initial_conditions = {
         "age": jnp.array([0.0, 0.0, 0.0]),
         "wealth": jnp.array([wealth_lo, 5.0, 20.0]),
-        "regime": jnp.array(
+        "regime_id": jnp.array(
             [RegimeId.alive, RegimeId.alive, RegimeId.alive], dtype=jnp.int32
         ),
     }
@@ -500,7 +500,7 @@ def next_regime(age, last_alive_age):
     initial_conditions = {
         "age": jnp.array([0.0, 0.0, 0.0]),
         "wealth": jnp.array([2.0, 5.0, 9.0]),
-        "regime": jnp.array(
+        "regime_id": jnp.array(
             [RuntimeRegimeId.alive, RuntimeRegimeId.alive, RuntimeRegimeId.alive],
             dtype=jnp.int32,
         ),
@@ -524,7 +524,7 @@ def test_runtime_action_grid_passes_initial_conditions_validation():
     initial_conditions = {
         "age": jnp.array([0.0, 0.0, 0.0]),
         "wealth": jnp.array([10.0, 15.0, 20.0]),
-        "regime": jnp.array(
+        "regime_id": jnp.array(
             [RegimeId.alive, RegimeId.alive, RegimeId.alive], dtype=jnp.int32
         ),
     }
diff --git a/tests/test_solution_on_toy_model_deterministic.py b/tests/test_solution_on_toy_model_deterministic.py
index 98996a52b..890c80ad3 100644
--- a/tests/test_solution_on_toy_model_deterministic.py
+++ b/tests/test_solution_on_toy_model_deterministic.py
@@ -156,7 +156,7 @@ def analytical_solve_deterministic(wealth_grid, params):
 def analytical_simulate_deterministic(initial_wealth, params):
     """Compute analytical simulation results in the same format as to_dataframe().
 
-    Returns DataFrame with columns: period, subject_id, regime, value, wealth,
+    Returns DataFrame with columns: period, subject_id, regime_name, value, wealth,
     consumption, working. Sorted by (subject_id, period).
     Uses categorical dtypes for discrete variables to match to_dataframe() output.
     """
@@ -186,7 +186,7 @@ def analytical_simulate_deterministic(initial_wealth, params):
                 [np.zeros(n_subjects), np.ones(n_subjects)]
             ).astype(int),
             "subject_id": np.tile(np.arange(n_subjects), 2),
-            "regime": pd.Categorical(
+            "regime_name": pd.Categorical(
                 ["alive"] * (2 * n_subjects), categories=["alive", "dead"]
             ),
             "value": np.concatenate([V_arr_0, V_arr_1]),
@@ -216,7 +216,7 @@ def dict_of_vectors_to_matrix(d):
     return np.column_stack(list(d.values()))
 
 
-@pytest.mark.parametrize("discount_factor", [0, 0.5, 0.9, 1.0])
+@pytest.mark.parametrize("discount_factor", [0.0, 0.5, 0.9, 1.0])
 @pytest.mark.parametrize("n_wealth_points", [100, 1_000])
 def test_deterministic_solve(discount_factor, n_wealth_points):
     n_periods = 3
@@ -263,7 +263,7 @@ def test_deterministic_solve(discount_factor, n_wealth_points):
     aaae(got[1]["alive"], expected[1], decimal=DECIMAL_PRECISION)
 
 
-@pytest.mark.parametrize("discount_factor", [0, 0.5, 0.9, 1.0])
+@pytest.mark.parametrize("discount_factor", [0.0, 0.5, 0.9, 1.0])
 @pytest.mark.parametrize("n_wealth_points", [100, 1_000])
 def test_deterministic_simulate(discount_factor, n_wealth_points):
     n_periods = 3
@@ -292,14 +292,14 @@ def test_deterministic_simulate(discount_factor, n_wealth_points):
         initial_conditions={
             "wealth": jnp.array([0.25, 0.75, 1.25, 1.75]),
             "age": jnp.array([0.0, 0.0, 0.0, 0.0]),
-            "regime": jnp.array([RegimeId.alive] * 4),
+            "regime_id": jnp.array([RegimeId.alive] * 4),
         },
         period_to_regime_to_V_arr=None,
     )
     # Filter to alive regime only (dead regime has trivial values)
     got = (
         result.to_dataframe()
-        .query('regime == "alive"')
+        .query('regime_name == "alive"')
         .drop(columns=["age"])  # Analytical function doesn't include age
         .reset_index(drop=True)
     )
diff --git a/tests/test_solution_on_toy_model_stochastic.py b/tests/test_solution_on_toy_model_stochastic.py
index fdb175b1e..d4a3ba58e 100644
--- a/tests/test_solution_on_toy_model_stochastic.py
+++ b/tests/test_solution_on_toy_model_stochastic.py
@@ -144,8 +144,8 @@ def analytical_solve_stochastic(wealth_grid, health_grid, params):
 def analytical_simulate_stochastic(initial_wealth, initial_health, health_1, params):
     """Compute analytical simulation results in the same format as to_dataframe().
 
-    Returns DataFrame with columns: period, subject_id, regime, value, health, wealth,
-    consumption, working. Sorted by (subject_id, period).
+    Returns DataFrame with columns: period, subject_id, regime_name, value,
+    health, wealth, consumption, working. Sorted by (subject_id, period).
     Uses categorical dtypes for discrete variables to match to_dataframe() output.
     """
     n_subjects = len(initial_wealth)
@@ -179,7 +179,7 @@ def analytical_simulate_stochastic(initial_wealth, initial_health, health_1, par
                 [np.zeros(n_subjects), np.ones(n_subjects)]
             ).astype(int),
             "subject_id": np.tile(np.arange(n_subjects), 2),
-            "regime": pd.Categorical(
+            "regime_name": pd.Categorical(
                 ["alive"] * (2 * n_subjects), categories=["alive", "dead"]
             ),
             "value": np.concatenate([V_arr_0, V_arr_1]),
@@ -208,7 +208,7 @@ def analytical_simulate_stochastic(initial_wealth, initial_health, health_1, par
 ]
 
 
-@pytest.mark.parametrize("discount_factor", [0, 0.5, 0.9, 1.0])
+@pytest.mark.parametrize("discount_factor", [0.0, 0.5, 0.9, 1.0])
 @pytest.mark.parametrize("n_wealth_points", [100, 1_000])
 @pytest.mark.parametrize("probs_array", HEALTH_TRANSITION)
 def test_stochastic_solve(discount_factor, n_wealth_points, probs_array):
@@ -267,7 +267,7 @@ def test_stochastic_solve(discount_factor, n_wealth_points, probs_array):
     aaae(got[1]["alive"], expected[1], decimal=DECIMAL_PRECISION)
 
 
-@pytest.mark.parametrize("discount_factor", [0, 0.5, 0.9, 1.0])
+@pytest.mark.parametrize("discount_factor", [0.0, 0.5, 0.9, 1.0])
 @pytest.mark.parametrize("n_wealth_points", [100, 1_000])
 @pytest.mark.parametrize("probs_array", HEALTH_TRANSITION)
 def test_stochastic_simulate(discount_factor, n_wealth_points, probs_array):
@@ -296,7 +296,7 @@ def test_stochastic_simulate(discount_factor, n_wealth_points, probs_array):
         "wealth": jnp.array([0.25, 0.75, 1.25, 1.75, 2.0]),
         "health": jnp.array([0, 1, 0, 1, 1]),
         "age": jnp.array([0.0, 0.0, 0.0, 0.0, 0.0]),
-        "regime": jnp.array([RegimeId.alive] * 5),
+        "regime_id": jnp.array([RegimeId.alive] * 5),
     }
     result = model.simulate(
         params={"discount_factor": discount_factor, "alive": params_alive},
@@ -304,7 +304,7 @@ def test_stochastic_simulate(discount_factor, n_wealth_points, probs_array):
         period_to_regime_to_V_arr=None,
     )
     # Filter to alive regime only (dead regime has trivial values)
-    got = result.to_dataframe().query('regime == "alive"').reset_index(drop=True)
+    got = result.to_dataframe().query('regime_name == "alive"').reset_index(drop=True)
 
     # Need to use health of second period from LCM output, to assure that the same
     # stochastic draws are used in the analytical simulation.
diff --git a/tests/test_static_params.py b/tests/test_static_params.py
index 4669145ca..7e980acf7 100644
--- a/tests/test_static_params.py
+++ b/tests/test_static_params.py
@@ -110,7 +110,7 @@ def test_simulate_with_fixed_params():
         initial_conditions={
             "wealth": jnp.array([5.0, 7.0]),
             "age": jnp.array([0.0, 0.0]),
-            "regime": jnp.array([RegimeId.alive] * 2),
+            "regime_id": jnp.array([RegimeId.alive] * 2),
         },
         period_to_regime_to_V_arr=None,
         log_level="off",
@@ -126,7 +126,7 @@ def test_simulate_with_fixed_params():
         initial_conditions={
             "wealth": jnp.array([5.0, 7.0]),
             "age": jnp.array([0.0, 0.0]),
-            "regime": jnp.array([RegimeId.alive] * 2),
+            "regime_id": jnp.array([RegimeId.alive] * 2),
         },
         period_to_regime_to_V_arr=None,
         log_level="off",
@@ -225,7 +225,7 @@ def test_all_params_fixed():
     "wealth": jnp.array([50.0, 80.0]),
     "health": jnp.array([Health.bad, Health.good]),
     "age": jnp.array([60.0, 60.0]),
-    "regime": jnp.array([MarkovRegimeId.alive] * 2),
+    "regime_id": jnp.array([MarkovRegimeId.alive] * 2),
 }
 
 
@@ -362,7 +362,7 @@ def test_series_fixed_param_with_derived_categoricals():
         initial_conditions={
             "wealth": jnp.array([3.0, 8.0]),
             "age": jnp.array([0.0, 0.0]),
-            "regime": jnp.array([RegimeId.alive] * 2),
+            "regime_id": jnp.array([RegimeId.alive] * 2),
         },
         period_to_regime_to_V_arr=None,
         log_level="off",
diff --git a/tests/test_stochastic.py b/tests/test_stochastic.py
index 2ec8ec107..9bcd91f4b 100644
--- a/tests/test_stochastic.py
+++ b/tests/test_stochastic.py
@@ -41,15 +41,15 @@ def test_model_simulate_with_stochastic_model():
     result = model.simulate(
         params=params,
         initial_conditions={
-            "health": jnp.array([1, 1, 0, 0]),
-            "partner": jnp.array([0, 0, 1, 0]),
+            "health": jnp.array([1, 1, 0, 0], dtype=jnp.int32),
+            "partner": jnp.array([0, 0, 1, 0], dtype=jnp.int32),
             "wealth": jnp.array([10.0, 50.0, 30, 80.0]),
             "age": jnp.array([40.0, 40.0, 40.0, 40.0]),
-            "regime": jnp.array([RegimeId.working_life] * 4),
+            "regime_id": jnp.array([RegimeId.working_life] * 4),
         },
         period_to_regime_to_V_arr=None,
     )
-    df = result.to_dataframe().query('regime == "working_life"')
+    df = result.to_dataframe().query('regime_name == "working_life"')
 
     # Verify expected columns
     required_cols = {"period", "subject_id", "partner", "labor_supply"}
@@ -190,11 +190,11 @@ def test_compare_deterministic_and_stochastic_results_value_function(
     # Compare simulation results
     # ==================================================================================
     initial_conditions = {
-        "health": jnp.array([1, 1, 0, 0]),
-        "partner": jnp.array([0, 0, 0, 0]),
+        "health": jnp.array([1, 1, 0, 0], dtype=jnp.int32),
+        "partner": jnp.array([0, 0, 0, 0], dtype=jnp.int32),
         "wealth": jnp.array([10.0, 50.0, 30, 80.0]),
         "age": jnp.array([40.0, 40.0, 40.0, 40.0]),
-        "regime": jnp.array([RegimeId.working_life] * 4),
+        "regime_id": jnp.array([RegimeId.working_life] * 4),
     }
 
     simulation_deterministic = model_deterministic.simulate(
@@ -208,10 +208,10 @@ def test_compare_deterministic_and_stochastic_results_value_function(
         initial_conditions=initial_conditions,
     )
     df_deterministic = simulation_deterministic.to_dataframe().query(
-        'regime == "working_life"'
+        'regime_name == "working_life"'
     )
     df_stochastic = simulation_stochastic.to_dataframe().query(
-        'regime == "working_life"'
+        'regime_name == "working_life"'
     )
     pd.testing.assert_frame_equal(
         df_deterministic.reset_index(drop=True),
diff --git a/tests/test_validate_param_types.py b/tests/test_validate_param_types.py
index 06a95d597..51a5a6c29 100644
--- a/tests/test_validate_param_types.py
+++ b/tests/test_validate_param_types.py
@@ -60,7 +60,7 @@ def test_numpy_array_param_normalised_to_canonical_jax_array() -> None:
     """A numpy array param is cast to a JAX array at `canonical_float_dtype()`."""
     model = _make_model()
     internal = model._process_params(
-        params={"bonus": np.asarray(1.0, dtype=np.float64), "discount_factor": 0.95}  # ty: ignore[invalid-argument-type]
+        params={"bonus": np.asarray(1.0, dtype=np.float64), "discount_factor": 0.95}
     )
     bonus = internal["working"]["utility__bonus"]
     assert isinstance(bonus, Array)
@@ -74,7 +74,7 @@ def test_jax_array_param_kept_at_canonical_dtype() -> None:
         params={"bonus": jnp.asarray(1.0), "discount_factor": 0.95}
     )
     bonus = internal["working"]["utility__bonus"]
-    assert bonus.dtype == canonical_float_dtype()
+    assert bonus.dtype == canonical_float_dtype()  # ty: ignore[unresolved-attribute]
 
 
 def test_python_float_param_cast_to_canonical_dtype() -> None:
@@ -82,5 +82,5 @@ def test_python_float_param_cast_to_canonical_dtype() -> None:
     model = _make_model()
     internal = model._process_params(params={"bonus": 1.0, "discount_factor": 0.95})
     bonus = internal["working"]["utility__bonus"]
-    assert float(bonus) == 1.0
-    assert bonus.dtype == canonical_float_dtype()
+    assert float(bonus) == 1.0  # ty: ignore[invalid-argument-type]
+    assert bonus.dtype == canonical_float_dtype()  # ty: ignore[unresolved-attribute]
diff --git a/tests/test_validate_regime_transition_probs.py b/tests/test_validate_regime_transition_probs.py
index 9d97f1e87..9f121344b 100644
--- a/tests/test_validate_regime_transition_probs.py
+++ b/tests/test_validate_regime_transition_probs.py
@@ -240,6 +240,56 @@ def test_solve_catches_transition_bug_hidden_at_first_grid_point():
         model.solve(params=params)
 
 
+def test_regime_transition_validation_passes_period_as_int32():
+    """Regime-transition validation hands the transition function a 0-d int32 `period`.
+
+    The x64-enabled test suite would otherwise trace a Python-int `period` as
+    int64 inside `jax.vmap`, breaking any consumer that dtype-checks its
+    `period` slot (e.g. a beartyped `Period` hint).
+    """
+    seen_period_dtypes: list = []
+
+    def _transition_recording_period(
+        action: DiscreteAction, period: ScalarInt
+    ) -> FloatND:
+        seen_period_dtypes.append(getattr(period, "dtype", None))
+        # `action` keeps a grid variable in the signature so validation takes
+        # the `jax.vmap` path; both outcomes route to the always-active
+        # terminal regime so the probabilities are valid at every transition.
+        return jnp.where(
+            action == _Action.leave,
+            jnp.array([0.0, 1.0]),
+            jnp.array([0.0, 1.0]),
+        )
+
+    active = Regime(
+        transition=MarkovTransition(_transition_recording_period),
+        active=lambda age: age < 27,
+        actions={
+            "action": DiscreteGrid(_Action),
+            "consumption": LinSpacedGrid(start=1, stop=10, n_points=5),
+        },
+        states={"wealth": LinSpacedGrid(start=1, stop=10, n_points=5)},
+        state_transitions={"wealth": lambda wealth, consumption: wealth - consumption},
+        constraints={"budget": lambda consumption, wealth: consumption <= wealth},
+        functions={"utility": lambda consumption: jnp.log(consumption)},  # noqa: PLW0108
+    )
+    terminal = Regime(
+        transition=None,
+        states={"wealth": LinSpacedGrid(start=1, stop=10, n_points=5)},
+        functions={"utility": lambda wealth: jnp.log(wealth)},  # noqa: PLW0108
+    )
+    model = Model(
+        regimes={"active": active, "terminal": terminal},
+        ages=AgeGrid(start=25, stop=27, step="Y"),
+        regime_id_class=_RegimeId,
+    )
+    model.solve(params={"discount_factor": 0.95})
+
+    assert seen_period_dtypes
+    assert all(dtype == jnp.int32 for dtype in seen_period_dtypes)
+
+
 N_PERIODS = 4
 
 
@@ -268,7 +318,7 @@ def test_simulate_raises_for_invalid_regime_transition_probs():
     initial_conditions = {
         "age": jnp.array([40.0]),
         "wealth": jnp.array([10.0]),
-        "regime": jnp.array([MortalityRegimeId.working_life]),
+        "regime_id": jnp.array([MortalityRegimeId.working_life]),
     }
     with pytest.raises(InvalidRegimeTransitionProbabilitiesError):
         model.simulate(
@@ -285,7 +335,7 @@ def test_simulate_with_solve_raises_for_invalid_regime_transition_probs():
     initial_conditions = {
         "age": jnp.array([40.0]),
         "wealth": jnp.array([10.0]),
-        "regime": jnp.array([MortalityRegimeId.working_life]),
+        "regime_id": jnp.array([MortalityRegimeId.working_life]),
     }
     with pytest.raises(InvalidRegimeTransitionProbabilitiesError):
         model.simulate(
diff --git a/tests/test_validation_scalar_actions.py b/tests/test_validation_scalar_actions.py
index e53e521b3..1f982f694 100644
--- a/tests/test_validation_scalar_actions.py
+++ b/tests/test_validation_scalar_actions.py
@@ -38,7 +38,7 @@ def _aggregate_with_ids(
     """
     data = jnp.array([consumption])  # shape-(1,) when scalar
     ids = invariant_array.data["ids"]  # shape-(1,) always
-    result = jax.ops.segment_sum(data, ids, num_segments=1)
+    result = jax.ops.segment_sum(data, ids, num_segments=1)  # ty: ignore[invalid-argument-type]
     return result.squeeze()
 
 
@@ -108,7 +108,7 @@ def test_validation_vmaps_over_action_combos():
         initial_conditions={
             "wealth": jnp.array([5.0, 7.0]),
             "age": jnp.array([0.0, 0.0]),
-            "regime": jnp.array([RegimeId.alive] * 2),
+            "regime_id": jnp.array([RegimeId.alive] * 2),
         },
         period_to_regime_to_V_arr=None,
         log_level="off",
diff --git a/tests/test_variables.py b/tests/test_variables.py
index 02998948f..2de2a0974 100644
--- a/tests/test_variables.py
+++ b/tests/test_variables.py
@@ -162,7 +162,7 @@ def next_state(x):
         },
         functions={"utility": lambda c_action: 0},  # noqa: ARG005
     )
-    variables = Variables.from_regime(regime)  # ty: ignore[invalid-argument-type]
+    variables = Variables.from_regime(regime)
     assert list(variables) == ["a_discrete", "b_continuous", "c_action"]
 
 
@@ -188,5 +188,5 @@ def next_state(x):
         actions={"a": DiscreteGrid(binary_category_class)},
         functions={"utility": lambda a: 0},  # noqa: ARG005
     )
-    variables = Variables.from_regime(regime)  # ty: ignore[invalid-argument-type]
+    variables = Variables.from_regime(regime)
     assert variables.discrete_state_names == ("first", "second", "third")