diff --git a/celeri/censored_distribution_stability_hotfix.py b/celeri/censored_distribution_stability_hotfix.py
deleted file mode 100644
index a5cdfcb5..00000000
--- a/celeri/censored_distribution_stability_hotfix.py
+++ /dev/null
@@ -1,160 +0,0 @@
-"""Hotfix module providing numerically stable log-probabilities for censored Normal distributions.
-
-This is a temporary workaround until https://github.com/pymc-devs/pymc/pull/7996 is merged.
-
-The fix monkey-patches the MeasurableClip logprob to use a stable log survival function
-for Normal distributions instead of the numerically unstable log(1 - exp(logcdf)).
-
-Usage:
-    import stable_censored_hotfix  # Just import to apply the fix
-
-    with pm.Model():
-        normal_dist = pm.Normal.dist(mu=0.0, sigma=1.0)
-        y = pm.Censored("y", normal_dist, lower=None, upper=40.0, observed=data)
-"""
-
-import numpy as np
-import pytensor.tensor as pt
-from pymc.distributions.dist_math import normal_lccdf
-from pymc.logprob.abstract import _logcdf, _logprob
-from pymc.logprob.censoring import MeasurableClip
-from pymc.logprob.utils import CheckParameterValue
-from pytensor.tensor.variable import TensorConstant
-
-
-def _stable_normal_logccdf(mu, sigma, value):
-    """Numerically stable log complementary CDF (log survival function) for Normal.
-
-    Uses erfcx-based implementation that is stable even in extreme tails.
-    """
-    return normal_lccdf(mu, sigma, value)
-
-
-def _get_stable_logccdf(base_rv_op, base_rv_inputs, value, logcdf_fallback):
-    """Get numerically stable log complementary CDF if available.
-
-    For Normal distribution, uses the stable erfcx-based implementation.
-    For other distributions, falls back to log1mexp(logcdf).
-    """
-    from pytensor.tensor.random.basic import NormalRV
-
-    if isinstance(base_rv_op, NormalRV):
-        # Normal distribution: use stable implementation
-        # base_rv_inputs are: rng, size, mu, sigma
-        rng, size, mu, sigma = base_rv_inputs
-        return _stable_normal_logccdf(mu, sigma, value)
-    else:
-        # Fall back to potentially unstable computation
-        return pt.log1mexp(logcdf_fallback)
-
-
-def _stable_clip_logprob(op, values, base_rv, lower_bound, upper_bound, **kwargs):
-    r"""Stable logprob of a clipped censored distribution.
-
-    The probability is given by
-    .. math::
-        \begin{cases}
-            0 & \text{for } x < lower, \\
-            \text{CDF}(lower, dist) & \text{for } x = lower, \\
-            \text{P}(x, dist) & \text{for } lower < x < upper, \\
-            1-\text{CDF}(upper, dist) & \text {for} x = upper, \\
-            0 & \text{for } x > upper,
-        \end{cases}
-
-    """
-    (value,) = values
-
-    base_rv_op = base_rv.owner.op
-    base_rv_inputs = base_rv.owner.inputs
-
-    logprob = _logprob(base_rv_op, (value,), *base_rv_inputs, **kwargs)
-    logcdf = _logcdf(base_rv_op, value, *base_rv_inputs, **kwargs)
-
-    if base_rv_op.name:
-        logprob.name = f"{base_rv_op}_logprob"
-        logcdf.name = f"{base_rv_op}_logcdf"
-
-    is_lower_bounded, is_upper_bounded = False, False
-    if not (
-        isinstance(upper_bound, TensorConstant) and np.all(np.isinf(upper_bound.value))
-    ):
-        is_upper_bounded = True
-
-        # Use stable logccdf for Normal distributions instead of pt.log1mexp(logcdf)
-        logccdf = _get_stable_logccdf(base_rv_op, base_rv_inputs, value, logcdf)
-
-        # For right clipped discrete RVs, we need to add an extra term
-        # corresponding to the pmf at the upper bound
-        if base_rv.dtype.startswith("int"):
-            logccdf = pt.logaddexp(logccdf, logprob)
-
-        logprob = pt.switch(
-            pt.eq(value, upper_bound),
-            logccdf,
-            pt.switch(pt.gt(value, upper_bound), -np.inf, logprob),
-        )
-    if not (
-        isinstance(lower_bound, TensorConstant)
-        and np.all(np.isneginf(lower_bound.value))
-    ):
-        is_lower_bounded = True
-        logprob = pt.switch(
-            pt.eq(value, lower_bound),
-            logcdf,
-            pt.switch(pt.lt(value, lower_bound), -np.inf, logprob),
-        )
-
-    if is_lower_bounded and is_upper_bounded:
-        logprob = CheckParameterValue("lower_bound <= upper_bound")(
-            logprob, pt.all(pt.le(lower_bound, upper_bound))
-        )
-
-    return logprob
-
-
-def _apply_fix():
-    """Apply the fix by overriding the singledispatch registry."""
-    # Use the register decorator to replace the existing function
-    _logprob.register(MeasurableClip, _stable_clip_logprob)
-
-
-# Apply the fix on import
-_apply_fix()
-
-
-def verify_fix():
-    """Verify that the stable implementation works correctly."""
-    import pymc as pm
-    import scipy.stats as st
-
-    with pm.Model() as model:
-        normal_dist = pm.Normal.dist(mu=0.0, sigma=1.0)
-        pm.Censored("y", normal_dist, lower=None, upper=40.0)
-
-    logp_fn = model.compile_logp()
-    result = logp_fn({"y": 40.0})
-    expected = st.norm(0, 1).logsf(40.0)
-
-    if not np.isfinite(result):
-        raise RuntimeError(
-            f"Stable censored fix not working: got {result}, expected {expected}"
-        )
-
-    if not np.isclose(result, expected, rtol=1e-6):
-        raise RuntimeError(
-            f"Stable censored result mismatch: got {result}, expected {expected}"
-        )
-
-    return True
-
-
-if __name__ == "__main__":
-    print("Verifying stable censored fix...")
-    verify_fix()
-    print("✓ Stable censored fix is working correctly!")
-    print("\nUsage:")
-    print("  import stable_censored_hotfix  # Just import to apply the fix")
-    print("  ")
-    print("  with pm.Model():")
-    print("      normal_dist = pm.Normal.dist(mu=0.0, sigma=1.0)")
-    print("      y = pm.Censored('y', normal_dist, lower=None, upper=40.0)")
diff --git a/celeri/solve_mcmc.py b/celeri/solve_mcmc.py
index a2336f82..7879ebce 100644
--- a/celeri/solve_mcmc.py
+++ b/celeri/solve_mcmc.py
@@ -19,11 +19,6 @@ class PymcModel:
 else:
     from pymc import Model as PymcModel
 
-    # Apply numerical stability fix for censored Normal distributions.
-    # This is a workaround for https://github.com/pymc-devs/pymc/pull/7996
-    # Fixes issue https://github.com/brendanjmeade/celeri/issues/341
-    import celeri.censored_distribution_stability_hotfix  # noqa: F401
-
 
 DIRECTION_IDX = {
     "strike_slip": slice(None, None, 2),
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+  - __osx >=10.13
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+  - numpy >=2.0
+  - numba >0.57,<1
   - filelock >=3.15
   - etuples
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   - minikanren
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-  - python 3.13.* *_cp313
   - libcxx >=19
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-  - python_abi 3.13.* *_cp313
+  - python 3.13.* *_cp313
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-- pypi: https://files.pythonhosted.org/packages/17/85/32c68a35c319551304c80dc2539e8776dc1a1d87775506092e61ea2090c0/tfp_nightly-0.26.0.dev20260121-py2.py3-none-any.whl
+- pypi: https://files.pythonhosted.org/packages/96/8d/5751d380b21143c065c149b069d578dfd78df656d6b3fb752264a5f2771d/tfp_nightly-0.26.0.dev20260126-py2.py3-none-any.whl
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+  version: 0.26.0.dev20260126
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-- conda: https://conda.anaconda.org/conda-forge/noarch/typer-slim-0.21.1-pyhcf101f3_0.conda
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diff --git a/pixi.toml b/pixi.toml
index 5976edbc..eab1dac1 100644
--- a/pixi.toml
+++ b/pixi.toml
@@ -47,7 +47,7 @@ polars = ">=1.27.1"
 ipykernel = ">=6.29.5"
 seaborn = ">=0.13.2"
 nutpie = ">=0.15.2"
-pymc = ">=5.25.1"
+pymc = ">5.27.0"
 numba = ">=0.61.2"
 papermill = ">=2.6.0"
 python-gmsh = ">=4.13.1"