diff --git a/docs/api_reference/developer/inference/smoother_configs.md b/docs/api_reference/developer/inference/smoother_configs.md
new file mode 100644
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@@ -0,0 +1,20 @@
+# Smoother Configurations
+
+Developer-facing API reference for smoother config classes and dispatch types.
+
+Concrete smoother configs intentionally mirror the filter config hierarchy while
+requiring users to opt into smoothing-specific classes. Backend support is
+validated by `Smoother` before dispatch:
+
+- discrete `KFSmootherConfig` and `EKFSmootherConfig`: `cuthbert` or `cd_dynamax`
+- discrete `UKFSmootherConfig`: `cd_dynamax`
+- discrete `PFSmootherConfig`: `cuthbert`
+- continuous `ContinuousTimeKFSmootherConfig` and
+  `ContinuousTimeEKFSmootherConfig`: `cd_dynamax`
+
+Smoother-specific fields live on the concrete classes rather than a nested
+options object, which keeps handler dispatch and API docs aligned.
+
+::: dynestyx.inference.smoother_configs
+    options:
+      filters: []
diff --git a/docs/api_reference/developer/inference/smoothers.md b/docs/api_reference/developer/inference/smoothers.md
new file mode 100644
index 00000000..acd3aa20
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+++ b/docs/api_reference/developer/inference/smoothers.md
@@ -0,0 +1,7 @@
+# Smoothers
+
+Developer-facing API reference for smoother handler internals and dispatch.
+
+::: dynestyx.inference.smoothers
+    options:
+      filters: []
diff --git a/docs/api_reference/public/inference/smoother_configs.md b/docs/api_reference/public/inference/smoother_configs.md
new file mode 100644
index 00000000..7f0e60ea
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+++ b/docs/api_reference/public/inference/smoother_configs.md
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+# Smoother Configurations
+
+`Smoother` is configured using explicit `*SmootherConfig` classes.
+
+Use smoother configs instead of filter configs when entering a `Smoother`
+handler. The classes inherit the familiar filtering options, plus smoother
+recording fields such as `record_smoothed_states_mean`,
+`record_smoothed_states_cov_diag`, `record_smoothed_particles`, and
+`record_smoothed_log_weights`.
+
+## Common Choices
+
+```python
+from dynestyx.inference.smoother_configs import (
+    ContinuousTimeKFSmootherConfig,
+    KFSmootherConfig,
+    PFSmootherConfig,
+)
+
+kf = KFSmootherConfig(filter_source="cd_dynamax")
+pf = PFSmootherConfig(filter_source="cuthbert", n_particles=1_000)
+ct_kf = ContinuousTimeKFSmootherConfig()
+```
+
+`PFSmootherConfig` exposes particle-smoother options:
+`pf_backward_sampling_method`, `pf_mcmc_n_steps`, and
+`pf_n_smoother_particles`. `ContinuousTimeKFSmootherConfig` exposes
+`cdlgssm_smoother_type` for the CD-Dynamax continuous-discrete linear
+Gaussian smoother variant.
+
+::: dynestyx.inference.smoother_configs
+    options:
+      filters: []
diff --git a/docs/api_reference/public/inference/smoothers.md b/docs/api_reference/public/inference/smoothers.md
new file mode 100644
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+++ b/docs/api_reference/public/inference/smoothers.md
@@ -0,0 +1,54 @@
+# Smoothers
+
+`Smoother` computes posterior smoothing distributions \(p(x_t \mid y_{1:T})\) and adds the corresponding marginal log-likelihood factor for parameter inference, mirroring `Filter` semantics.
+
+## Usage
+
+```python
+from dynestyx import DiscreteTimeSimulator, Smoother
+from dynestyx.inference.smoother_configs import KFSmootherConfig
+
+with DiscreteTimeSimulator(n_simulations=4):
+    with Smoother(
+        smoother_config=KFSmootherConfig(
+            filter_source="cd_dynamax",
+            record_smoothed_states_mean=True,
+        )
+    ):
+        samples = model(
+            obs_times=obs_times,
+            obs_values=obs_values,
+            predict_times=future_times,
+        )
+```
+
+`obs_times` and `obs_values` are required together. `Smoother` consumes them,
+adds a marginal log-likelihood factor, and can record deterministic sites such
+as `f_smoothed_states_mean`, `f_smoothed_states_cov`, and
+`f_smoothed_states_cov_diag`.
+
+## Prediction Semantics
+
+For this release, smoother-backed prediction is intentionally future-only:
+every `predict_time` must satisfy `predict_time >= max(obs_times)`. The
+downstream simulator rolls out from the final smoothed state distribution.
+
+Prediction times inside the smoothing window currently raise a clear error
+instead of silently using incorrect indexing or backend-specific missing-data
+behavior.
+
+## Support Matrix
+
+| Model class | Config | Backend |
+| --- | --- | --- |
+| Discrete linear-Gaussian | `KFSmootherConfig` | `cuthbert`, `cd_dynamax` |
+| Discrete nonlinear Gaussian | `EKFSmootherConfig` | `cuthbert`, `cd_dynamax` |
+| Discrete nonlinear Gaussian | `UKFSmootherConfig` | `cd_dynamax` |
+| Discrete non-Gaussian/nonlinear | `PFSmootherConfig` | `cuthbert` |
+| Continuous-discrete linear-Gaussian | `ContinuousTimeKFSmootherConfig` | `cd_dynamax` |
+| Continuous-discrete nonlinear Gaussian | `ContinuousTimeEKFSmootherConfig` | `cd_dynamax` |
+
+::: dynestyx.inference.smoothers
+    options:
+      members:
+        - Smoother
diff --git a/docs/tutorials/gentle_intro/00_index.ipynb b/docs/tutorials/gentle_intro/00_index.ipynb
index 05f718be..8c5e7e25 100644
--- a/docs/tutorials/gentle_intro/00_index.ipynb
+++ b/docs/tutorials/gentle_intro/00_index.ipynb
@@ -29,9 +29,13 @@
         "\n",
         "7. **[Part 6b: Continuous-time dynamical systems (ODEs)](../06b_odes/)** - `ContinuousTimeStateEvolution` (drift and no diffusions); `ODESimulator`. Probabilistic numerics coming soon!\n",
         "\n",
-        "7. **[Part 7: Hidden Markov Models (HMMs)](../07_hmm/)** — Working with categorical state spaces models (filtering and Bayesian parameter estimation).\n",
+        "8. **[Part 7: Hidden Markov Models (HMMs)](../07_hmm/)** — Working with categorical state spaces models (filtering and Bayesian parameter estimation).\n",
         "\n",
-        "8. **[Part 8: Hierarchical / mixed-effect inference](../08_hierarchical_inference/)** — `plate` for trajectory-level parameters; hierarchical dynamical models and inference."
+        "9. **[Part 8: Hierarchical / mixed-effect inference](../08_hierarchical_inference/)** — `plate` for trajectory-level parameters; hierarchical dynamical models and inference.\n",
+        "\n",
+        "10. **[Part 9: Discrete-time smoothing](../09_discrete_smoothing/)** — `Smoother` for discrete-time models; smoothed state summaries and future-only posterior rollout.\n",
+        "\n",
+        "11. **[Part 10: Continuous-time smoothing](../10_continuous_smoothing/)** — Continuous-discrete smoothing; smoothed state summaries and future-only posterior rollout."
       ]
     },
     {
diff --git a/docs/tutorials/gentle_intro/09_discrete_smoothing.ipynb b/docs/tutorials/gentle_intro/09_discrete_smoothing.ipynb
new file mode 100644
index 00000000..7d97f07d
--- /dev/null
+++ b/docs/tutorials/gentle_intro/09_discrete_smoothing.ipynb
@@ -0,0 +1,659 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Part 9: Discrete-Time Smoothing\n",
+        "\n",
+        "In the filtering notebooks, we used `Filter` to compute a marginal likelihood and, optionally, filtering summaries of the form\n",
+        "\n",
+        "$$\n",
+        "p(x_t \\mid y_{1:t}).\n",
+        "$$\n",
+        "\n",
+        "Filtering is causal: the state estimate at time $t$ only uses observations up to time $t$. Smoothing answers a different question:\n",
+        "\n",
+        "$$\n",
+        "p(x_t \\mid y_{1:T}).\n",
+        "$$\n",
+        "\n",
+        "That is, after seeing the whole data window, what do we believe about every latent state inside that window? This is often the quantity we want for retrospective analysis: reconstructing trajectories, denoising latent states, checking model fit, and initializing forecasts from the best available terminal state.\n",
+        "\n",
+        "In this notebook we will use the following two-dimensional local-trend model. The latent state is $x_k = (p_k, v_k)$, where $p_k$ is position and $v_k$ is velocity:\n",
+        "\n",
+        "$$\n",
+        "\\begin{aligned}\n",
+        "\\alpha &\\sim \\operatorname{Uniform}(0.5, 0.99), \\\\\n",
+        "x_0 &\\sim \\mathcal{N}\\!\\left(\n",
+        "\\begin{bmatrix}0 \\\\ 0.15\\end{bmatrix},\n",
+        "\\begin{bmatrix}0.2^2 & 0 \\\\ 0 & 0.1^2\\end{bmatrix}\n",
+        "\\right), \\\\\n",
+        "x_{k+1} \\mid x_k, \\alpha &\\sim \\mathcal{N}\\!\\left(\n",
+        "\\begin{bmatrix}1 & 1 \\\\ 0 & \\alpha\\end{bmatrix} x_k,\n",
+        "\\begin{bmatrix}0.05^2 & 0 \\\\ 0 & 0.03^2\\end{bmatrix}\n",
+        "\\right), \\\\\n",
+        "y_k \\mid x_k &\\sim \\mathcal{N}\\!\\left(\\begin{bmatrix}1 & 0\\end{bmatrix} x_k,\\ 0.1^2\\right).\n",
+        "\\end{aligned}\n",
+        "$$\n",
+        "\n",
+        "Only position is observed. Velocity is inferred from the dynamical structure and the whole observation sequence."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## What this tutorial covers\n",
+        "\n",
+        "We will build a small discrete-time local-trend model, generate synthetic data, run a Kalman smoother, use the smoother marginal likelihood inside NUTS, and then forecast forward from the final smoothed state.\n",
+        "\n",
+        "The forecast step is intentionally constrained: in the current release, `Smoother` only supports `predict_times >= max(obs_times)`. Prediction inside the smoothing window is not implemented yet, because inserting extra times into a discrete-time model would change the transition sequence itself."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Imports"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import arviz as az\n",
+        "import jax.numpy as jnp\n",
+        "import jax.random as jr\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpyro\n",
+        "import numpyro.distributions as dist\n",
+        "from numpyro.infer import MCMC, NUTS, Predictive\n",
+        "\n",
+        "import dynestyx as dsx\n",
+        "from dynestyx import DiscreteTimeSimulator, Filter, Smoother\n",
+        "from dynestyx.inference.filter_configs import KFConfig\n",
+        "from dynestyx.inference.smoother_configs import KFSmootherConfig"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Defining the Model\n",
+        "\n",
+        "The code below is a direct translation of the equations above into an `LTI_discrete` model. The unknown parameter `alpha` controls how persistent the velocity component is."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "def local_trend_model(obs_times=None, obs_values=None, predict_times=None):\n",
+        "    alpha = numpyro.sample(\"alpha\", dist.Uniform(0.5, 0.99))\n",
+        "\n",
+        "    dynamics = dsx.LTI_discrete(\n",
+        "        A=jnp.array([[1.0, 1.0], [0.0, alpha]]),\n",
+        "        Q=jnp.diag(jnp.array([0.05**2, 0.03**2])),\n",
+        "        H=jnp.array([[1.0, 0.0]]),\n",
+        "        R=jnp.array([[0.25**2]]),\n",
+        "        initial_mean=jnp.array([0.0, 0.15]),\n",
+        "        initial_cov=jnp.diag(jnp.array([0.2**2, 0.1**2])),\n",
+        "    )\n",
+        "\n",
+        "    return dsx.sample(\n",
+        "        \"f\",\n",
+        "        dynamics,\n",
+        "        obs_times=obs_times,\n",
+        "        obs_values=obs_values,\n",
+        "        predict_times=predict_times,\n",
+        "    )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Generate synthetic data\n",
+        "\n",
+        "As in earlier tutorials, we use a simulator handler to generate data from the exact same model function. The model does not know whether it is being simulated, filtered, or smoothed; the active handler decides how to interpret `dsx.sample`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "((200, 2), (200, 1))"
+            ]
+          },
+          "execution_count": 3,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "alpha_true = jnp.array(0.88)\n",
+        "obs_times = jnp.arange(0.0, 200.0, 1.0)\n",
+        "\n",
+        "data_predictive = Predictive(\n",
+        "    local_trend_model,\n",
+        "    params={\"alpha\": alpha_true},\n",
+        "    num_samples=1,\n",
+        "    exclude_deterministic=False,\n",
+        ")\n",
+        "\n",
+        "with DiscreteTimeSimulator(n_simulations=1):\n",
+        "    synthetic = data_predictive(jr.PRNGKey(0), predict_times=obs_times)\n",
+        "\n",
+        "true_states = synthetic[\"f_states\"][0, 0]\n",
+        "obs_values = synthetic[\"f_observations\"][0, 0]\n",
+        "plot_times = synthetic[\"f_times\"][0, 0]\n",
+        "\n",
+        "true_states.shape, obs_values.shape"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 700x500 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "fig, axes = plt.subplots(2, 1, figsize=(7, 5), sharex=True, constrained_layout=True)\n",
+        "axes[0].plot(plot_times, true_states[:, 0], \"k--\", lw=1, label=\"true state\")\n",
+        "axes[0].scatter(\n",
+        "    obs_times,\n",
+        "    obs_values[:, 0],\n",
+        "    color=\"C3\",\n",
+        "    marker=\"x\",\n",
+        "    s=20,\n",
+        "    label=\"observations\",\n",
+        "    zorder=3,\n",
+        ")\n",
+        "axes[0].set_ylabel(\"$x_0$\")\n",
+        "axes[0].legend(loc=\"upper right\", fontsize=8)\n",
+        "axes[0].grid(True, alpha=0.3)\n",
+        "\n",
+        "axes[1].plot(plot_times, true_states[:, 1], \"k--\", lw=1, label=\"true state\")\n",
+        "axes[1].set_ylabel(\"$x_1$\")\n",
+        "axes[1].set_xlabel(\"time\")\n",
+        "axes[1].legend(loc=\"upper right\", fontsize=8)\n",
+        "axes[1].grid(True, alpha=0.3)\n",
+        "fig.suptitle(\"Synthetic local-trend trajectory\")\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Smoothing at fixed parameters\n",
+        "\n",
+        "First, keep `alpha` fixed at the true value and ask only for smoothed state summaries. Because this model is linear and Gaussian, `KFSmootherConfig(filter_source=\"cd_dynamax\")` gives an exact Kalman/Rauch-Tung-Striebel smoother.\n",
+        "\n",
+        "The `record_smoothed_*` options add deterministic sites to the NumPyro trace. This is the smoothing analogue of recording filtered means and covariances in the filtering tutorial."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "((200, 2), (200, 2))"
+            ]
+          },
+          "execution_count": 5,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "smooth_predictive = Predictive(\n",
+        "    local_trend_model,\n",
+        "    params={\"alpha\": alpha_true},\n",
+        "    num_samples=1,\n",
+        "    exclude_deterministic=False,\n",
+        ")\n",
+        "\n",
+        "with Smoother(\n",
+        "    smoother_config=KFSmootherConfig(\n",
+        "        filter_source=\"cd_dynamax\",\n",
+        "        record_smoothed_states_mean=True,\n",
+        "        record_smoothed_states_cov_diag=True,\n",
+        "    )\n",
+        "):\n",
+        "    smoothed = smooth_predictive(\n",
+        "        jr.PRNGKey(1),\n",
+        "        obs_times=obs_times,\n",
+        "        obs_values=obs_values,\n",
+        "    )\n",
+        "\n",
+        "smoothed_mean = smoothed[\"f_smoothed_states_mean\"][0]\n",
+        "smoothed_var = smoothed[\"f_smoothed_states_cov_diag\"][0]\n",
+        "smoothed_sd = jnp.sqrt(jnp.maximum(smoothed_var, 1e-9))\n",
+        "\n",
+        "smoothed_mean.shape, smoothed_var.shape"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 700x500 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "fig, axes = plt.subplots(2, 1, figsize=(7, 5), sharex=True, constrained_layout=True)\n",
+        "\n",
+        "for i, ax in enumerate(axes):\n",
+        "    ax.fill_between(\n",
+        "        obs_times,\n",
+        "        smoothed_mean[:, i] - 2.0 * smoothed_sd[:, i],\n",
+        "        smoothed_mean[:, i] + 2.0 * smoothed_sd[:, i],\n",
+        "        alpha=0.3,\n",
+        "        label=\"approx. 95% smoothing interval\",\n",
+        "    )\n",
+        "    ax.plot(obs_times, smoothed_mean[:, i], \"C0.-\", markersize=3, label=f\"$x_{i}$ smoothed mean\")\n",
+        "    ax.plot(obs_times, true_states[:, i], \"k--\", lw=1, label=\"true state\")\n",
+        "    if i == 0:\n",
+        "        ax.scatter(\n",
+        "            obs_times,\n",
+        "            obs_values[:, 0],\n",
+        "            color=\"C3\",\n",
+        "            marker=\"x\",\n",
+        "            s=20,\n",
+        "            label=\"observations\",\n",
+        "            zorder=3,\n",
+        "        )\n",
+        "    ax.set_ylabel(f\"$x_{i}$\")\n",
+        "    ax.legend(loc=\"upper right\", fontsize=8)\n",
+        "    ax.grid(True, alpha=0.3)\n",
+        "\n",
+        "axes[-1].set_xlabel(\"time\")\n",
+        "fig.suptitle(\"Kalman smoothing at the true parameter\")\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The velocity is never observed directly, but it is still reconstructed. This is the main practical difference from a raw simulator trace: smoothing uses all observations to infer a latent path, not just to generate one."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Filtering versus smoothing distributions\n",
+        "\n",
+        "Now compare that smoother to the corresponding Kalman filter. The filter computes\n",
+        "\n",
+        "$$\n",
+        "p(x_k \\mid y_0, \\ldots, y_k),\n",
+        "$$\n",
+        "\n",
+        "while the smoother computes\n",
+        "\n",
+        "$$\n",
+        "p(x_k \\mid y_0, \\ldots, y_T).\n",
+        "$$\n",
+        "\n",
+        "Both calculations use the same model, the same observations, and the same fixed value of `alpha`. The only difference is whether the state estimate at time $k$ is allowed to use future observations."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "((200, 2), Array(0., dtype=float32), Array(0., dtype=float32))"
+            ]
+          },
+          "execution_count": 7,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "filter_predictive = Predictive(\n",
+        "    local_trend_model,\n",
+        "    params={\"alpha\": alpha_true},\n",
+        "    num_samples=1,\n",
+        "    exclude_deterministic=False,\n",
+        ")\n",
+        "\n",
+        "with Filter(\n",
+        "    filter_config=KFConfig(\n",
+        "        filter_source=\"cd_dynamax\",\n",
+        "        record_filtered_states_mean=True,\n",
+        "        record_filtered_states_cov_diag=True,\n",
+        "    )\n",
+        "):\n",
+        "    filtered = filter_predictive(\n",
+        "        jr.PRNGKey(11),\n",
+        "        obs_times=obs_times,\n",
+        "        obs_values=obs_values,\n",
+        "    )\n",
+        "\n",
+        "filtered_mean = filtered[\"f_filtered_states_mean\"][0]\n",
+        "filtered_var = filtered[\"f_filtered_states_cov_diag\"][0]\n",
+        "filtered_sd = jnp.sqrt(jnp.maximum(filtered_var, 1e-9))\n",
+        "\n",
+        "terminal_mean_gap = jnp.max(jnp.abs(filtered_mean[-1] - smoothed_mean[-1]))\n",
+        "terminal_sd_gap = jnp.max(jnp.abs(filtered_sd[-1] - smoothed_sd[-1]))\n",
+        "\n",
+        "filtered_mean.shape, terminal_mean_gap, terminal_sd_gap"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1000x500 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "fig, axes = plt.subplots(2, 1, figsize=(10, 5), sharex=True)\n",
+        "\n",
+        "for i, ax in enumerate(axes):\n",
+        "    ax.fill_between(\n",
+        "        obs_times,\n",
+        "        filtered_mean[:, i] - 2.0 * filtered_sd[:, i],\n",
+        "        filtered_mean[:, i] + 2.0 * filtered_sd[:, i],\n",
+        "        color=\"C1\",\n",
+        "        alpha=0.2,\n",
+        "        label=\"filter approx. 95% interval\",\n",
+        "    )\n",
+        "    ax.fill_between(\n",
+        "        obs_times,\n",
+        "        smoothed_mean[:, i] - 2.0 * smoothed_sd[:, i],\n",
+        "        smoothed_mean[:, i] + 2.0 * smoothed_sd[:, i],\n",
+        "        color=\"C0\",\n",
+        "        alpha=0.2,\n",
+        "        label=\"smoother approx. 95% interval\",\n",
+        "    )\n",
+        "    ax.plot(obs_times, filtered_mean[:, i], \"C1.-\", markersize=3, label=\"filtered mean\")\n",
+        "    ax.plot(obs_times, smoothed_mean[:, i], \"C0.-\", markersize=3, label=\"smoothed mean\")\n",
+        "    ax.plot(obs_times, true_states[:, i], \"k--\", lw=1, label=\"true state\")\n",
+        "    if i == 0:\n",
+        "        ax.scatter(\n",
+        "            obs_times,\n",
+        "            obs_values[:, 0],\n",
+        "            color=\"C3\",\n",
+        "            marker=\"x\",\n",
+        "            s=20,\n",
+        "            label=\"observations\",\n",
+        "            zorder=3,\n",
+        "        )\n",
+        "    ax.axvline(float(obs_times[-1]), color=\"gray\", linestyle=\":\", alpha=0.7)\n",
+        "    ax.set_ylabel(f\"$x_{i}$\")\n",
+        "    ax.legend(loc=\"upper right\", fontsize=8)\n",
+        "    ax.grid(True, alpha=0.3)\n",
+        "\n",
+        "axes[-1].set_xlabel(\"time\")\n",
+        "fig.suptitle(\"Filtering and smoothing distributions at fixed alpha\")\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The two distributions agree at the final observation time, because there are no future observations left for the smoother to use there. Earlier in the window, the smoother can shift the mean and usually tightens the marginal uncertainty because it conditions on observations from both sides of each state."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Parameter inference with the smoother marginal likelihood\n",
+        "\n",
+        "`Smoother` also adds the marginal log-likelihood as a NumPyro factor. That means we can use NUTS to infer `alpha` without sampling the full latent trajectory as part of the Markov chain.\n",
+        "\n",
+        "This is the same compositional pattern as the filtering tutorial: data-condition the model inside a handler, then hand the resulting NumPyro program to an inference algorithm."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "sample: 100%|██████████| 200/200 [00:01<00:00, 143.79it/s, 3 steps of size 1.25e+00. acc. prob=0.90]\n"
+          ]
+        }
+      ],
+      "source": [
+        "def smoothed_parameter_model():\n",
+        "    with Smoother(\n",
+        "        smoother_config=KFSmootherConfig(\n",
+        "            filter_source=\"cd_dynamax\",\n",
+        "            record_smoothed_states_mean=True,\n",
+        "        )\n",
+        "    ):\n",
+        "        return local_trend_model(obs_times=obs_times, obs_values=obs_values)\n",
+        "\n",
+        "\n",
+        "nuts = NUTS(smoothed_parameter_model)\n",
+        "mcmc = MCMC(nuts, num_warmup=100, num_samples=100)\n",
+        "mcmc.run(jr.PRNGKey(2))\n",
+        "posterior = mcmc.get_samples()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/plain": [
+              "Array(0.8879194, dtype=float32)"
+            ]
+          },
+          "execution_count": 10,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "az.plot_posterior(posterior[\"alpha\"], hdi_prob=0.95, ref_val=float(alpha_true))\n",
+        "plt.title(\"Smoother-based posterior for alpha\")\n",
+        "plt.show()\n",
+        "\n",
+        "alpha_post_mean = jnp.mean(posterior[\"alpha\"])\n",
+        "alpha_post_mean"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Future prediction from the final smoothed state\n",
+        "\n",
+        "After fitting, a common workflow is to forecast from the end of the data window. Under `Smoother`, this forecast starts from the final smoothed state distribution, not from the prior and not from a filtered-only state.\n",
+        "\n",
+        "Below we recompute the smoothing summary and the rollout using the same posterior-mean value of `alpha`. This matters: the earlier smoothed curve was computed at the true value of `alpha`, while the forecast uses an estimated value. Mixing those two summaries can create an artificial visual jump at the boundary.\n",
+        "\n",
+        "For this release, this is the only prediction mode supported by `Smoother`: every requested prediction time must be at or after the final observation time. Recall, however, that arbitrary (i.e., in-window) `predict_times` are supported by the `Filter(...)` interface."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "future_times = jnp.arange(obs_times[-1], obs_times[-1] + 40.0, 1.0)\n",
+        "\n",
+        "forecast_predictive = Predictive(\n",
+        "    local_trend_model,\n",
+        "    params={\"alpha\": alpha_post_mean},\n",
+        "    num_samples=1,\n",
+        "    exclude_deterministic=False,\n",
+        ")\n",
+        "\n",
+        "n_rollout = 1_000\n",
+        "\n",
+        "with DiscreteTimeSimulator(n_simulations=n_rollout):\n",
+        "    with Smoother(\n",
+        "        smoother_config=KFSmootherConfig(\n",
+        "            filter_source=\"cd_dynamax\",\n",
+        "            record_smoothed_states_mean=True,\n",
+        "            record_smoothed_states_cov_diag=True,\n",
+        "        )\n",
+        "    ):\n",
+        "        forecast = forecast_predictive(\n",
+        "            jr.PRNGKey(3),\n",
+        "            obs_times=obs_times,\n",
+        "            obs_values=obs_values,\n",
+        "            predict_times=future_times,\n",
+        "        )\n",
+        "\n",
+        "forecast_smoothed_mean = forecast[\"f_smoothed_states_mean\"][0]\n",
+        "forecast_smoothed_var = forecast[\"f_smoothed_states_cov_diag\"][0]\n",
+        "forecast_smoothed_sd = jnp.sqrt(jnp.maximum(forecast_smoothed_var, 1e-9))\n",
+        "\n",
+        "forecast_states = forecast[\"f_predicted_states\"][0]\n",
+        "forecast_mean = forecast_states.mean(axis=0)\n",
+        "forecast_lo = jnp.percentile(forecast_states, 5.0, axis=0)\n",
+        "forecast_hi = jnp.percentile(forecast_states, 95.0, axis=0)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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tQyz04pmyZMkSJcDlrzwX5N5Rm9exMpR13Uo7P4QQUhEougkh543UFxbEQlcVaNmFxXIiSaAskXnacsvP2bJv3z6VmEgrTVXaS5WjiAgUy5G8hFnWfa4qJPGbJZKESqjqbYkVWYS0WD8161FpyEu9uPjLINY1SaAmWc9FdGvuqCLgK7OP8n1x/5TjLu/lu7RrJ+uQ46mO61EVVEWbkU4WsTpW1W+rNGQbthmdxTW3vO1KNQJJUijZpUV4adh2FpV1HeV3LG1BPGYsrbPyG9aW1wT2zoFgzyJa2rFUZB3n22Zsz7GWFLEmz6dc87Fjx6p28tlnn6lOIjkHlUX2TSoFSOeDZUddZfbd8vlg2Vkp7VpCYyzPb1nXzfK75/scIYTUX+heTghxGImztWf90GKnyysTUxERL1ZQiWuVrN8aEoso2bHlxU4ThZKZV174LV+YRAiKdVYyE2to4tvei5UjiLVDYtEl3lKzFNur/evMiCVdXD/FKimZwMuy7ti6Bcv1EJdV7XqIABfXWMkQbntOHbGgijVMtiPxyrZItnPLdcq1s3fdZB2SY0AyKNsin5f11CYVaTMSP28vFEJCOCTLc1X9tkpDOjBsY+DFxbk8K69mBbS85jJuWTauvN+g/E5lO5K53hLJsi1t1FHPCkdKhpV3DqTdi+jTEDGpuU9bUlqbrMg6zrfNiGi0HDTLd1WdT0eQ2GtZp2QVl/ATsdCfD7Lv4ir/xRdfmOfJ71iy4Uvogha24AhyTsTyLmEalu1TQjXkGmnPEe26rV+/XglyDanCIJUILDnf5wghpP5CSzchxGEkzlgSeUnJKXEjlRcUKdEkL0iSMM02KU5lkcQ54sos65OXLHmx00qGyXak3I+GuD3LS6TUAJfyOFrJMIlbtaw7LG6QgohNSYol25BkRdpLlCO88cYbquNBElFJMh5xrxQBK4mNxLJkW/u4NhH3XnH3FMQaLPH24v4vL7SSUElKOJWGWMgkjlni9KVGrrzsyvFJmZ1Zs2apz4gVSuKP5RxKx4dcK3npF4uU1NW2J4Qtkesq+yDl4CQZntT2lWsicdqyn3KtZfvatZNtvfrqq8olXjoAxANCSiBJIicpWSRutfI5Ea5ifZVyZ5JUSysPV1s42makDJW4kMtvS45DxIJ0MElZL0kmqNUwri7EdV2SuIngE3dxSeom17C88yf3AREsUoJM2pwkqBLBaC8eWPsNSlIsSQomAlN+i9KGrrrqKvXblGsmbU46zb7//nuVBMvRclyOlAwrC9kXcZGXayD7KPc6aXfi5m+bvEyORa7f7NmzVWeUdEDJNa7IOqrrPlNV59NRy/wNN9ygfrOSXNBSyFYGKcX1wQcfqN+zxIfL/V5+y1LeSzr4yspDYW/fJOeIlAyTfZQEoGL1lnKVEhZl2UEg7V+2I5+TzjzpvJH7p+25kmk5TukQln2R54dcp7qUZ4AQUk2UmducEEIs+PXXX03Dhw9XJY2kfIuUY2nVqpXpqaeeMqWkpFidK7m92JacKq1Uj1aG6KuvvrKa/8UXX5i6dOmiSo8FBgaaBgwYYEpISDjnmqxcudJ0ySWXmHx8fEwBAQGmW2+91bRnzx67JaYiIiJUmR7L0jL29rW0MjJynPLZqKgok4eHhyp/dvXVV5s+/PDDco+nJkuGyXdlkPI9ck6k7NbDDz9s2rBhg93vWJbfKSgoMI0fP16VmmrQoIEqjyTj77333jnf++eff0zXXnut+XMXXnihVakhOX8yvzTkvHXr1k1dO1lHp06dVCm6pKQk82eknJaUQJPlsp+WJXyysrJUaSxph9Ieg4KCTH369DHNnDnTbim7ypQMk3PnaFk2e23JkTazY8cOdc67du2q2rper1dlo+655x7T1q1bHSoZ5ujvSmtbci40pKTSxIkT1fnz9fU1XX/99aqMlCMlw+S3ds0116h7gnxf2plWdsryPEuZOblXSGkzaZeW7Vuu45gxY0zh4eHqHLVu3Vodj2X5udLOb1WVDNPKIkqpQmlLbdu2VaWr7JX7krJxl19+uWq3sszyHDm6jrKOxZE2Uxbnez5tr7ttOS5LvvzyS7XskUceMTlKab8r7diHDRum2pKcQ7kn2Pu9llcyzLJEmDyz5DyEhISYHnvssXPKHAqzZs1Szwd53sjzZPPmzeeUDBOk1GL79u3Vb5TlwwghjuIm/1WXoCeEEEIIIXUXsaD369dPhSdI8kVCCCHnQtFNCCGEEEIqhYR3SCjEoUOHmGiMEEJKgTHdhBBCCCGkQixbtkwljPv5559VDgZm9iaEkNKhpZsQQgghhFQIEdmSZPG+++5TicX0etpxCCGkNHiHJIQQQgghFYIpgQghxHFYp5sQQgghhBBCCKkmKLoJIYQQQgghhJBqgqKbEEIIIYQQQgipJii6CSGEEEIIIYSQaoKimxBCCCGEEEIIqSYougkhhBBCCCGEkGqCopsQQgghhBBCCKkmKLoJIYQQQgghhJBqgqKbEEIIIYQQQgipJii6CSGEEEIIIYSQaoKimxBCCCGEEEIIqSYougkhhBBCCCGEkGqCopsQQgghhBBCCKkmKLoJIYQQQgghhJBqgqKbEEIIIYQQQgipJii6CSGEEEIIIYSQaoKimxBCCCGEEEIIqSYougkhhBBCCCGEkGpCX10rrisUFxcjKSkJDRo0gJubW23vDiGEEEIIIYQQJ8BkMiErKwvh4eFwdy/dnk3RXQ4iuKOioqr6+hBCCCGEEEIIqQPEx8cjMjKy1OUU3eUgFm7tRAYEBMCZLfJpaWkIDg4us5eFEGeBbZa4GmyzxNVgmyWuBtsscTUyMzOVgVbTjKVB0V0Omku5CG5nF935+flqHym6iSvANktcDbZZ4mqwzRJXcM1NSUlR4yEhIWqa77PEFSkvDJkmUUIIIYQQQkiNYzQasXr1ajXIOCF1FYpuQuoopsJCFKWmWs2TaZlPCCGEEOIM1sFGjRqpgQmLSV2G7uWE1EFEWCeMHoOCgwcRs2ghPMLDUZSUhNghQ+HVujUi35wDN0/P2t5NQgghhNRjdDodbrzxRquQiKpCXNUNBgMt6KRK8PDwUO21slB0E1IHMZw+rQR3UXy8Etrh095A0sRJalpb7tG0aW3vJiGEEEJIlVNYWIjk5GTk5uby7JIqQTwxJDu5v79/pb5P0U1IHUQEtVi4RXAr4f3AgJL5UVEllm8KbkIIIYTUQcRafvToUWWVlNrJnp6edF0n5+01IVWiEhIS0Lp160pZvCm6CamjiEu5WLg1wS3ItMwnhBBCCHGWRGrCVVddVSXiWKzcIryljJOvr28V7CUhUGWZjx07hqKiokqJbiZSIy4PE4bZR2K4xaXcEuVinpRUI9eFEEIIIcQRC6IMMl6VsIQuqUrOt0OIlm7i0jBhmH0kS7nmWi4u5ZYx3TI/ZukSupgTQgghpFYRYXzJJZeYx6uTlXtK6oFXNde0D3Hoc7/99huee+45ZYkXC/wHH3yAiy66SC1LTU3F4MGDcfjwYXh5eeG9997D5ZdfrpY9+uijWLt2rbK0/u9//0PDhg1VB8VNN92Ed955By1btjzvYxg6dCg6d+6M0aNHY8qUKTh9+jTefPPNSq9vzZo1qt76DTfcYHf5Cy+8gLZt22LAgLPemPbYvn079u3bh/79+6OmBXZ6errKql9V0NJN6lTCsNytW81iU+bL8vqIvlEjlaVci+H27dq1JJY7KkrNl+WEEEIIIbWJCO3o6Gg11GXLtAg4EZiLFi3Cjh07MGPGDCvBOWnSJPTq1QsHDx7EggUL8MADDyg35l27dql5O3fuxJVXXonFixerz3/88cfKHd9RwS1Z3GsSEd3SyVAaL7/8crmCWxPdy5Ytq9Q+1PQxl0fdbd02vPvuu2jWrBm8vb3Rs2dPbNy4sbZ3iVRhwjARk1rCMM26W58Thkk5MCkLpizaZ2K45a9Ms1wYIYQQQkjNIRbsJk2aoEOHDmr6sssuQ1xcHLZu3aqmv/zyS4wYMUKNd+/eXSWA+/PPP1WZqoKCAhWjnpOTo5LCSVb2zz//HGPHji1zmyLSR44cid69e+O6665T8fPjx49Hx44d1fDUU08pq3tZlPUdsY5bWsOffvppZSUXoTxv3jwsXbpUWc9FYNti+V35zn333Ydbb70V7du3R9++fXHq1Cll/ReLuMT8y3q087Np0yb1mYsvvhhdunTBV199peZLvLVYpidOnIiuXbti+vTp6pwfP37cvF3Z1pgxY8z7K+da1i1eBfv370d1Ui9E9xdffKEa5osvvqgat7hyXH/99epikrqTMMwSJgwrEd62nQ4yzfrchBBCCKnrMd3OhGS8PnnypHITF3744QdkZWUpoSjzxaodGhpq/rwYCkWUiwu2WLRFRB45cgQDBw5UolEs5Xp9+VHCBw4cwF9//YVVq1bhww8/VIJ1y5YtShhLR8CcOXPK/H5lvqMJZLFky3dEOJfHhg0bsHDhQuzZswdNmzZVrvfyVwS7HL8m5MXt/ZFHHlGCfvPmzfj9998xbtw4JCYmqvVkZGSojg3Re8888wzuuusuLFmyRC2T9iWeBsOHD1fTIs7l2GTdjz/+OEaNGoXqpF6I7tmzZ+Phhx/GsGHDVA+KXDSJpZg/f35t7xqpApgwrGZgwjpCCCGEVCViSV25cqUaZLyuInHYX3/9NSZPnoxu3bphxYoVSpM4IpxfffVVJQzFoisiU7KyiygXXSOiUoyLpSEiXazlgpxjsTBLzLhsV7SRrK8sKvOdynDDDTcoq7QglnkR9/aQTgvpfLjxxhuVuL/mmmvUfM1KLccqx6wh50jc9TWXd9lGp06d1LQch2xLLPgi7uUcVyd1PpGauEBI74w0cg2JGZGLtG7dulrdN3L+1IeEYSJ2JTbd8jjkuCUuu6as1hVNWOcM+0wIIYQQ58ff3x/1AbHYyiCIy7hYtkV4ixAUQStu0Jq1WyzgEuduSWZmJmbOnInly5dj6tSpuOKKK5TAFA/e2267DT4+PhU6t5XJxm35Hdlny44SSZxW2Wvp7e1tHpdyXKXFY4u1WizZmseAJXLOxKhqmRtARLW45ktYsVjSRYQL4kXw5JNPKku3xMVLnL2WuK66qPOi+8SJE6pBhIRYZxaUacmGZ4v8CGSwbOCCXDAZnBXZN2mIpe2jiCBjRgb0wcHmeYa0NOgaNnRpEeQeEACvVq3UeNSC+UoMyt/4YcPVfFnuzNetPOS6JY0Zi4JDh8zHJ2JXO77wObNr5PoZ0tOtEtaFTn0dxyc/o6aFovR0c9tydJ/La7OEOBtss8TVYJslzo4IpJtvvrlK26y2Dm3QMKF63NcddYuXWOywsDA1LpZViUsWwSffv+eee/D++++rmGMRguIuLSLQct3iDv38888rcZ2dnW2eL67pol0shavlvmnruPrqq/Hpp5/i/vvvV+ddkrFde+215uWW50wbyvqO7LuIWRkXF/lffvkFgwYNUtMNGjRAbGxsmefGZGd7ludTW4+4jGvzREQfPXpUWak1K7dYqKXzwvb7GmKpf/vtt/Hrr78q13hZLm7qYhWXTg5pL7Lc9rvntJ8z07aa0NG2WudFd0WRnqOXXnrpnPkSayI9OM6KXHCtUdpmfzQVFSFnyhQYjx5Dgzmz4R4SguKUFGSNGQtd82bwmzIFbmdcT1wR/TOTocvMRLq46Eicvl4PX7G8BgQgrZqzl8u5NWVmwv2MS4xQfPKk2nZVnFNZV+7+/ShOSsKxwUPgN3kScqa+oaalMynl8GGrbVcnvjNnqDYjQjt+4CA1zz08XM0/JTelMzkSHN3nstosIc4I2yxxNdhmSX1ssyJCZT1iLbW0mFaX+7qjWbJFMP/zzz9qPySps4S7at8VF3KxwrZp00aJQbHKilVZWy6W3dzcXGUpl3lSRkwE7rRp01Smcz8/v3P2Q86hbEubL7HMhw4dUvHhgljKxdoryzUhqY3Ld2W8rO/IMinldcEFF6BFixbo0aOHeR2SFE0yrYsLeL9+/VSpNEtK257tMtmeWPcvvPBCJbglMfb333+vOiAklluutXgEiOu+9n3b8yAdBtJBcMcddygRL8tln++9917lWh4YGKg8BWy/a9t+tH2VDgbNZV+Q2HxHcDPV5awFZ9zLxdVALoZcdI0hQ4aoXg65cOVZuiV2QlL9BwQEwFmRRpCalIRADw94Wlj1xZotlsf44Q+aXbAtrZQyHb1ksZUFnDiXFVpbp2ZVFuS6adusSaQkmya4hagli1U5ssrss7RZ6cySupMU3cQVYJslrgbbLKmPbVaMZOJq3Lx5c7vWX0Iq267Eyq5Vw7LUio0bN1adRWVpxTpv6ZbU+pKw4I8//jCLbvlBy7T01NgiiQJksEV++M4sDEQA5r70EnLj4q1ibuPOxNxGf/wR4h562MpKaS6rZeN6T+yfX9sY5bwjR0tcrhMSlMC0jCcXijMzqySe3CsyUq1byqFpyLTMr+z+y3T+ocPwbtXS3DFQXsy1tCfprLFEprX2Vpl9ll5cZ/9tEWIJ2yxxNdhmiTMjlti///7bXEZL3gfOt81q69AGQqoCrT3Ztk1H22m9eNOVcmEfffSRShO/d+9ePPbYY6rWnRZMXxeQeG1xH9dibsUiqSUYE2Ho5uNTp8pq1WQmbS2JWOyAgUp4qm0lJSFx1Ch4REfDIzKyWmuEl5edvbzjtt1/5fkw4jEcu+cexI14rORcSlK0AQPV5+ytyzZhXcxnS8/WRpf5tteinH0mhBBCCBGHW4l1lqGOO9+Sek69EN1ScF3iAaROnMQWSMD9b7/9dk5yNVdG3MMlXtsshGwEIAyGOiOCShPBZYnG8xHrYiG2TCJm2aEhQ9PJk6qtM8NS7OojI+HTraukdTy7Lzt3lnvctvuf9fffyNmwQbqXkbthAzJXr8axgYPMHTTyedtzIhZw8ZjQ2pO4lKuOhagoNV/n52c+r1YCPTISv19xOYyBgVYCvbo6SAghpL5i7/mmQsyKimptnwgpD7ESSnyzDPR6I3WZeiG6BXEllyx6Eq8tBdjlx13XkARpEq9tibJu6/UVslI6O2WJYE00VqVYF4u1JjBtOzTCp09D6hvTqq0zw1LsRs6ZDUNqmhLLIrylo0Viyss77qLG/kifNgb6yAj12cQnnjSvQ/4mjRoNQ1IS9GFh5g4a23MiLudSFkyVYDvToSB/ZTpixnQkjp9gPq+yz4WBjWES967QUAT+/Q9SUlJUoruisDCY8vLUZ2Xf+TJICCHnT2nPt7iBg1QiVVfo5KysB1tNer6RqkeEtiThkoGim9Rl6o3org9IRnLbmFsRgCJyyrJSikhyJcoSwTJfjkd7AGsPYxmkx98s1gcPcVisq22Gh5/jnt900kQkTZhYYoUOD0e05T5VUWeGpdj16dTJfNwilvO2blXx5Lbu7LlFudiYuBF70/binY3vIGxWGHr8eBOGddpute64oddZTZuMBuzYuQrHBg+2e05kX2xd5mXamJNj1Qmy/5dfkLR1G9wk+2RSEnqGhiLcw0MlNXz0559waNCZ9R86pDK/C3xBIoSQ6umMltAzCUFztU6DwthYHOt/v1UHsDwrinNyrJ7xslw+J5+viOcbIYTUJHU+e/n5IhnpGjZsWG5GutqmMCUFR+9/QIkcZYG1SOqlMpQvXKBKWFmKpvISZzk76qXCIlGXWPB9OnZUD1p5+ZDkcSnTpiN/71613PuCCxA4bCjihg4rsfSewSxay3AJVw/xMy8wGrrQUHi3aIGCo0fN6w+ZOEElrJPODBHL1XFuc7ZsQdyAgVbHjU7tsOHov0hJOoQR6ycjo6DkBSsoT4cMTyOC8vVYsCoa0dln96fIzQQPk/0EI4bQIGTMGId2HS6Fu5s7TuWewobEDWjg1QAp2SlK0Ec1jFLTcgvJijuKvm+ugVdKunkdbuFhCF/4EfafOIBGE2bBkJB4dgM6HaI//RRZEeFobDCUZHyvxnNGSJVViUhNRdOmTWmRIbWWxLO0Z7e955Q836SsY2inTjXWZiuyz5bLleA+884S9tqriH/kUZjy85UXVrMvlikvLClDKfPcvb0R8+ki5cl37L7+MCQnw83bG1EfzEPyc8+f7YgX76wqyq9CqgetZrLQqFEjNX2+91ktyzSzl5OqpLR25ahWrPPZy+sDmxI3YemWRegX5IFw9wizgJS/8gBWVu7g4HMedq78ICotUVfE7Flne/uHDVcPacOZHvE8caWW79jUaQx+9ZWyBbdlXLV87sw6jcePq3W6ubvDmJKCAr1eJayTh3x5nRmVeSkRVn6+DB6vvALLK7f9ieEYcsUhjN7UCK0yvODbNwcZfkBYjh5L17TCMf8CRGS4ISrHE/F+RXijawre+idCCW4R3qMuTcALm0MRmne25uCQjluw7Zc7gF9K3RV4GIGGhTqc8Ck5n106+WBpSox5+QPt1+O/hW1QbCrGVZ3D8G5Cw7NfNhqRPGkSvCeMR/z0GeaXRNtzQggh9RXNiivPNMuqJNpzXUJ7xNNIu2fKcvHAUiFEZ5CQs+xqyF9T2jNM8ntIuFFp+1xax6rmwaY9a+MGD7FaLl5d8vw2JCSoTlvJeC2ftfRAEzEuFVvU+qo4oSmpPuRaSp4l4Z577mGHJqmz0L28DrBw+0K8te1dXNduOS7v/jce3/wiFmxbgKLgRkoA1jXrYVmZtBPHjkP47FlqWuKUNcEtGMXFPCkJ1pIbWH//A/jg1VfNvVWWddot46p1ERE4/tSTOPbYCBSdcckvTktTgtvyAS9DeYLbYTe6ggLs2rVL1aw8sH8DPGa+gqbFxYj3L8SAa2IR51+IxukFeG9FCNqmeylL9sI/otElzQeLVzdHaCZwiVtzhF3QVcV09/rfcrzx7HL49+qlXlx2RQCHAwrgJvHdFkxdH6ZEuwhrsZZbItM+hcCcfyPw1erW6nMyzFgbfs46QrLd1bKJa3yslhW7u6nrlfXUyFJDAyyvN10ECSH1jbJcxvMPHEDCyFFWz5HcbduQOHKU1Tok5ExCz6qSsp5h8SNHoeDAAbWPkqBTknaeTxiXvmlTZcW2CiNbstgqvEyW62zEtStXZ6mP+Pj4qKGuM2XKFGUtrS0WLlyIffv2OfTZNWvWmDtDSNVA9/I64F4+d8NcjPrN+kEr+Oh9sKjfIkQ3jEankE7w9fCFM1BZK295vf8Si5wTGYS8F57AV3On4uHfSty+7RHvX4RJvZIwbX0EIrP1yG7iD+O7L2PZD8sx7/15aNSiERoZGsHX1xfdb+yOpwePxr6Nm3DbI0OABkCXVB8sDTtr1RXhP+CVV1T9vjZt2qjh8j59EBMYeM5xoqgIsUOHlelGV5CTg3333Ycd+ekYoTuEqDtbIL7oiBK6Ys0e1jcOyX4GJWi//PsC7GuQg5c7xWHhXy0QlnW2L80sZoOC7NbpLvLzRPzQYXBPToMuLAxZTw+B/5xPUZyQhDi/QpwI1OPi4ggM7RuP9YZD6O7WDG/85IXDvjlolumFyFwPpPibAKMBIWcs5cd9iuCu16NplhuO+xfDWGxARK6n6iCY3CtZiXFLN3fb0ID8ffsQs2A+PGNiHLKQEFJT0L2c1DSluYyLV5d0MmvPEWXhFsF9JklmxNy3VJJPWe4eHo7mn38GzyqyeJfnCi7bln0RMVxeGJfl+4DdMC7JG2LRESvPCslLYxtepsS5xeccCRsjdfc+66zu5fKOmJ6ertzobTEYDNDrq9cB+corr8To0aPRr18/hzoIxO3/zTffrNZ9qk/u5RTddUB0S+KsVrNaIbkgGYFegThVcOqcz4T4hWDX47sQ5BsEZ3aXc1RY2Qr3v2L/wgPzbkSqR67d+GVLxKV68NWx+C84X4lW+eyhhgUYc0kiiqyNunbRvmO5/vRAL7zQAziV4I6TW08iOSEOc6IjcEVYc7T94otzjlOL/bZ8uRB0YaH47prm6PL534g06JVQHXhNrNmFW3PpdmsSiLTcNLQObI3Nd/0OXaNG2J2+H60SDUgZ/OA5LyiV6cDIC20Mn+PpKhZbrOSZ44ciYMZCNV0cFoxvmjdDzzX/ItrieulCQ5AxeyLaBrVB6oOPQde8GbIKs+CVkIqhV8ViveEwuqcH4OPlYVbx5DkNfWF6bTwavvYBjMnHy43NO9+OG0IqA0U3cZb8JXJftydSRXCLJdi3SxfzckRHofm770J35iWxKu6fdrct4ldE96yZ6rliTyyX9vyx7ESQY/C56CK1DcPx41bf0aqGaElM7W3bMp8NY7pdj7oqukeMGIEPPvgAHTt2hE6nw4oVKzBhwgR1jIcOHVLHLFZoW2EeFBSEzZs3o1mzZjh48KASzfJZ8cp85JFHVHUmW3788Uc8++yzat0i5l977TXlMSnfDQ4OVrrm9ddfR1RUFB577DHk5uaqc/bAAw/gueeeU6WVb7jhBuX6HxERgTvvvFOVXV6+fDleeeUV5OXlqWOYNm0arrrqKtQX8im6qxdXEN3CtNnT8OK7L8KQZoDvU77I8syCv6c/sguzzZ+R6YGdBuKq5lfhlja31Irl27aH3DbhW0UfkMsPLcdzq5/D5qTNZtfnJStjlCBO8jNAV2yyssDC3R2hOTokNjDi/quPILeBF3yzClSyMaGpwReJXrlmcSvIMhHjYQXeykq9YHXJ+m0tt5pAzvLTo2m+Bz5a3lTNLwptgg+beePxYwa4HU9RAvb07AkIzXRD9kMjzcdy2t8djbKLzdOyPs2iPazzMKw5tgZHTx9VQnvtg2txJP0IOjbtaL6OpVlEyuvtL+sFzHDiRKnrlBec2Q8/jJv++dfui5W2DkHV/m7sj917/0GjsdOVcE/0KYQebtYWcjcdmuZaP2Rtj6GqOm7qGuyIqH4ouklNU9593VaQR7z7DgKuvtoqyerJwkKERESoF/Cy7p+mqCi0fP89uHt5OXSPKc/abIl4UunfewNbTXFo6tcUmYmx2J+yG1fOXg3ftExkBvogM0CP8LhsuBebkBPoA193H7idOKU6YXNeG42AOYtLEnKeKXdZVsK1+v48cGWqU3QnJyerwZLGjRurz8l39uzZc866up55p9m/fz9ycnKslokQDgwMdHi/bAX10KFDsW3bNvzzzz9o0KCB3c9oolsEspQ7XrJkCdq1a6eEcq9evfDJJ5+ge/fuVtu56KKLMG/ePPTu3VudT9Eysj5bS3dWVhY8PT3h5eWlhHSfPn3w/vvvq/XaWrqPHDmCAQMGKOEtekg6Ci677DIcO3ZMfb8+kH+eopsx3XWE8aPHY/vP2zHtxWlo/EVjeC7yxJd9vkSLxi3Ucr27XgnweVvm4b6v70P4rHAcO32syrbvaJ3M8sp9OSq4j2cfx71f3Ysblt5QIrhNgFuhmxLIyUEeSthe/OWPCOzSQ1mPxQLbpEsPdP3yR7WtVt364ucRfyF2TCwaR5Scow83tsUfG3vhUvfWyo3765Wt8NXKVmr8MvfWWL2xD6buaKVioJMDivHs7UZsC87DmBszlUAWa7lsv6C4APGe2Uowy3yP4yfxxPpEJbgT/A24+uK1uH3JzdgzeoTVMeUbrWPJX7ssUwluEdnv3PSO8lTY8NAGbB+xXXks9IjoAR+T3lwSzTLZW0XKl5VWCkzNtxNjp8XKievg7bFxpdYo19ahrV86By5udyW827RV1+eFe3R44NpYdYyCJHITwZ3qbbC7PeGc0m8VqNNel6lsDXpCiGvmL1H3vp07z0koqlzKz9wDBL0kUfXwKDVOPO3vv7GjXz81fWTNGnRp3RqjRpWEq8nL+rp165Qg2LJhg4rX1u4xMiSOe9pq27bFcFK8izC4byxydcUwJifjyID78din/dH/3avhMXIKvN9Zgjt6bFXPyYBTeYg8lqUEt0zf2WcnVnvF4rifEbffcAQXbx6KIX3joIsIh65xY3hERqp3Br8ePdDi++/Uc08qiEhHr0okWwfz2dRVxJoqbUwGGa9OxNLcrVs3q+H5559XyxISEs5ZJoOGCGTbZb/8Uka2WQeR5HGa4C4LEf27d+9G//790blzZyWQRTTb6yi4+uqr1e94+vTp2LFjh113dkGE9kMPPYROnTopoR0bG6us3PaQ+G4R2pdffrna/t133606ReLirN8DSekwe3kdQn5UY8aMwRNPPIE5c+bg6t5XY2efndiZshMdmnZQydVG/lZiWZWSUu3faY+hXYZiypVTVM+zvZ5sLfbXu1VL88PL1g3NXs+5JFWR7OHe7dqZH3za9zQhZ9lD7mjSE+k4eO2v1/D+5vfNZbEUbsDHfT9Gx3Yd0WFSW3hmF6hj6DRvoVmEafvseya7eLMz+y8iVllg15ZYYD9ZFYGiwny4Z5d8L9grCteu8oExOQl9IiNxeuZYtA3tgL+Cg7ErdZfq2LjZrw+25SaiWXBrLLh9Ae744g4kIw0v9DmBhSvOHtfEXonIQyGWrLK2lk9fG6bini35aOeFyJz+NDp0usJszRahrWF53qU8mvTqmwwGefPBqUWfqnla+bLK1mIvK0u8ZTyhpceCvMiV5rGg1R2Xa7KioS/+3v832t7micyHzrpHmdysX9wOP/EE2n3zjRrXrBfasWkdN/UlW21pv099wwDzi7QkLwqf+rrZNV9gVnhCXO93riXxFCRBqE/btuYM3x7R0UgaM1Zl9a7IPdg2S/iJhx+BPF1OeXoic9RI9JUkaEVF6rOFhYXqxV4I0unwWbPmiNTrcfj+B6AzmcxW7eO6IkAHhKalWW3L4G5Cnr4Yp72M8M11R2CBHs0zPfHKxrN5PfI8THilQwY+2hBs/p48E+MbGFTIl2WFjHVFB/HRQ5fjxk534uLADuZ3Bsn/0WzZ51bvJQxFch2ksyb+zLNKhJ9YequLRx99FLfddts5lm4hMjISW7ZsKTMJmT1L9/ni7+9vNS1u25adD1riNTlPYlUvTRRbMnv2bCXQV69ejSFDhigLtbiy2/LMM88oS7pY2yWeXNzIS0v0Jtu/9tpr8dlnn1XiKIlA0V0HEZeHyZMnq/GdW3bi8UceV246OYU58O7mjXy/fHi6eyLPmKfE6ycbP8AznZ7C4CueQNbEF+ETl4qIOXNUDeqEp0aq7KO+PXsiet77ZndjS7ct255zS3evfHkwi+i1cPeSeGZ7Qq4sN2j5sX+5+0sM/m4wCo0lVrsLm16IEzknkJSThKa6puh/ef+zLvO+ZVtxLV9sfJs2RfcLr0PRpx2VYJEM55YuIO4nT8OI03ZdtTURvHrcdiXAOzQuEfzHRh/D7p1/ImDCTBTjrNXhjd/D8ORNacoqrocOI3qnINuvEO5ntmjrRtd44hx4LO0GND03FMDyvIsAlfOeNGmy2v+KlC9z1Mpi+VKXMGYsvGJKEsnZK1FXlsjXromuuBgXmcKR+9J4q+Xibi6u5m5nxvXpp7H3rn7Q673glnaiZB0+PpXuuHFVbDu3JDle/IjHzv4+P/5IJeiT68+yOfUPhhe4Fo6W3JLnbP7+/aqTU3vuyn1d+xzc3Cp8D9538iTmGY2wjATtsnABLunaFfdYeKmJy6hUzxARIG6mm375BT7ffYcmKSnQ/JESfQsx+tJEvPVPJFAI5OmK8cRl8Xh5Yxgicz0xa20E3rjBiLlrw+FzPA2LVpU8NxL9jcobrHFhAKZtjxCJb96XGRuiMOiqI/AOj8Kw0GGYEzsH6fnpatnMA5+ooYlPE+x/cj+a+DZR8+11MDAUyTUQa6lmUa7uWvJhYWFqKO3dWXMlt0fbtm3Pe/ti0RYX5NIsz0KrVq2wYcMG3HTTTfj222/NQl+2L67LCxYswLBhw9Q8sTyLELd1cZfY8A4dOqhBxLTEjwvyfdm+hrixXyAeInq9sqT//vvvypKtfVYs3xrXX389XnrpJWU5v/DCC9W8jRs3okePs8YgUjZ0L3dxynPrlhuY9MRJKYbIkEjclXYXhhYNxfJBy9VyiV2e/U8Yer3xI2575UIk79igxF5s//vVC7y80EvsVO6GDcj6+2+7bry2LuNSX1MEt3l/EhLOljnZt09ZwEtzl5N9l2QPy5YtU71pRcYifLHrC0S8HoH+3/Q3C25haPBQHBx1ED/3+xlHJhypUIy6PXdcNV8sxaVQlqiTbXcP7oxTTz+j1um277ASzJIFXOLPDB1bK5fqKDdPLFzVHF2nvoeMpyaizdFLMLLFs2jRo69yjxM3uW43D0GzTz9V56Wslyd7510EV0XKl5WFZmXR1iex2tr2vNu0QeTct0qsKWfOSUVd+sRNPEssNWfaQvSni1Sng+Cm0+OJyxKQ5FticfFIz1KCO85QiOEJ8fhr1Sq7HTdyLR0NdXBWStt/W7d6+T1a/j7zDx2CycY1r653RJASGF5Qd66XZckt+Z3n7dpl9irSnrtyX3f38zMLcEfvwWK5fvfdd3Frr1642+K5J4iruOY2roWlSA6T9u3bwxhoxPfZ3+PnNhsx9grrsLQJfZJxoFEB9jXOR2FQQ4zub8L6sDy8eLcO7pHhCLuwJ757bjtiZltnQO763kJ8P/Bn/LyjKwILCtXzMefNZ5Gmc0d4ljs+/aE5sibH4YX7X8D3V3yPP4f+iQtOXWD+/sm8kwh6KQhdxnVBZn5mhUuu1ddQJGdE3lO1qi/VLbprm3HjxilrsbhnS9y6PcRTVVzDpQNALNBNmpR0LIkw/umnn5QQF9ErgvrBBx9ULuL2LNiyvEuXLli8eLGKzxYk8ZokUJPti2u8JE0TES/rmzRpEvr27Wtexx133KGs6vLZl19+WXUGyHu5eAtIzLiIdWY2rxjMXu7CidQse3GjFsxHul6PxgYD4ocNLzeBiJbxvOh0KpasiEH0mZJOMzunYta/EVaZpbWkJeUl57JNqlLcqBHcLR5qSlh9/BFSpk1TNUZ9Z85UiU8CjUYcGzwEJ/38MNVQhJ/++A3FrYuBKKDpVU2RmnvmxmSSTgIPFOmLlGX77wF/o03zNlWW0E1eOixLnNhSXlIyy3XqIyPhEdIUedv/Myd8EffAJAtrheW1sbR6aOOCZqUuK7NsaZltnd16ZpSEFE88AcTFnxOW4NGmNVInDUb8htVo/+IS83eGXZuMopOBeH2zG6LdPVXt9MgZ080WeDnvYoEvjIurcJI1Z7AUlmeZsZv13ub3aQnL5tSPRGpVnaCSlE6VZP4u53ppz4qKJsUsq81KNmNxL9VnZeHHTheioWQfDgstCaU6WfK8KWzsDze9Hh5pp5Ed5I+pD/hjbc5epOSmlFq5IymgGIOuPIJG4c2x/t7fVQy5eH1Jkk+P9OxSE3LKsz9m4QIcf+llK2+6/JQU7LvnHqT5+CLzkYfRpUcPlbRIfm9H4o/gymVXIj43Hm5wg0leCgD4efjh0MhDCPUPPfdcVzLBKKk96mr2cuL6MHt5PRbdtg9u7wnjkT99hll8RM6dC5/2Z3uG7WWT/mHDD9iy8Dfc8Oe/CNfZjzZ4Kfo4XowLtRJ1D82ciZiYGNVjJz1kq7/6Cl2+/Z/Vg+14URFCLRK4nBg7Bnc//zyy0tPhV1yME+5GRF0chX1/7kP8wR3o9dRVCIlsgIMtTqIYxeZs5Fr2cEF6u7313lZZuyt9/kopeSJ1QaXLQYtXU9N6/VkrchkvsOU94Mt7OauoO5wrv1DIgzUlMRFNPD2t6sdaZk6X0mUqW+0ZpGPo4SvjMHFbCFqd9oLp1ZcQeGEbtDM2USXKJM6xKDbWbpxjWdfOWdwQHRFPynPEopMl5MUXkPLSy1YZhCPenEPRVY9Et6vfC1yFqrxPVDQreVmdqdKJvjFxI3z0PkjMTMSPf/+InUd3Im1/Gq696lqEtA5Bfno+du7ficCYBhjwUzIaJWVh4FWHlTfZ4pUxCM/1OKdyxglvgzmm2rIyyPEAE8b1iMPMjdEIy3RTJSSjli5GQHhM2Z3R0g4NBqtnq3QaSpI1y/uzveek1tkh7y0i6jMLMnH/hzeY3w8aFjfE1lFb0SKwRY12TJPzR8IHs7OzzfHNMk3RTZwRiu56LLpLe3Ara190NArjz1oQtc95tmypBGXB4cNWy47cex+KT5ywvw03k5XlOzvID9M7+WP1z7twMv4kgrx1+Cy6GSLdPMz1nP2nfozi4yW94xqm0BC83zMMptAmyPDMwLenvkVRUS4aFepx2tOgsoS3yvDC2D6JONioQNXb/mFdZ+zyy8QjPQ6oJGWS9KwqS53ZK3kSs/hTpEybjvy9e9U8yYiqWRgdebE6nwd8RSxWrm7dKkvAWHsNlLSpBjMWwpiQqF4Ih/aNhcEd6mVQQiSCMrwxpuEYDBnxJCQlihabXxkvhdo+l2W9jAv26vJaWrpV2ZylJd4BmhiImDEdxpwc1jWvw6JboLioXs7nPmHPQp75xx9IfOJJh+pvq1KT08eZE2smZSVhyY4l2Ju2F0t3LkVRcUkojiNoJTG1BGVd0nywdOVZwfzc3e740X2P+Zk8+bYi7DLEY+HatuiSrIdX94txYsojaOceojo7y3ou2nZUCMcGDLTyKivv/myvsyMz7jA2330z9jbIxthLklGoK4aPyQdv3vomBl448LxLaZKaQ+pIf/XVV+ZM3nJvpegmzghLhtVz7JV0Cpk8SQluu3FMBw4o127LZfIAtBXcBjcTnuuepP6K4Bbh/fhl8Urw+J/IwUOb4uH2wGk0fLUhsse74UBkPuL9i3Bt93W4YfVQJGQmqPVIUpUhfWPV96Rk1i1/bMQXKfOw5MQSJbjlob54ZTTanPZSD3fpRV/2ezPMXxWNpatbwi8tG5cYovDv3cuV4BZ3taqKzbWXmRuSgMzDQ71ASL1PGWRcsqM6Eq9cWrZvy7jxsqhISbWyYq4rm7HcWeKhLY9N4tu7XT8QzT/9VL14Skm4k95GZPnr1cujtKEF/4ZjztEZiLgzBguXLjonNt9ebLPlsWrnXSwxpZ33mjoPpZVpk7ZpmdhO6vGaBbdOh/C33lT7Ly+z8jn5vLRZEdyScMnRcmLO0gZIxajIvYfXuHKUdn+W353l/dn292Ivhjt32zYkjhx1zvXK27nzbPnHyAikzRiD/KaNlMfPqUeeRKsXmyDg9QBEzI7AxJUTsfC/hVaC2z3j3M4g1TGZd8ZdTPZPBzzW9TFE+4Yrl/GZG6OsPv/6+lBcXdza/Ez+/K/WWHfJYlxsilD3m+LEJHTxbYWA6JblPhe1ihVa7LkMEbNmVij3hL3YbBH7YmW/srgFdg1Yh1CEIs8tD4/+9CguePcCZf0vr+RaWaU0Sc3i4eGhBkLqMs7XVU/O+0VL6nSGT59mV7g1W7JYDZbLtB5nXWgofLp1VS/wepMbXo6/EH5iodXpsCsC+DcsB6NuOG2uSZ2jM8IjKxv5upLSHgOuOYYcU8l8SaoiWU373XAEm0LyzDWrtVrWvSJ6IcatifmhPnd9MxVPLiJftt3thC9Cs9zUtiNmzFD1nSU5maVQOB8hUNrDWKyjMl+zSFgmIisvKVl5D/jCxETHapmXURu7rJcZ7buVrU/qTMmYSju2Zp99hnu+3YB/Ht2AhLEJ6OAVbW5DC/ZGo9PFHmj+2ccw2pxnW/HhaCK9puOfPuspUonzUBlxU5p4MuXlmTsimn7yPva19IF3j4vVb0QsT/vbNkDIwg/Vi3puVBAK/b1UmxULt6PJhJypDZDy25NM5+3Zi6LExLNCLTwc0dKBFBl5jriQv8U5OWVeY1lelzpdqrqDwd79WVymzeu283uxFY1i4Y4dOMjcYSYdaNqz4shTjyM/pBGygvxUJ/YVWx/Fzb02m5+fJ/T5yCrKstq8T7GP+ts6sDUOTzqs/gotG7dE24CWqmPyi1Wt0EvfUs3v7dEagz7cizV7rsPvm3ojLNO95L7x1rOqDrYkAJ27PhpNpr+m5su076jXYEy0TtSpzocDyTotq4hUpmO6rM7oNkuXoXXbHlg2ZJn583EZcfjz2J/V0jFNqh5JECY1n2WQcULqKkykVkdjutWL+aSJdl3XSnOrVnUug4KsypOIlawgNg7FzcKxO31/SU3quX2wM+sg3t3SBhfnN8Xgq45iOxJxoSkMs3/2x76AXMy5Fnjz8ql4bMMzyuodFRCFJrnu2F0Qa3YTF6SsVqMJs6zidm2paFIyR6iOGN6y1lmaW7+97dWWO1xNu1hX1FXXrntmUiz2J+yAx/ip8EopKSmjIZnPx/dJUlYceam0jRl0KJGevBDPmY3UGTNLPQ+Olv5xpI2VdQ3k5Td92hgU69xx++9DVYLBAHdf9CgKx184jEKdSZWeCynwVC/mjbwb48PLZ6lyeEmHdpzzO7PXppzJzd4ZqQ33csuazdr9Re5/liUdfbp3h87bW927Ba+2bVUFidxNm84p91hW3oPzSUTojFTVfd7yN15aLhAJ6xALbqnhQPa+p9Ohyfx5+N0nFjnxx9B8ykIcCMjD+F6J8DOedf8WYooCkOSeiZBGkXjvqvcw6LtByHDPAE4Cbf5ug3eWvoNLWl+i3KrFyqslMytKKflNe6ScNIfqBMxYqO4Fttf7nDKE895H1po1Vhb584mHPt/7S1nhE3LM7d9uj6TTscp1PqZFZ/w97G94m3TIP3QY3q1ann3G1nCCTFIxmEiNOCuM6a7Horus7OWqRzguTr1YORIXKtYRZQE/8wJeXiKT3Xv/QaOx0888uK0f5JZJVdTDP3E72nlG2M1qKuu3fZBaYZuZ2QERVJHzV9XZqktbp5RekRJs5b1s1LboqUnBX5EHa3kvz34D70PK8EfNnz/hW4z7rjmCZD+Dcp/8bE1rmFpE4oL35yO0cWSpx6q9PEuIRuKYsdax0nZ+I2UJ68okdbM8TrFm/1twAMbjKQiZ/C62+KRhZO9Yc1LBstDc7sULQLxM5Dz0SwnD66salvvyzBhI5xHdlu0hYvYsc+kouQ96t2+P/D17VBs1ubuX3BenTVfeOiphlVhYJfljZKTyfHIkO7blNupCp0tV3E/LugY+F12kfi+G48etvuNohY8P7grEXM915izclolDW3q2xOH0w4AfEOQehHXD1+EUTuGCJhfgovYX4VTWKdww+AY8dd9T6NO9D9zc3Ow+i3RBQTi+cydynx5v974uQtvcoWB5viIj0XTC+HPug+fzPDifThBH7ks5uRnY+cgQZO/fjcFXHUP7jlfg6yvm4cRDj7tkp1F9haKbOCsU3fVYdNs+WLWXwYJ9+5E4cqR9S4bly9h5ZneWmDSzi1wpD8HKlkCyFNal4WrJUBx5aXCGLNo1lYypIg/WMi3AYWGAm5tV4jT3sFD0v/Kg8sCQ99mg/LMvsxeGXIie4T3R2KcxLssIRsvnPrHy+AhZMh/7dCfRfEcKToyZcI43SEWEdWniRkrnufn4nFMWLk2fj893fY7Y2F2Yd+wLc6ymbRZ/L50XCowFCPELwXs3v4exy8ciNiMWkQGRKvNrQepxfPZHc0Rm6UsvBRjaFBGflhxreR1hzPZb8TZbFdi2ewl3sBVBBpMJegvBFVdUiOebFuOG7jfi3m27oEtLq1B27LrW6VLZ47Es3ahdA11YGDxDQ5G3Y4eV11XCE09ahbSEfPoJDkXo1e9K767H3wf/xsJvZmDIN0cQkas7J0u4dIoJDTwbIKswS7mFL+ixAAsXLkRsXizWfLkGHvDAzp07Va3cf//9V9XPbty4scMd8j7btyNJam9XotOttju7He08sfycnNvJvZIxfUMkIrJ0LtlpVF8wGo3YtGmTGu/evbvqQGIitapF6mn3798foaEllYikbvfp06fPu872O++8g82bN6t7VX0gv5RSdI5qRbqXu7jotvcy6GYwVImbs6MPPUtsH+RliiWLDgB7LuRNJ09G4lNPlVqD2BWFgCNipjbrRTurpbu0fVNtyGRSbuH23LFPz56AbdnH8OjvZ63gGvZqzib7GTH8ukQUGguxaHUzJVotkbq2WU8PRYOZC2FMPm5+6U4cNbrUbOm217zJ9Ndx/H9fQn8kQZXrSQzxwHfXNkb/+QdUJl7Jj2Brze7SuAvi8uNwMu8kWjVuhXUPrcOR9CPm0nmW7qSCjEsZNbH+W7qUS0LECb0S8drGcPga3ZHsa8B91x1F64AWWLQqGt5t2trtCKtoG3CGmufOJrore05K88jQ+K55CPodPVspYvC1CdgclA0YgS4HfLB0+9ms1F9e2xnuXdpjeI9bkTdmQpWUq6oNKnouK3o8jmTclvvLwfGD0Kttb6QOfRim42dFd4K/AUP6HsOJBm4wFhsRmOdmLrelicGp68NKpn0KMfD6WGQY3HHZ/stgDDDi+4++R0Pfs14px48fx/z581V5zltuuaXc82P73PV86EHkvPyKwxZr2/MVMfctBFx3Xa2FGlSkM1rm77irH3zTz8a95zVtiE7LvnXJTqP6ALOXVz/NmjXDd999h86dO6tpiu7Kwezl5BzKSrAVNfctRM59q8LJt0pLZKLcv8tIiFJmNu4F8+Hdrt2ZDNWLELNgQYklMSpKWRBTX3+9RIBHRqqHfnnbcnYcTSBjmXRGw5FkNee9f06e6dVugrk3pqqSbvYS5YiAlAR8A7sPNCcWCvUKxUtXvKQsx5rglpfgAdeUZNgPy9Hho+Wh+HRltNlKLMskKaAgQtt33Bvq7+mGHnhvYAg+PLxMuTVacnLsA/g05Vd8vWoe9o56zGpZyoTJyNi2GW4pJ+B5IgM+R5Jx74e7VSZecQeXeEQhyCdI/fXM8sS2p7eh1Y+t8Fqz17Bx+EYE+QahR0QPc1kc+ds9vLuq0yvjx/49hoXf/YycBx+02vaM64zYEpKHDO+Sl+9GBe5oddoTL31tVOL89M7tOKaFQURGVqoNnE8yNlfPql3a/peXvKys47ObuMuCm49Zuza/uq6p6lAKy9dj6qEwq2W91m3EF9tfw64hd6lrmtcoAJmzJykBaU60t3PneVVgqG4q2r7Ku+/aa1+2ic+Kjh+Hyabz94E2GzBk9YPYdPeNSnBb3kcis0s69BrmFEP+iZeKJEGTZeNvycW24Dy8dKcOuogING5xEQY1fhwPGx5GA10DRCACxnzrbYl16plnnnFIcNt77ua8OOVs0ra5b5X5m7abnFUs3OIddx6JOs+HiiQNlfnN37K23kknR2YjrxrbX1IxpANTxKAM1eVBJB5gOYU5Dg9pOWkqGZ/8dfQ7so2yyMvLw3333ae8VC666CJcd911av6aNWvQsWNHPPbYY6pjrVOnTtixYweGDh2qxnv27InExESzV8D48ePV52V46qmnUHjm/iUdwnfeeaf6jiz74IMP1PyXX34ZSUlJattyjrdvL8mplJycjFtvvVXtT9++fXHq1Cnzvs6cORM9evRA165dccMNNyA2NlbNz8rKUutp27YtLr30UuV9U5bQf+6559CnTx9ERUVh3rx5WLBgAXr37q2WLVt2NvmheDrIPlx88cXo0qWLuYScdMhcf/31an6HDh3wwAMPICcnx+q8Pf744+p8ynKxujsztHTXQUt3dbo92ou/dsRNvTRLgyNJqOzF0RlSU80uva7gMlbbsdqOUNOu7VVh6bZ01S7L6qVZgjs0bgvj6dNo9nYnvLIxAG0zfDHz+mKs0h1GF8/m+HhlBDZ5JiPfkI/WmT7KWmWOC/89BiF5Z0uapPgUYewlSZi+NgwRudbnRV6ux/VJxKy1EWZhb9fF24LcQD88dPVxbNcnQp+px9HHtiJJn4X2YRdi9YrVmDFjBv7++29s3bpVPZT279+PLVu2KFenw4cPY+XKlfj222/Vw2nkyJH4/uOP8WFoGKItrplW67dtUBvEDRwMXerZh6x0LAzvG4eXt0SiR6ovvLt3d7gWb1W0dWcIrTifNlvW/tsLQ0icMBEGm3uYPWutvXYvHgsvXZyMFzeHmUs6jro0AZO2haj2luBbCA93PUKy3ZHob8Rx7wJcdNJHVYaI8ytEim8ROp/wxcamORh5aQIuNoThg01tURQeBJ/j6aoDRjpdwl5/DUmTJpvDkWQ/hdr0WKhI+7L8rD1vqrBp05D09NOq81drX9o1kMRzRwcNhvHMy64t8pt++Mo4TNwWgtYZXhjaNw4nvA2q/OWctZE40DAf028w4Ycbl+L+lY/g0KmD6OLbGj+PXGv2UrEM6agOKmqxdoXnVHnY+73ItVo0vCVG3fYKOoV0MndWkvoT0y2i2H+qP6qT7MnZ8PP0K3X5//73PyU8ly9frqZF5AYGBirxeM0112DDhg3o1q0bnn/+ebz//vv4559/0K5dOzzxxBPw9fVV7wAy/4svvlDr0Ol0uO2223DFFVdg4sSJSgy3aNECU6dOVedP1iXitVevXnYt3eISLu8QTZo0Ua7nIlwnT56Mzz77DKtXr1b7KttYvHixEsg///yzEvwpKSlYtGiR0keybukUsOdeLtu84447MGfOHBw6dEh1Bjz77LNKiIvIvummm5CWlqbc3K+66ir88ssvCAsLw4kTJ5TYX7duHcLDw9V5kn2UTg0R2DExMZg0aZL5vEm4jeyD7K+cY+38Vge0dJMaw25da6kPPH1amaU4yrI0lGbVdffzM/dse7dte7bsx7LPEbNwAWI+XeRSZT9coXRJVZcgqykrvLhC22LrGaAswcGdcerpZ5A65GGs6/crfo5qj9H74jDpf574bdu1WDV2G9p9+Q2ybnkMqXcPRdN3P4J/VHP1/WaNmiOsUUkCNg0R4EtXxpgFt2RL1yxdInxEYMt4SkM3Fbe5MipbCfHSyDuVjZSZKWj/b3vs6f8n8h8bh7DpS+FrcMMNPXvir7/+Ug8uEdySkbj/3XdjwIAB6oEmvc39+vVTHYTCrOeew8o+lyjBne3nh8dPp8MgCZMSEhHw9Ezodd5o8ebbVtsXQRaU76EyvbsZi3F41z+4/b3Lce1nN6Lpoo+sREmZltlyas1LW7dnDTakpTlc2swZsVdLWNt/SWoZPme2dalGSXJ55v5pzoptY62V7Px77r+7pJ37FWJLUK4S2CK0H9kThO1BuTDp3M0lHcV66h4ZjpALu6NZtyvU9rp9tBRdPGLU9ZWSjGk+BoTneqt1RGV74q1/I/HsHz64u8M69Ir5GhsRV1I2MjISp+YvUOEbkjdB7lNStq62ysdpXgTltS/L54nlfVc6bw2paWaLr1tgIOKHD1cdCnl79pQkCU1Kwubrrsf83h3QeHgL9G+y1mofpKPN8jf+0ZpoLLrSExd+/RP8m0ap5IVS/rLD+wvQ/ZMvsePOP+A78hX8sq8v/hn+L1aP246G2cXqXiT3pOr0YKqMxdoVnlMVeU5suesuxBWXXKsh8w/jlncuQed5nVUnLCE1jYjavXv3KuEowtmyLrnkahCRLEjHuUyL4BbE4nzw4EE1Lp3rYgH38vJS5dUefvhh/P777+Zljz5aEkonHRZi9ZZ5pSEWbBGzglifpfNeEHEu35P9EZE+ffp0xMXFqWV//PEHHnzwQRV3L+8bYnkuC+kI0I7P29tblYXTjlHEtAjutWvX4siRI7jxxhvV9kRIC2JYEKEt7zjy3iNeACL8NUu9tl4R3LbH4KywIB6p1MPM0lKTNGGiWajJX8uee3vf03rOZX5ZPeeWglxeDiwt4va25cxogtbZj6G0TpDaRnsZFDQrovzVrDWlvQxaelJYiiKPZ6fg6+nTcOTwaJVoKm73YRjTTsMjJgbr1m7Cl19+iZdMr6HVBa0w+LJbMXFfBopTUsyZ+n1ffh84edZK7B4chOdvTMG2ojxMucMNr31vwqEGBfjgJl/8fve38Pv+VoQlHMPE/6zdfS1pAjcs79wdzV97C8efeVbtpzxwEkaOMpf0admypdmS81mXLoj4808EBAbaPV/ebdqgwM0NnRctxE8NG8Lj9GnEDR2GjceP47rmzTE/phnCLUI2tE4ESyvec1tC0SrDiI9bf4AGPs1xX8e+Dlm9NZdoSyubTEum5LKs2eK1oMWTa9+1J6acEU0Mavc72/0X4fpbm9a42jI222jE0VGjMTk5CeM8PNG0uFi9iORs3YJDgScx6Mv78aq+IVr5l2Sh1yypb62LwaGAkvb153WLEdOyJf5J319iPX2oxHoqaG3ff+kytV+Ij0e3EyVWPmnLs3sexdjlnkqUzF4Xodxwg9KNar9yjx5UObXdpdRUeDgChwy2ivW39VCqTux5EaikchalrKR92cbsSvuUBGCfvvce1syejY8WLcSRgYNQnJSEwv/+M38u/ng8Fix5FDd+eRDB+UZE+hcjpg0wda3177XYzQ3HfYsw5Q53LFwdjrDoEKwe9z78fBti8wOrEf/zILhnpiH3mVfQ4bVXEf/I/ap0m3RcdPFtBaSdtmthruocCLbPXctyomU9d13lOeXoc6JFaCh2XHUlMqdMxCGvLOXmf+LUQeX1JOE5xDmQ55y4Xgs+ZxKMVjXSySWWaEeQTpnen/RW1QMkqeG6B9c55B1R3mfECr1nzx6sWrVKidoJEyaYBaRlUi6xLttOi5u1PWyrFji6zN42tW3I9RCL9yOPPFLm9yuzDe8z0/I9GWSbsj1xDRfxbcuSJUvU+fpT3nUCAjB37lw1Xd4xOCs1U2iUuDz2esAlDtu2B9y2576qes5rK865KqkLx+BKVnjb2E8512JVFCuXuPmKKBLB7REViZZLl8AvssSSLW5U4vIkblnXXHENNq7aC5PkGYiKwuq+fTHi0eeRdPxs4irB3cMTowJHwnepLw68nY4nj2fgheQsBPzUFCEx7bDutl+w8LtoRGTrcNLNDVkWPdy6pk3VIOjT0xE/aLD5ZTlyzmwluO1ZTz2Sj8OntAexzfny8/ODZ0SEmo5++SV816WrEtwZ3t7Y2O924Exvt8bkpGQY3T1UjLkIsg5Tv8D7v4zChjuud8jyXJp3i9SRLsuaLWEC58Tt2xFTzordvAPT3oAuNBRP9e+P1r/+Zv0FnQ7uqamYptMrwZ3q6Y67Y/ag4/8uw62f34rTxpLEegOvicUJDzeVYK+wdTQ6fv29sqSuH7kdjTt1VaJPi/HX7imW9xt7+xUxfTr+N2UnAj98B+4R4eo6S6dLdE5JOMQ1PTbg2p4bkBxgUtbgOEtPkxruBLH1IshcsaIki7sd7ynLuPpjx47h+ltuUS6RYeHB2GQ4hrgn+p1jvQ6HBwZ/uA/Bp43m0JDZZ0JD5PgH941Fir8JYbl6/L6pN5YPW4kWn32OTvMWqnMvSInMNkuXnfXAGTykRHCfQd1z7HhunE8OhNKwfO5K9nJ9p07qryPPXVd+Ttne91Ss8DXXoM1nX2HipSfUZ6KK/NEhuINL5oyoq0ic8vfff68GGa8OROCJ67cjQ7BfMHY8tgMbHtqg/sq0I98rT4AmJCSoz4hLuMRMi9iML6NCjz3ECvzpp5+qOG4RmB9//LE5NlyWffTRR2pc3mEk3Ozaa69V0yJYJUzWEcRrTly1tRjvoqIibNu2zbwNicuWfRf38s8//xznS58+fVQogKVVXjoj5BjT09MRFBSk9l/iyV09Szot3cQhKtsD7uo958R5qKgV3vZFXfOysM2GHz6tJDzCEnG5EjcozRVKs0TlvPMO3gkMREBuLkzyIHjuWeTOmAlDYiIu/PxrzBgwAYXFxcjS61Wvq7g+yUtdYHA4Wlx2FRAXhx4ff4SUadORv3evWrckgpOM4cckftTC7Vr216dTpzKtp2Udf2nn65Kbb0bC7yvVuem2aCHEoe3Yho2wlO8fRnTEoE+P4PVuXnjNQ4/odJit4InehVhxZSR6HP4LNzW+CV+8PQ+jn38egVJuLSQEMQEBGHvipDpH4qL8SkYGhmVlITQ+HuvuvgdbrroStxcX2z0eGAx2xXpVZc+v7qzqpXU2iGvzkMNH4C/XpIzqD2MuP4r44BKLTwtDY6R7GpCOLHgHhmHvk9usMtaHVMF+yXnt3vVG5M4Isa4ffZUXkv1LWsTTPeLM1766O0HKuj6WvwOzhdumlNWxwUPgFROjOqp+7N4dL7w9F62CGuGniy/Cto1foX/Bm/joz2hJT3h2mzb7MOPKfBzwLUBykAdaNIrAxfM/wLu6k6oagObl0SAozG57sefhIaUGJet5ab/d0u5T5+NRYPnclXKiELf8evLctXff+3PrDhR+UIA3O0SiZYYnVl67DLdf8aDT5Yyoz5QnWGsaucdWtTeEhIGJBVkEqwjmQYMGKZdpiU12FLE+iwu1xDwLV155JUaPHq3GxQosydgkdlq2IfHTmuu15HkRV3SJDS9PuErY2smTJ1WctSD7Onz4cOXiLfHmDz30kHJ9Dw4OVsnUCgoKzuOsQJU+FLfxp59+GuPGjVMiPzo6Wrm5Dx48WHXGSOI22d5ll11mTurmijCRWjkwkRohrpv8r7Sas46WzrGlrGRZ5ZXji5gxHcacHPVCaFkDWEvadGzgIIfLjp1vCSd7NYjFhTh86utIfu55q8RJWccOIWXwg1aWwQeujVUJ5joUh2DZmjaI9wJWd7kYJ09k4PSJE3g0MxMt9B6IXrgAI55/HuEeHrht8xak+XhjbHIy1i9ZguPDhlsdjyTuqs4kThVJ1Faa+HMPCEDa6dN226xtEqrQ11/DfkmAk5mlOh80Mdj0k/exCyk4selftHh+PnTFsEpmN/iakti5FZt6w79te6ROGoyOESUxwJWhvORY9mrJi+v5fZcfwPHs5HPK6knZvOZLl5p/L1XVaeHI9cnbtav0xGCDh+C0jw8CcnNgTEzCSV8PvNE9F6O2uKls4pJcTn6fkoMh3r8Ik3olYfb6KJVszhKJic+c/jTat+sDz+yCitWTtnO/ES8Wy840e7/d6izVWNO15Z0RESC3X345RmWkILxQh6QGxbjo7U9w+vmXXSpJXH2hOhKpEVIVMJEaIYRUpNzSmczFlSmHVZabe9jzzynBXZrrtCa4tfXIuBZrLp8zZ4i22a/qKOGkbd/SDbXZksXw69nTKvxDEmedenaK1Xcl9nvxyhh0SfPBrJ/9oEtJg/upRPzo+xkeeO4BjHt7Ii75+Tt1TgxNG2PEKyPwzDtz0OXXX3DTqlXY8eefOPnCi1br3PXwwzi0Zw+8WreqtiROZSU6c9TdN2nMWJiKiuyuXzuXuvBw7O5/H65+8knctn07Mn19VXy9lGrUv/8Gmn/eQyWo85o6TwnuRJ9C1ZGBM4Jw6e8x+Hx1S+iS02A4fETFAp9PtuWyQnwkq7ock22CQkm4t2x1S6xY39OcfV9crHN1xapc3rEBA9Tv5XzdoB29PvtWLMflMTHYYJO0J2X6dMRu2YLXPvwQd+7fhz5rf8TzfaFcwpvkFmHGnx5KcCcFFGPcjZnY37hALbvoi+/xwSPfIbxByW/YzdsbubNKSqcVJySh8cQ55wju8tys7SV7lDh+S8Fd2m+3tLAEe4Lb1cvq1ZYV9cnnn8egg0eR1MCI8Cx3pA0tyU/gKjkjCCGuDy3d5UBLNyF1z9Ids2QxfCULeBW7F1bGYlXRUlNVXcKnNKsuiooQq9Xs1pInjntaucpaIoJMknyJ5dt8zO4e6BbWDbvTdiM/Lwut9WGYO3CxKpEUNvFtc0mqvAeHI3fadATk5yOusBDPFhvx588/w7d1a8yaPRuphw/jePJxuDdqiBdfeQWRfn7lWlXLclEWj4Lyrk95lmHfN+cg9IILrNqsWNLkxT41KQmdmjVDalGRysL63ty56NwkCEcaFuDFf17FDwd+ULWbc3RGzFgfgc65TTD6pkwkZiXg81Ut0TTbrcotnWWdE8vSjKV5buQfOogBVxzG0Zx4LFvRDOG5HijQA3HPDsYFn/xRci2rqC3Kto8NHqzWqZHt749Nl1+G7n/9Df/sbKR7eaHJSy9B9+67JbHTboUYGpmIoh7+OOl5Un1HOoQsXeKz33wGHa6+C7sSt6OdZ4SKv9Z+d/n79iFG4p3DwlTOASlPqd0P5Jw5asW3/R1Dr8ex+/qr34uI+qgP5p3jRWIubebgfaMyZfVo6T5z7iSZXZcuaBtlwpSDZzsoQj79BIE9+lSqvZLqgZZuUlct3RTd5UDRTUj1UN0vg/bEk6O1kc+HyriCV0YUVXccYmkv+Ef7329lvRt9Wy5W+MXBS+eFAqN1bJeHEaqMkiRk07Jvv/9XNHqm+iKhRSPoJXFaahHc4Y7jjXxwoGcf9F39jxJ8f65ZA0lt92FUJP7Ztw+emZn4pdvF8LWoqVwVLsoVcfeVZFTper25zcoD9s0338QPP/ygaqxKCRfJfC+1S6NCQ7F7xDBk7N2Jey7ba673vnBVDA42zMe8m3zw7/0roQ8OVtmUWyUarNz4I959BwFXX312n6q4nTrSSSGoUlqN/bEzZSe2bl6Btq98qo5DQxcZgeafflpm54AjsfSSNEeSAn37+ut427+B1fXx6djRfF3FPX917i5s3/47es/8FQcbFqhkc5JkTpB9s3WJFwt2Mzv7qO2X7Ie2fnG392nb1txBU5HfmeVx2op6z5gYu7/dytYdd7QTjqL7LCs++wyN35oN/4wc8zxjWDDaSgI8F0nWWBeR5Glbt25V4xKrrDow6V5OnBC6lxNCSBVm3D8fyqpJXxaO1Kuv6drp9lzp1Xy9df7Nd7a2xsYbf0DsoO1oHVhSricyIBLz+8xCawSbM6CLEOp4ygeRWXrlVq1LSgUOx8It5QQSshMxIeIvtPjfEiUgcvfvRdeQJggF8ArcsP3zZfi2Q0dV6in/wAEsfPsd9VLmiIuyJNfSXMjz9u936Po44u67dOlSVQJm6tSpuOKKK9TDWLj33nuxt3Avrnu3DxJ3rEdgepE6drG+yt+obA9cWdxCCW6xuIrreBe3yHPc+CVZWO6ZjLFV6cZdkWzV2jLZx56RPfFYv+dR/PzZMl3Coxfsxmcpv5da+9iR7Nx///23Sszz2ujReLlJ0DnXRwRw+JxZ2PXi/Qhd3An9vuiHKfvfVTWzNcE96ZJJ6ObRwiy4JTY7961nleAWy7m9EBLt+CzbjcS3q46ZStSItzyX2u+n2bLPleAu7bdbkQofFalRTqyRa99iwUIluCXEQKu3LmEcjoYXkerzQjh06JAaZJyQugot3eVASzch1UNNWGCqO1u1JZWxQrkSZR2fuM/qAgMRsvBD7LPI9Kxv2QJpw29GwDNvqlhZjcwmvrjjkh1q3NYqqbmrC5+ubo6IrLO1xGW7KU88jsvvusucuVUywEq2VY8zZdhKCymIeHsuUqe+UTJfp4PPRRfBIHHJNp4Pjli6xbo9bdo0tV35GxERoUTnN3u+wcLtC7Hq2Cqz1fWrvy9Qwrs0q6vteW06aWJJdm5J9if7PfctpL4xzSnakT33b+16ZTbywu+DfkfvqN5Wvzur44uMVGX7bH8Xv23YgI9nzMBUN3e4paSc076kvTx5SwY2Fx622p8mPk1wMu+k6uzZPmK7irc//ORj8IlLNZ9jRz1DqjOZWVXfpyriTUNL99lzLJ08ufv2YVaTABjubozftixW95jmXa9Ai7ffZfbyWkLa6O7du9W41GsWaOkmzggt3YQQ4gQ1Z6uqJr2zUtrxSdZzQRLBidDumKJXf0W8SCKwzs16Imr6DKt1tZ7zNvyjmiuX67evtS4VM/WKHDVfhgk9E6yW+UyZiEv69cPx48fx/vvvq86aYcOG4a4zIlxwDwlB9vBh1jtvNCLl9anQNw02Z68XUdP0heetro9KDpZ4xipqk1xMpuOHDUfxmVIq4gq9ePFihIeHY/XR1QiZGYLB3w02C25BjsF7yoRz6mNbijjb8you5ZJzQNvPxCeedAprppYoTAS3dByIFbkwJNDsxeCTmY8+8/vg1k9vxJrBt+DI/fcrISv7K0JbjkfVqj5jnUVoCJZfeolyr7/11lvxv99/R4MOHdRxBsybg7mZv2DG9cVK1G/2TsF/eYfRrKghgj0aq/0Rob3vyX2qlu62BzepfAFSM1tqZzf77DMVUy1Cy1HPkIokM6vN+1RlvWnqO5rnQdj8+fhm1V/wW+uHpi064P6rj2DqdUUU3LWI3MelzJUM9TXDPqkf0NJdDrR0E1I91EULTE1a1p3p+CTLedxDJdmANcy1t6UsmR0LosTm7j9xAI0mzLKynIqgG3DlYSRkJqj4Z3HH1pBST8seaY+mUW1wTVBPtG3TC39t+wuh7qG4vF0n/LlzKx59/D68ZwhCtIdnqWXiVAmv6GgUxsefE8NbWum33XfdhaPGYvT4/DME+/vjpG8x5m6Yi18O/YKkY7uR4Wk8G1fsH4bk7GT09miNRauirY6vtARZtuc1848/lOCuqjJx54u9ePnMuMPYfPfN2NsgG5MvO4UstzwE5emwZGWMEuNpjXT4d3A33PzlYXgcL0lypjEgMRYHwt2x/of1yk1/e/J2HD+dgF+3fIlPj36L2f+Gq9CEsX0ScbBRAYLy9Vi+qRcatO1gVUatMsnFnM3S7SiM6a4aXnzxRcycORNf/PsFbv3+VjXvg1s+wMALB55XpQBSNTCRGnFWmEitmqHoJqR6qIuiuz5jz+W1rNrblhZy22VSC7qouAjuKSeRHFCMp3vE44314UqAx/kVIq5BoRJ1Wtb0Fjk+WLSmGfb75iAqS4+oHE9kNfbG7/0vwPVfH4JfWpbVvi6/uiv67o5VQlBEfub4oQiYsdCciVvqi7t5eKgEYuuPrMcHr3yAVV9+g0v7X4sHM7MQcPw0+l++3yo52tHGBjzVOxbNgltj7YNrcfTQVjQaO928zoqEGzirALRNFCbjeYY87CtMVCJ4xYYvcO+K4Uog24YNFLmZ4GE669WQ4F+EIX1jcbqhBwJz3JCqzzV3WlgKdynxNL5nAmZujEZYpluVJBdz1dAQZi+vGtLT01VHz8CBA5FzTQ4WbF+g5rdq3Ar/PfYfhXcNI3HcRWdKMUqYkEzTvRxo1qwZvvvuO1UJQ0KpRo8ejX79+lX6PC9cuFAl+ZT8GaRy0L2cEEJIjWNZL7g0l1exgJfqct+mjapfbW+Zd9t2CLigkxq/+OtfMG/yGvT8ZjnymzZSgjsmy9Ps1nxxig++/DkKTTJNiMnQI7ZBIZJ8i5BZkIlGP/yFRzvtVILPkgs2rMfgjluU67KIYt9Rr6m/2UF++HPM1Xhuz1sYu+kVBL0RhKuXXY0vW36JE88a8U/47/BISEbwaePZ5Gh/lCRH62tqhX/vXq5iixtmF6Nby0vg3aZthcMN7NV7rmg9+ep2g7ZMjuYDD/Ro1gceaafRYcpSzNvQWmWpf75rmtV3RXBLh8mWoFx1PSKzPdQ5bJ5SjE9WhKos95Lt/o52d8A7JKykQyXAhPAsnSr/ZRbcNi72VZFczFVCQ+wlN6ypxIp1icaNG2PChAlKhNzW/Dbz/EPph1QlAVLz2cu/+eYbNch4fcFgOFtmsyaQ9r5v374a3SaxxjoNLSGEEFIBi1vE7FmqtvE5CcqkhvJDD5utxpr40USCZUmq8pb1QEs13XLZMgz48lqcTjqKJWtaIjrTE5+uOluPWeeuw4ftT+KN9RGIyPWASO1p68OV4Iv3K8Sk3smYuj5MCXaZ/0aXFLz3d5T5+4923oNtm6w7DxRnHDFO+BiVGNSsuOZa0JLwbMYMtL7QuvZ7xIzpMObk2D2+0gSSJgAFzbItf7V1OoMAtM0Sf7YcXyIui4zG2itfRoPXP0IxLOKMdTp8dKkRD/+rV9fD4GZCmrcBc9dFIzy7xMTd2bcVlty5RI2rMmp3W5dRKy3GWovHtvS0qEg8tiZmS2uLziRmS4v/JhVj5MiReOCBBxAcHozwleFIyippq+l56TyVdRHJil5KhQW7yGfT9gPBbQFHQw7kc27WOUpskXJoL7zwAn755RdlvR4/fjxGjBiBgwcPKgv/U089hUcffbTMdYgXQGnfsbSOCxdffLEKpZDM8Js3b8aYMWMwZcoUvP7667jpppus1ivfFe+PVatWIT4+Hs8++yy8vLzw4YcfIjk5GW+88Qb69++vPrtp0yZMnDhReQNLR8kzzzyDe+65R3Uk3HzzzTh58iTy8vJw0UUX4aOPPoKfnx/WrFmDJ598Epdffjn+/fdf9dlFixapfSwLg7EYel3d8Iak6CaEEFJp0ZUwZiw8QpqWJHIyGmFIS0P4nNmq9JKyHgYHnyNabC2Vji6TElubn9huV5DpmzZFaGqqWYSLlfTBy+Pw8q6WiDKEY0rfeGwrysOUO9zw2vcmxPsU4vntEVbrn7U+GgP6HlZu40Ijr0Y4XXAaUQFR+HXAr6pU1SEcwrvX6TDtW4svGo1ImjDRyi1ZzbYQ3KUdkysKQM26rFnkzWJXp0PIM8/ATbLES7Z6nQ6GC1rAKz0HxsQkTN3dChnTH4L+9Y+BhER0O1HyMisu/oHTx2FNpyvMrr1SRi322aFW25Vza8/FvjRPi4q441PM1i9EBMggwuCfe//B6L9H44cDP2DEzyOwY8QO+Lt51en8HM6ETqfDfffdZxal1VI2TET069UcmvNMEuDp59DximgV5Ljbtm2Lb7/9Vonpbt26KaEqbuClISK7ot956KGHsGTJknJd1HNycrB27Vol0iWxnQjvdevWqf0VkS6i+/Tp03jkkUdUx0FYWBhOnDih6qv36dNHJRb97LPP0KRJE3UdH3/8cbz99tuYNKnk/iyW9k8++QTvvfce5s2bp9a/fPlyu/tiLC5GflGxGq8rortuHAUhhJAaw9Kl15CQgLwtW5XwNLvndupUbS6vpdW1tn1N6/zOfHw95l/c8+0GtPjsc6wcv11luv5jwg50+eQz9HZvjrAsdxQENsaggnjEFxUiNNsNC1fGqJhiyY59cORB9R3Jkt2haQdse2Qbll+2VMUXWyGZuauwbnJNZt2vLPayfdvLEu+TkY/IN98sCRto0xbdLr1TZXC3RKa7d73RLLgr4mLvzO74xPm57rrrMG7kOOVh0axRMxw7fQxDvhqA2JEjy6wtT6oOEdqS10UGGa/rDB8+3Dy+cuVKs5Va8tvceeedal5ZVOY7jqJ1frRq1Qre3t64++671bRYo0+dOqUEt4jyI0eO4MYbb1QW9WuuuUZ9Zv/+/Upoz5kzB126dMGFF16In3/+Gdu3bzevv1WrVujZs6ca7927Nw4fPlsGUr5rKC5GoaEYeYUG5BQYYSyuW3Xb67Sl+9ixY3jllVeUq4SUmJEeGHGdkJ4VTyd6eSGEEFejPJfe6nJ5tRVZyrV53NMwJCdbfU5EeRcRvr4NAV9AcqD3iOihlvm07YjsCzqhQH8QrRYtxPqmTVGckoK4ocPQpmUL/DzibHbsIN8g8zo9T+cg5tVPYLST/K02Sk3VJvasy8qynZCgBtvkb5qlXrLEl2eVroiLvSu44xPn5eGHH8aQIUMwcftEzLt5Hm5YegP+3vULDm9pofIJaOETlr9zWws4cQGkQ08s0Y5axT++Fkg/CjRuDjz0u2Mu5g66ofv7+5e6rDKdDpbf0ev1VnHxkvirIojQtrTIa9OyDRnEJVzEsdRTF/Fti1jTRXP9+eefCAgIwNy5c9W07fqLi00wise/QcS1AcUmk4oAqOvUaUu3uDFIhuQPPvgAu3fvVr0v4s4gsQeEEEIqT23VC7ZNeiUZ0jXcvL0RbZlUqxRLp21CKnlR8YyIUNMxc+eqxGCWpYO0pHG6hg2ha96sRHBLqbGOHc01qGv6PNQmpVmXLcuy2euEEbHiiFW6IgnDmFyMnA8DBgxQFjlx2Q3wDDDnbhh01VGVuLEqPViIfeQ9fdu2bWqQ8WpBhKm4fjsy+AUDj60FHlpV8lemHfleJQSzWIkl5llIS0tTLuPXXnttpb8jluQNGzao8Y0bNyrrs4aI4IyMDJwv4kZ+9OhRK+u6WLMLCwtVZYCgoCC1raysLJW8zRKTVDopNCC7wKAs2jJDrNn1QXDXedF9ww03YMGCBcp9SMpD3HbbbXj66adVAyWEEFI5asOlVxO+liILej10fn7wvuACVYKsxfffwa9nT4cyTzvqwm2ZqVustH5TpiBs5gwVsx732ONIGj+hxLU+MrLeuDbby/btSOdDRbKEV8TF3hXc8YlzItY8iS+VmNWNP21UYSWC5HWY0LPEY8NeJ5L8tulmXjWI0BYjmWYocwo8fYHIbiV/qxGxBO/du1fFT1911VXKE1dzv67Md1599VW8++67KsZ7/vz5yiKtIXHYkkBNXMIlHvt8sv+L27isS7bTvn17FbMt127w4MHIzc1VMefifn7ppZcqK3ZeoVGJbVOxCQYxcddT3EzVkrHAeXnuuefw22+/qSx+9igoKFCDhmTmi4qKUr030nPjrEhjlx6v4OBg1jwmLgHbrOsiL5tJY8ai4NAhRC2Yr15ERVzFDxsOr1atVCK1qhQ85W0vbPo0FOfmQh8cbP6OJHQTy/T57oesJ27goJIOhshIeE2YgILp01GUkAB9ZCQ8Y2JQFBdXI+fBmZBrYszIUOfc9hyFvjEVxyc/Y+6UiV6y2HxtLL9X1deKnAvvs47x8ssvKwtenyv6YEPiBrz4xRN4/psiq1rz+rAwRC/+VI3Xh994TbbRHTt2qHHxOhDO931W3KolxLR58+ZWLtOk6lEWa1U9xA3ukggPJmW9LjKW/D1f3N3d4Odp3aFb23W6JdO7ZbsSrSidEeJJUJZWrFeiW7LxSZY/SZ8vcTz2kFT6L7300jnzDxw4gAYNGsCZb1pysRs2bEjRTVwCtlnXxlRUBFNmJtybNDHPKz55Em4BAapEWFUi6816aiSKk5LgHh4Ov8mTkDP1DfN0g7fnWu1HVR+P6dQpZI0Zq7anobYrL9yBgTV2HpwVOXc5U6bAePSYOifuISEqRl7Ombjji3eAvXNRk22ovsL7bOnYa3/GEyekgDLSR4+E7ngaEnwLVYm7kLyS9ujWpAnc9HrVvqvq3kOqvs0WFRWpdcTExFB0VxMiH0VwG6tZRrq7ucHbw3lEd2xsrGqbHhbPKHGlb9OmTd0U3eLGMG3atDI/I64X7dq1M08nJibiiiuuUHXxPv7441K/R0s3ITUDLTCkImgWZMukZWJF1SzM1W25z9u1C/EDB5m/E7VksXKPJqiUBbumvSXqK7zP2sde+zu5bx/23nMv/Nu1Q1BwENJ2b8Otvbeqzy9eGYPwXI8qv/eQ6mmztHRXL5rLeE0kF3evQ5Zul8xePm7cOAwdal2/0xaJ4dZISkpScQ/iOiRF3stCCsHLYItWzsBVyi4Q4gqwzRJH8YqMtJstXeafL0WZmerlWwS9iD7bbMVSk1zcpS2R6YrUga7zeHtDZ+PG6RkSUulzXpyZyYRVVQTvs461v4yJkxBsNCJpxw5sGjIYt48bBcN3VyAtNw3j+yRh6cqYKr/3kBKLqWb/09rq+bZZbR3aQKoGqZ0tMdmFxuIaS37mVsms7tWB1p5s26aj7dQlLd0VQSzcIrjFrVxS2UvSjIogvRfiRlBe74Uz9Aympqaqmn0U3cQVYJslFUHVyT2TvE3DsiRVda0/YvYsJI4dZ45P9p4wHvnTZ5xNIicZtpnNuErPOTszqg7eZyvW/tzCwvBmk0B8/N13KqRwxpsz8G/+9+j/0R6r+G6206pDylB99dVXavyee+5R77Dn+z6rWSQZ031+SGkvg5T3Un9rTmhbIrHifl7OYSMurV05qhXd67rgFnfy6OhoFcct7ipSr1sGQgghrkFNZEvX6o5bItPebduaM26LO6m+U6cSt9JysqOTyp9zeg+Q2mp/0bNmYt7XX+PIkSMYPnw42jZuime+zleCO86/EAOuiUVygKnOVygg9duanV9kRFZ+kSrtJeNFNWjZrss4R9dBNfH777+r5GkyRNq4AdVxAz8hhNQZtFJTgmYFlb/y0ltVwre0uuOyHSlRJjWmdUFBQGqquWa0bJdxx9Vzzim8SW22PzHWvDFxoipJmPjbbziUHI9hfeNUKbGBVx7Gik294M9OtypBPFDvuusu8zjfz2s+Plss2pIQzVBFGcdJPbR0S9y3FitiOxBCCHENLGtza2JME74y/3yFb3mWdBHcrAPt+rXeCbHX/qT0n0+3rqrOvNb+cnfuROyAgUgcPwERM2YAr7+OEyq6tKSG94yBjRE+ZxY73aoAiZH19PRUg7PE7tYka9asUbWzaxLRQQWaNTvfgNxCIwqKiq0E999//YmVK5abp5OTknDDNVfV6H7WNeq06CaEEFI3EGFdXcJXs6RrcZqSlVxZW+lCXm3wnBNnaX+Rc2bDkJoGGI1KeEsGfslsLgJckigac3Jw1W13IunZJLTY2wIwAp8m/IiJa57DxmNrkZkUe46gl+zoxPWQ62bb4efK11Pi5c8t82VUbuMFhrJdxv8R0f37CvN0WHg4flu5ujp3t85T5xOpnS9MpEZI9cAEP8SZkJcqW4u2vGxZupCzzdb8OSfnB9usY+3P0aR+p0+fRpehXXCsyzF4GIE5/0bggix/XPz1zwiIbmlejwj6qvDCqS9tdPfu3Wq8Q4cO6m9tJFKT9pAweozqaNGue1Vfz+XLl2Py5MlKDEuJqffff18d65NPPomuXbti69atqoLSJ598oqzfBw8eVF672dnZ6jzdfvvtePXVV1Ud8ueffx6rVq1CYWGhqhH9wQcfqHXK5+W8SWitrHvQoEGqitPst+aiyFCMzKxsdGjbElu270JKynGMHfUUcnNzUVCQj3vu7Y/xk57Bjv+2467bb4Wx2IjwsHDcens/9B8wEJf16o645DR1LCLIX3rhORiNBjRq1Biz33ob7S5oryzk48eOQp9LLsOG9WvVsb7/4Xx07dYNJ9LS8NDwIUg5nqy8Gjp37or3Piy9jLPARGqEEEJIHaI6LemE55w492/e0aR+jRo1wqFvDuGhLg+hYaEOrTK8EJbphvghw5C7datZuItwE0FPykfE5K5du9Qg47WFXC+5buYQgyq+niKAH3jgASxatAg7duzAI488grvvvltZn6XTYciQIeocTJw4Ef3791fz33nnHdxyyy3477//sHPnTowdO1ata8aMGfDz88PGjRuxfft2dOrUCc8995x5W1u2bMHPP/+M3Xv24N77B+DLr75CZnaeqqv93bff4LLLr0BQcDCiY5rhh1+W4+91G/Hnvxvw/Xf/w6aNG3DhRZ0x/KGHce999+OfDZsx8Zmz6xbSUlPx0LDBmPfRJ1i7cSuGDn8QgweU7LNwYP9+PDBgEP7dsAWPjHgCr7z0gpr/xbLPEBPTDOs2bVPfe/WN6ahP0L2cEEIIIYTUW0pLqibzbZFkX2/e8CayfTxUcjXJau6enIbYBwaczVEgllKWEnQIsXi2atVKDbUZ0y3XSwsrUsK7iq/nhg0blDiWQRgwYICyQEulpWbNmuHqq69W8++9915VZSk+Ph6XX345PvroIzz77LNYsWKF6vQRvvvuO1UGWazhMnz++efKsq8lRut3512AhzdyCowICYtQIvqXn39Uyz9b8ikGDhqixvPz8vDU44+id/cuuObKSxEfH4cd//1X7rFs3rQRHTp0RIeOJcdyb/8HcDw5GUmJiWq6RcuWuLhHDzXeo2dPHD1yRI1379FTxYk/O2kCfv7xB9VxUJ+g6CaEEEIIIfWSyiT18/P0w67hu2DIaIzJvZKtlrHsXcWQTozu3burQcZrE2cpYyidDzJIVvd///0Xbdu2NVu9BbEov/3228rKLcOePXvw/Y8/IafAoJKh+fj6WcVrDxw8BEs//RRHjx7BkcOHcc1116v5L734PJo0CcLf6zYpq/Sll12u3MzPF2+vsy79ck2NZ2LLe/Tshb/Xb0K37t3x4w/f4arLesMouRTqCRTdhBBCCCGkXlLRpH5asq2WUS1xdPJGzF7bzGp54oQJdi3kpG55PFSUXr16KRdxcSEXli1bhoiICDUcO3YMq1eXJCn7+uuvERISokodS0y3jA8ePBjTp0/H+vXr1Wf69euHOXPmqFhsg7EYqacysXX7jlLLfd1y6+3YunUzZs+Yjnvvvx96fUnF6NOn0xEeEaGmDx7YjzWr/jB/p0FAADIzMuyuTyzWu3fvwp7dJcfy9VdfqERrsq6yOHbsKPz9/XHnXfdg+qw3cejgQRWvXl+o03W6CSGEEEIIKa8koWVSP60koW1SP8tkWxGzZyF57DiE5JpQ5GbCjiZ5aJqrR1RCImKHDEHM0qUlSdqYHNAlPR7Ewq0E9xmPB1Wy8jxczIODg7F06VIloLVEal999ZWK9ZYEcgsXLsTIkSNV6TRxFxdLtwhwcSOXeRLvPm/ePLUuifuWZHHde/Qwu+SPHvs0LmhfkojOFknOdsedd+PjD+dh07Yd5vnjJ07Gow8Nw+dLF6N58xa4/Iorzctuue12LPtsKS7tebE5kZqGxIN/PH8RHn1ouDmR2qIlJftcFv/89RfefftN6Nx1MBgNeOX1N9CwYUPUF5i9vByYvZyQ6oFZdYmrwTZLXA222WoQZgMGmut7e4Q0Rd72/1S5sTi/QqT4FqHzCV9sDyiC+6vP47a2lyH1wceYzbwMRICKuBQksZhk3q6r2curAlX2y1isSn6hHtSf0rm7wc/LOWzEpbUrR7WicxwFIYQQQgghToyWbEuziBoSEtR8fWQEmkx+GEFTZsPDlIlQAzDp+8cRui4CETklr9q25fHIWZyhenFFPB5qC3Elzy8yqizkxPWg6CaEEEIIIaQCybYku7VGxPTpaN21K4ouuAzb770NUSeApStj1LJENyN8nngcrSi47SKJtqT+tDZemwK8tNKRNU1xsQlFxmIYLc6FjJYWs01cAyZSI4QQQggh5DyTbYkgbzl7rtWy167ORELzAuQW5Sr3dHFjJmeROGBfX1811GbJsNpCOhlEZIsVu9BQrDKQZxcYlPu4wWgyDxTcrg9FNyGEEEIIIedZXix3506cenaK1XeeXeuHMUsHotcrLXHk/vtV3HBxTs45pcgoyOum23ppSD3tvEIjsvJLRHZuoVG5jlNc1932RPdyQgghhBBCHCwvJmjJtrQYb4/oaCSNGYuihATopa6zwQBDaioicj2xdEUMTG4mFOclId/NHQkjR6Hg6FHELJgPz5gYp0zYVZPJ/vbv36/GpR51VeDh4aH+SkktHx8fOBti0S4wGK1qaRPnp/CMl0pl68lTdBNCCCGEEHIeybZ0fn5IHD9B/KWVEBeODhgAY/JxhOaXiMBE30K82SkBs7YVofh4Co7c3g9RH8xD8nPPK2t5fUy4JqJ7+/btarx169aVzlhuiYiiRo0aqSzoQm26rhvPuI4rN/IzsdnObIF3Ntzd3aAz6Z2inaalpam2pNU5ryi1fxSEEEIIIYS4AGUl27IV5JGzZlklXNOb3JBUFIuEzCKEwwOm/HzEDRlasg5xVxfreT2r7S1iWEowaeNVRWhoqPqrCe/aQMS2gcnPzgs3Nzd46Z0jGlo6hKKjoyvdTmtUdKenp2PFihVITExU0+Hh4bj++utVgXhCCCGEEELqgiC3l3AtJM/DnNU8xceAkLyzr+FNxz/ttLWhqxOxSvfq1cvKolgViDAKCwtT9b6LiopQk+QXGnE4LQsZeQZmzzpP/L31aBfZCM6Ap6fneXli1Jjo/uSTTzBjxgzcdNNNSmwLGzZswEsvvYSnn34aDz74YE3tCiGEEEIIITWScE1KjCWMGgVj2gmLT1m7GCeOGQvMmY3UGTPrrat5dYn6ysbgVhTJQh57KhfHTuTAWOwGuJeEFZDzQOcBb2/vOnEKa0x0T58+HVu3boWfn5/V/FdeeQVdu3al6CaEEEIIIXUu4Zrgpvc4x+qd5FuEN7qmYNa/EfAwGpE4ctQ5rubENTiZXYD9x7NUFnJC7FFjTvLi5pGVlXXOfJlXH+vyEUIIIYSQuptwTRKsQa9XVm9DcrLKah71ycdwO2O5MxlN2B6Uh3F9SsIuNcQyLq7m9QGDwYCvv/5aDTLuamTlF2FbXDq2xZ2m4CbOYemeOXMmrrjiCnTs2BERERFqXkJCAnbv3o1Zs2bV1G4QQgghhBBSI/HdpsLCc8qMtfj+O8QOG4b0jNPw+cINkwNL3os1YsePQ6vFS+uN8K7pmOuqILfQgCNpOUjJzGfpL+IQbqZqzlsvluwGDRqocaPRiI0bNyIpKUlNS2x3jx49aizWojJkZmaiYcOGyMjIQEBAAJwVSTwhGRolYURVlFsgpLphmyWuBtsscTXYZp0DEd628dkS923ML8Cuu+6CX1YW4vwLMblXMqauC0N0jicKQwJR+PaL6HDBpfBIz66z2cxFhmRnZ6txf39/Ne2s77N5hUYlsmXIync9q7wrEuDjgR7NA+HMOKoVq93Sfdlll+G3335TqftFXPfu3bu6N0kIIYQQQohTlxnTFxYipHt3ZOzaifGXHsQ+fR4SGxgQluuBTYjD419fj27erfDpqhh4t2mLiBnTYczJOUe8iyAX7Al7ZxfrEmKqGecEZ6xhLQnSjpzIQezJHFq1SaWp9i6kLl26oGfPnti3b5/V/O3bt6tM5oQQQgghhNTX2O+2X3+N9S8dxr93L0ennKbwMLkhOtsTF53wwctfF8OQkIj8AweQMHIUjvW/H4Wxscp6nrdzJ2IHDET8yFFWywRVemzAQCSMHqM+SypHToEBm46dUhnJnbA/gLgQ1S66FyxYgKFDh+LSSy/FP//8gwMHDuDee+9Ft27dnNqtnBBCCCGEkJqwgvt6+KL7hdehwzffIL9RI0TleCoLd2SuJ5J9DVgxqBPyjxyGISkJh2+7XSVnE5Et5cUKDhxA/t69atmR2/shZ8MGc8mygoMHlQXcmUMgRBvIUFU1uquCzPwiHEzJwoajJ+lKTlwnkZrU4vby8sK1116r4rqvvvpqrFu3TsVzE0IIIYQQQgDf6Gi0fe9dxD4wwHw63E0mfLJpHi7IjECYvLoXFCBv27aShTodQiZNRMrUN9SkKT8fcUOGukzpMRHaW7ZsUeMtWrSo1ThucSM/djIHxzPymYmcuJ7oTklJweuvv46PPvoI7du3V27mYvmm4CaEEEIIIeQs4haeNHHSOTW9l66MUeMnfE0IyrUotWtR31vXtCmMqakuVXpMYrqjoqLM47Vp2d6VmIHcAtbZJtVDtXcnNW/eHH/99Re++uor1ZP1zTff4JFHHsGMGTOqe9OEEEIIIYS4BJL4THMLV1bqz5ZCFxxk9RmjqfSs2baSVcS7iHhnRkJNJQRVhtoIO5XEbUdP5GDzsVMU3MS1Rff8+fOxbds23HzzzWr6hhtuwOrVqzFnzhw88cQT1b15QgghhBBCnB7JNC41vc1u4aGhcNN7nGP1TvItwshLE1DkZp3ZyyDZysPClFiXdYh4VyLewvpNznIyuwAbjp7C4dRsOFE4OamjVLvo7t+//znzunbtirVr12LVqlXVvXlCCCGEEEJcJpt5zNIlgF6vBLMhORn68HBEffIxij1KBLib0YSJW0uynBvcgdx20XDz9javRx8SgojZs5TwFhEvYl6EN7OYl5BbaMD2+NPYFnca2ay3TepSIjV7NGvWTAlvQgghhBBCyNls5iKQRTALyuodHo7WP/2II4OHID07C/ne+YCuEKN6x+Ngo3246KIoLP2rJbzatkXKK6+i4PBhhM+eBZ+2bWE4cUIJeFmfiPqqrtst+1rZGuEGgwE//fSTGr/llluqNZGawVisXMnj03Np2Sb1R3QLjRs3rs3NE0IIIYQQ4rRWb0sx6xkTg5ZffYm2jRqhoLAQf+z8DntWDFLLNiMeP44ZhJHdHkfs0GHKtTxp7DiVTE3FdsfHq8/ZiuOqENxSC1xKk2mdA6pGeAVEfl5eHqqbpNN5OJSajUID/chJPRTdhBBCCCGEkNKt3pZo096enrjq4jsRtHIMThSfUPOe3vY6cht64/o3RqPxxNklMd1nSo9VV/kwEfEiuLX48YqKfEmeJvmetHFJbFbVcdsitrPoRk5qmdorhkcIIYQQQgipFL4evoidFIunfJ4CNpTMe2HNC+j5080Y1yPO6rPVVT5MBLUS81ritgcGnM2+7oDIlzJh4vkqQ1WWDMvIK8LWuHQVt03BTZwBim5CCCGEEEJcVHjPnTAXEy+cCJxJlRSWo8cTK4w1Vj5MxLyIektqo0a4JEg7kpaNtYdPYNPRUziVXVij2yekLCi6CSGEEEIIcWGmTp2KZ/o8g7bFwViwKhrR2Z44HeiN6KVLqr18mIh5EfWVEfnFxcU4cuSIGmS8MuQXGbEzIQNrD53EkbQc1tsmTglFNyGEEEIIIS6MuGa/9uJr2DLxIEwxUYjzL8Rdl+7C4EOvI/jj963Kh1UlIuKVmNdcyitYI1yE9oYNG9RQUdFdXGxC7MkcrDtyEimZ+ed5JIRUL0ykRgghhBBCSB3Az7ch5hX4Y39gDpL9DPh237dYdWwVYhduR4PgsCovFyYi3ra0mfzVspeXJ/KlsyAsLMw87iincwuxNzkLOQWG8zwCQmoGim5CCCGEEELqCDPffBNdb+1qnj6dfxpjNr+MT27/pEZKmynhvXSJQ3W6JWP5lVdeaZ4uz9pdZCxW2cgT06u/zBghVUm9cS8vKChA586dVS/a9u3ba3t3CCGEEEIIqXJatWqFZXOXASfPzpu/fT5e/etVbEzciNyi3BopbVae4JYa37bu54a0NJiKis75bF6hUSVJW3f4JAU3cUnqjeieMGECwms4iyIhhBBCCCE1zU3X3oR5nefB7zM/PNrmUTXv+dXPo+fHPdF5XucqF94VRQR3wugxiB0w0JxwTf7GDRyEnClT1HJBYrW3xJ7Cv4dOqCRphYbKJVsjpLapF+7lv/76K1asWIFvvvlGjRNCCCGEEFKXeXT4oxh8/2B463Tw+CIb7xxZquYfPHUQu/b8je4XXFXlMd6OIu7oBQcPqoRre+++Byn9+6P5Dz+gKCEB7kYjMlNP4ojJB+k551q9CXFF6rylOyUlBQ8//DAWL14MX1/f2t4dQgghhBBCagQR3GJRHrnoOFrlNzLX8TaOmIidjw5FTm5GrVwJcT9XidciI+Fx6hQi33tPCW59ZCRyXnodW7J1FNykTlGnLd0mkwlDhw7FiBEjcPHFF+PYsWMOxX7LoJGZmWlO7FDZ+oE1geybHK8z7yMhlrDNEleDbZa4GmyzxJCerizKhoRE/Ixu+PgqX1y9ajcaZRcgbucGPPTWxVg1dht8PWreMKULDUXo1NcRP2iweV7yYxOQ4xkAXbEJFUhmTuooJhfQNo7un0uK7kmTJmHatGllfmbv3r3KpTwrKwuTJ092eN1Tp07FSy+9dM78tLQ05OfnO/UFz8jIUI3T3b3OOzCQOgDbLHE12GaJq8E2SwTfmTOQNWYsjAmJGLZY5niqOt7D+sYhudCA77Z/h2tirqn59pmSgqyJk6zmNXrrNeSOmQRjBOAGqu76ToFBh9RU5w4xEK3pCG4mUWkuhgjgkyctUjLaoUWLFrj33nvx448/WtX9MxqNqjzBgAEDsGjRIocs3VFRUUhPT0dAQACc+cEq5yU4OJiim7gEbLPE1WCbJa4G2yzRyN26FfEDB5mnR96SjZUNEtR4VI4/nu37Bi7q0A0dm3Y8L6u3JEAzZmRAHxxslZFc17ChVfy4zJOkaRLT7R4RieOPT0Dg3DegT0lCUXAIUl9/B6bAIF7Aek6Ajwe6N2sMZ0a0YuPGjZXxsyyt6JKWbhGWMpTH3Llz8eqrr5qnk5KScP311+OLL75Az5497X7Hy8tLDbaI9djZLcjSueAK+0mIBtsscTXYZomrwTZLJCv48cnPWJ2Idze3xN6XXsTMf2bhuVWFOLRlKvr2SkRbnxj8O3GPWXhLSS9H6m1rgjtx7Djlzq7itcPDSzKSDxkKr9atVT1vbT0ejRureULuC1Nw0uQL40uzEPziOBSFR8DUoKGV0YxULe6GPPhnHEB2wzYo1vtYTQuVWmYqRoPT+5AT0FKN+2UeRE5AKxTrfeFuLIBf5mH1OZObO/wzDzm0fp0xH+5J24CmFwCezpmby1Hd5ZKi21Gio6Otpv39/dXfli1bIjIyspb2ihBCCCGEkOpHRHPskKHKoqyX0rkGAwypqTAeT8EFzy/GJ7owGLOTARPw1r+RiM7W4Z7Mq/Dsk3PQESFIffCxcwSzIxnJZZvh095A4oSJMCQkmJdLAjVNyMs6C06exBcr/0BGngH+va9F6mtzUeReDJ2HR5WdgyoRlFW1rDAbDTL2Ide/GdxMxfDP2I9c/2jAXQ9dUTb8Mg8h3y8SbsUG+GYdVeNAMXyz45DnEwI3Nx3cDbnwyYlHvk+ouCzDOzcJ+X4RKNZ7A0YDfHLiUOgVBJ2xAN65CTB4liTQ0xly4JWbDJObB0ISfoWHIRsGnQ8yG1+Ahqd2QVdciGLopPsE7ihW/xfp/dXn3NS0dIKI03/JMqO7F3TF+XCDSZpPhYMBTGrQqfVp6yhZvwnFbu4o9GwCz8JTgMkIBLYERvzjtMIb9V10E0IIIYQQUl8RcatZlMX6LBwbMBCG5GQYU1NLPhMZgTe678ek3z0Rne2JkatO4MncazBtfQQis/VIz0qHX3I8AmNami3amoDWECFd6O+F9Glj0GjCrBLh/cCAkoU6HQJffqlEcCclKUGuCXmZF9DQH1m5+1FYkA6fBn5oeHwL8nw84eauh74gE/6Z+5HnH6WEqH/GQRSI+BR5WJAB3+xYFHoHw624CD45CSjyagz34gJ45yTD4Bmg5kcc/RoeRZko0vnBzc0EvSFXic3TTS5E45P/KWuq0c1TqUYlPN09kOcTCp/cJLibjEoSulmIzULPRvAoyjizTCfuJHA3GdT3shu0hF/WUeiKC84sg/qcCFYZRMw6E3pjHgJPbDVPu8NoHpfj9TRkWkyXyGRtmb44z2JZxXFTg+X2YF6/u6kY3gVpZz986jCQuheI7AZXxSVjumvaT79hw4bl+uk7Q9xWamoqmjZtSvdy4hKwzRJXg22WuBpss8SeSJb4brMgBhDx7jvQX94b/+34A+4jX0TAybNiKs6vEMOujkOKrhgjmo3AgGvug27KHPglnETCqw9jszEWutR0XD5rJXb4ncaoPnHonN4Ai36PsDr5Kf5u+OWBVrjrm0QEnMzF6UBvLBlxAUKz9+KZ1MNn7J2aEKt/GN100IlFtxxyfcLhm5dkd1mOXxT8cuLtLktv0hWNT54V10X6BvAwZKHAqwkOdRiNlnvehnd+KvK8Q1Ungk9eMvJ8w7Gr5yx03DBOdUBIR4RcHW3Zjt5v4cJ1o+GTm4hc33Al2X1zE5DrG6nW4ZsTj1y/aGy5fCG6/TUUvjlxyPWVduFW8jm/SPx3ybvo/M/jah15PmHq4vvklqx/T/ep6LhpkrLOO7Ol21GtSNFdRSeytuGDlbgabLPE1WCbJa4G2yyxRbM0iyXajE6HmCWL4dulC9J+/REnxkwwLxpwTSy2BZ8V4UF5OixZGaMs4pIBfXKvZExdH2aeHtsnEbPXRqhp8zbdTPAwnZXS5szpfgZMMHliGrwrfKHyvZsqkVgexyOuR5PUdcrSXejRUIlBz8LTSmweaf84Wux9H175J5DvFaSWeeenId8nBHu7TkG7rS+XCEyfM0L0jBjc1WM6Om4cXzLtHaoswN75Kcj3DsH+iyag7X/T1L7JtFpn3nHk+UZg+6Xv46J/nygRo0qYuishmuPfDJv6LkP3Vf3hl30MOX7RquNBLbMcL+tzFVl2Zlr2o7pc6atiHcV6HzTyMOBin+NOHdNN0V3DJ7K24YOVuBpss8TVYJslrgbbLDknvnvAQCW4PaKi0HTSRCSOHCWlfZTwDpnyIlKmvFQyfYbkgGIMvPIIcgJ90UTfBEdzjiIsR48Fq6KthHWyTzGevzoDL6zxU/MTGxgxoWcCpm+IRESWxAnDrpB/qduTGLplMaJNJsTDDU18Q+Gbm4xcvyhsvmIJuv01GH7ZsY4LTJvPbbjue7Udp4nprkUBa5s8zVWyl/doHghnhqK7hk9kbcMHK3E12GaJq8E2S1wNtlli62aeMHqMVXbx3G3bECulxCyEtgjwiDmzkTpjphLoxWHBiFq6WJUB6zyvMw6eOojrs2Iw56ezwi3k00/g1bEDDj/5GHziUhEy/wPs051Eq2QjUoY8bLX+RH8jBvc9iuR8Azr+3REvPP8iklMOoUX3W+Dj4QfflK3IDekKk4dvlYhU4roEUHTXHyi6Cake+DJIXA22WeJqsM0SW+wlQcv84w8kPvGkeTpi7lsIuO66c5KeSfby3KJc7N75p0qWZkhINH9HLOci5PVBQVZZyjXLuiRryxw/FAEzFqrvGcOCcWLsGJwqMiErJ1dlL2/X+1q4uetgzDkNnV8jlgwjqEuim9nLCSGEEEIIqQeIcLbKOp6UhNQ3pll9Rizc3m3bws3HBzFLl6gM6ELenr3waBiAxhPnoCghUZUgC5/6OpKfe95cJkw+r63fNnO6qtvdqa/6nH/r1uhw/c2Ahwf2HY3H/uNZSnATUleh6CaEEEIIIaQe1/AWS7XU1U6aOElNH7m9H3SBgWi2ZLH6bPyIx5CzYQN8uneHV8uWMBkMgMmEU4s+RfTHHyHuoYeVwNYEuibwxUKuWb7Fyg693izkZbnsQ1DjQCTme6nvsKgSqau41/YOEEIIIYQQQmoWzRKtuYb7du1a4iIeLuWfAMMZ9/Ksv/9WglvisvM2bULAHf1UVm6p9S3x4ZpFXHNBt2dZ1+LJxd0cBkOJ4Jb1DxiInMkTEOTtLqskpM5CSzchhBBCCCH1DFtLtCAu4M2WfQ5TXp6yXovV2xzvrdMp4Z00anTJZ8+IdUt39dKQbYhA19zQLa3qBoMBTbKT0bbdBUjJzMfR+EwYqvfQCalxaOkmhBBCCCGkHmIb4y3ItGdMjBLGloQ896zVtCwXke4Isk4l0KOiSoT3AwNK3NojI7Hrvnvxz+7d8HB3Q3SgH9qFNsCFEQFo4E3bIKk7UHQTQgghhBBCzIjrt1iiLUl59TWraWWpTkpy+KyJQLcV8qFTX0eD5s0RGBhozlYuf4MDvNGzRRN0jWmMkABvuFOxEBeHTZgQQgghhBBiN8FaxLvvmF3LVQ3vd985a7GWz6WmVlrIH3/mWfTt1AnXX389dLINGwL9PNEpsiEuax2MtqEN4E/rN3FRKLoJIYQQQgghdhOsNbjsMvj17KkEt2/PnmpacxW3zVjuqJCP+WxphYS7h84dUYG+6NWiCS5uVmL9ZuI14kowWIIQQgghhBBSaoK1qHnvI//QYXi3alkSBx4eblX6q1zBYadmt/wVwe2ocNdo5Ouphvwif8SezEXi6VwUF/PiEeeGopsQQgghhBBSaoI1mfZpf4HVGXIka3l5mdKjFn+Kv7Ztw74//0Tfvn3Ncd2O4O2hUy7nMU18EXcqF4npeTAWm3gViVNC0U0IIYQQQgip8UzpuqAgnDh9Wo2bTKYKiW5L8d0mpAGaB/nheEY+EtLzkFPAomPEuaDoJoQQQgghhNQ47u7uuOyyy8zj54MW9y1Dek4hjpzIRnpOURXtKSHnB0U3IYQQQgghpMYRoR0ZGWmeLq6i4OzGfp7o5heIUyK+07JxOpfim9QuFN2EEEIIIYSQOoeUHAv0C8SJ7AIcTs1GVj7dzkntQNFNCCGEEEIIqXEkjjv1TLmwphVIzFZRgvy91JCSma/Ed26hsdq2RYg9WKebEEIIIYQQUuMYjUasWrVKDTJe3Uh9b6n1Hd3El3W+SY1CSzchhBBCCCGkVggICKjR7bm7u6ls58H+XtiTnIk8Wr1JDUDRTQghhBBCCKlx9Ho9br755ipPpOZosrWezQNx5EQO4k/lwsQS36QaoXs5IYQQQgghpN6h17krq3fPFk3Q2M+jtneH1GFo6SaEEEIIIYTUW/y99OgWU5LlPDWzQP0tNNSc1Z3UfSi6CSGEEEIIITWOJE/7888/1fgVV1wBNze3Wr0KWpZzyaqekVeEhPQ8pGblowa93kkdhaKbEEIIIYQQUuOIuE1JSTGP17bo1pD9aOTrqYb8In8V851wOg9GIwO/SeWg6CaEEEIIIYTUOO7u7ujdu7d53Bnx9tChdUgDNAvyU+I77lQuDBTfpIJQdBNCCCGEEEJqHBHazZo1q5Xs5RXFQ+eOFsH+iA70VW7nR0/kwFhMyzdxDOfsUiKEEEIIIYQQJ8x4LlbvHs0DEeDDjOfEMSi6CSGEEEIIITWOxHGfPHlSDTLuSvh56dG9WWM0D/aDk4SiEyeGopsQQgghhBBSK9nLV6xYoQYZdzUk4VrLYH9l9W7oS6s3KR2KbkIIIYQQQkit4OvrqwZXpoG3B7o3C8QF4QHw0FNekXNhIjVCCCGEEEJIjaPX63H77be7RCI1R4ho5IOQBl44kV2IE9kFamCmcyJQdBNCCCGEEEJIFSVaC23orQaJUz+VU4jkjHykZRUw23k9hqKbEEIIIYQQQqoh5ruJv5caDMZipGQVqFrf2fkGnut6BkU3IYQQQgghpMaR5Gn//vuvGr/kkkuUSK3LFnBxP5chPacQcSK+Cwzw0rvD20MHdzc3HM/Mg4t72JNSoOgmhBBCCCGE1Djifp2YmGger8ui25LGfp5qsKV5kB8OpmYhNbOgVvaLVB8U3YQQQgghhJAax93dHd27dzeP13d8PHW4MLKRigM/djJHWcRdrHw5KQWKbkIIIYQQQkiNI0K7VatWdSZ7eVUR6OephgKDESkZBTiemY/MvKLa3i1yHtSLLqWff/4ZPXv2hI+PDxo3box+/frV9i4RQgghhBBCSKl46XWIbuKLHs0DcWnrIFUHPKiBF2uBuyB13tL9zTff4OGHH8brr7+Ovn37wmAwYNeuXbW9W4QQQgghhNRrJI47MzNTjQcEBNT27jg1kmxNS8QmiBVcsqBn5htwPCMfOQXMiO7M1GnRLQJ71KhRmDFjBh588EHz/Pbt29fqfhFCCCGEEFLfkezlv/zyixq/5557GNddQSu4l79OlSOTBGyZ+UVIPp2Pk9kFyC00VtclI5WkTovurVu3qoyIEi/SpUsXHD9+HJ07d1YivGPHjna/U1BQoAYNrfdNYkycOc5E9k16C515HwmxhG2WuBpss8TVYJslrtBGPT1Lsnhr77B8n60c/p46tG7qp4ZCQzEy8gpxIrtQxYO7qjwwuYC2cXT/6rToPnLkiPo7ZcoUzJ49G82aNcOsWbNw5ZVX4sCBAwgMDDznO1OnTsVLL710zvy0tDTk5+fDmS94RkaGapzM/khcAbZZ4mqwzRJXg22WuAJSn1s4deoU22wV00QHNGhQjJTMfKTnFrqc+C4w6JCa6twJ5LKysuqu6J40aRKmTZtW5mf27t1r7nl49tlncdddd6nxBQsWIDIyEl999RUeffTRc743efJkjB071srSHRUVheDgYKeONZFjldqGsp8U3cQVYJslrgbbLHE12GaJq8E2Wz1EhpfEgBcZTdC7u6kht8iI/ceznTorupePB5o2bQxnxtvbu+6K7nHjxmHo0KFlfqZFixZITk4+J4bby8tLLYuLi7P7PVkugy0iZJ1dzIrodoX9JESDbZa4GmyzxNVgmyWuBtts9eDj6Y6SFGwleHro0aO5J+JP5eHwiWwYjSanbQvOjKP755KiW6y5MpRHt27dlIDev38/Lr30UjWvqKgIx44dQ0xMTA3sKSGEEEIIIaS0RGobNmxQ41LeV0QWqTnkfEtJspCGXsjILUJ2gUElYZOkbLkFTMZWlbik6HYUcQcfMWIEXnzxReUiLkJbkqhpGRIJIYQQQgghtYPkIoqNjVXjPXr0oOiuxUzoTQN0aGoxr8hYjNO5RSohW3JGPgqKXCwg3Mmo06JbEJGt1+sxaNAg5OXlqV60VatWoXFj544PIIQQQgghpC6jVRjSxonz4KFzR3ADLzW0CPJHalYBEtJzkVVggK+HDn5eeujc3ZCckedyCdpqgzovuj08PDBz5kw1EEIIIYQQQpwDEdrt2rUzTzt7eaj6iru7G0IbeqvBlqhAX+xJynTqhGzOALuUCCGEEEIIIYRUGH8vPbo3a4yWTf2V5ZvUU0s3IYQQQgghxDljunNzc9W4r69vbe8OOY+EbM2D/BDV2AcpWQVIOp2nErORs1B0E0IIIYQQQmole/kPP/xgTnLMuG7XRq9zR0QjHzVk5Rch8XSeSsJmtChHJgnqxSJucMISZdUJRTchhBBCCCGkVtDpdDzzdZAG3h5oF+qBVsH+Sngbik1o5OOBAB8PtXz/8SxlEa8vUHQTQgghhBBCahypMHTvvfeap5lIrW5av6MCzw0daB8eoDKj703ORKGh7ifQo+gmhBBCCCGEEFKjBDfwQkOfJsgtNKi4cMnDJm7nxzPzkZKZX6euBkU3IYQQQgghhJAax1PvDk+9p9W8xn6eaBvSAJn5dScZG0U3IYQQQgghpFYSqW3evFmNX3zxxcraSYhWG7yRr7UYd2VYp5sQQgghhBBSKyXDjhw5ogYZJ6SuQks3IYQQQgghpMaREmEXXniheZyQugpFNyGEEEIIIaTGEaHdoUMH8zSzl5O6CruUCCGEEEIIIYSQaoKWbkIIIYQQQkiNI3HcBQUFatzLy4tXgNRZaOkmhBBCCCGE1Er28v/9739qkHFC6iq0dJeDlkkxMzMTzozEwGRlZcHb25uJKIhLwDZLXA22WeJqsM0SZ8dgMCA3N9f8ri0x3nyfJa6EphHLy77vZmJ+/jJJSEhAVFRUVV4bQgghhBBCCCF1hPj4eERGRpa6nKLbgV7ipKQkNGjQAG5ubnDmXhbpHJALHhAQUNu7Q0i5sM0SV4NtlrgabLPE1WCbJa6G2K/FOyM8PLxMb2O6l5eDnLyyei2cDRHcFN3ElWCbJa4G2yxxNdhmiavBNktciYYNG5b7GSZSI4QQQgghhBBCqgmKbkIIIYQQQgghpJqg6K4jSG3DF198kTUOicvANktcDbZZ4mqwzRJXg22W1FWYSI0QQgghhBBCCKkmaOkmhBBCCCGEEEKqCYpuQgghhBBCCCGkmqDoJoQQQgghhBBCqgmKbkIIIYQQQgghpJqg6CaEEEIIIYQQQqoJim5CCCGEEEIIIaSaoOgmhBBCCCGEEEKqCYpuQgghhBBCCCGkmqDoJoQQQgghhBBCqgmKbkIIIYQQQgghpJqg6CaEEEIIIYQQQqoJim5CCCGEEEIIIaSa0FfXiusKxcXFSEpKQoMGDeDm5lbbu0MIIYQQQgghxAkwmUzIyspCeHg43N1Lt2dTdJeDCO6oqKiqvj6EEEIIIYQQQuoA8fHxiIyMLHU5RXc5iIVbO5EBAQFwZot8WloagoODy+xlIcRZYJslrgbbLHE12GaJq8E2S1yNzMxMZaDVNGNpUHSXg+ZSLoLb2UV3fn6+2keKbuIKsM0SV4NtlrgabLPEFVxzU1JS1HhISIia5vsscUXKC0OmSZQQQgghhBBS4xiNRqxevVoNMk5IXYWWbkIIIYQQQkitWAcbNWpkHq9qRMgXFRVV+XpJ/cPT0/O8vIkpugkhhBBCCCE1jk6nw4033mgVElEViJv68ePHcfr06SpZHyHu7u5o3ry5Et+VgaKbEEIIIYQQUmfQBHfTpk3h6+vLsr+kSkpIJycnIzo6ulLtiaKbEEIIIYQQUicQl3JNcDdp0qS2d4fUEYKDg5XwNhgM8PDwqPD3mUiNEEIIqSays7Px5ptvqgc1IYSQcwXyypUr1VBVidS0GG6xcBNSVWhu5ZVtpxTdhBBCSDWxa9cujBkzxlwShxBCiHXsdVpamhpkvCqpjsRspP5yvu2J7uWEEEJINbFjxw71d9myZejSpQvPMyGE2CSnuuSSS8zj1cnKPdXT+XlN+xCHPvfbb7/hueeeQ2FhobLCf/DBB7jooovUstTUVAwePBiHDx+Gl5cX3nvvPVx++eVq2aOPPoq1a9cq9+b//e9/aNiwoeqguOmmm/DOO++gZcuW530MQ4cORefOnTF69GhMmTJFueeLl1ZlWbNmjaq3fsMNN9hd/sILL6Bt27YYMGBAmevZvn079u3bh/79+6OmBXZ6ero5s35VQEs3IYQQUk3s3LlT/T106BDPMakWPvvsM9WpQ4grIkJbElPJUN2iuzYRAScCc9GiRaozdsaMGVaCc9KkSejVqxcOHjyIBQsW4IEHHlBu8uItJfPkWXLllVdi8eLF6vMff/wxrrrqKocFt8Qhi1A3Fhej0FCM/CIjcgsN5sFQXAxjcdV5Gojolk6G0nj55ZfLFdya6K7s/U2O2Zmou62bEEIIcRLRLVYMQqqD+fPn45tvvuHJJcSJEQu2JHXr0KGDmr7ssssQFxeHrVu3qukvv/wSI0aMUOPdu3dHeHg4/vzzT5Wwq6CgQGXPzsnJUXHFkkH7888/x9ixY8vcpoj0kSNHonfv3rj22uuQkVuAMePGo/NFndC184UYM2oUcvMKYDCaIJ79RcZiZBcYrMS3xC+PHz8eHTt2VMNTTz2lLPWaddzSGv70008rK7kI5Xnz5mHp0qXKei4C2xbL78p37rvvPtx6661o3749+vbti1OnTqnnpljEV69erdajnZ9Nmzapz1x88cXKg+yrr75S848dO6Ys0xMnTkTXrl0xffp0dc4lk72GbEtCvrT9lXMt6xavgv3796M6cTnR/e6776JZs2bw9vZGz549sXHjxlI/u3v3btx1113q8+ImcD5uEoQQQkhF6datm3roM6abVBeJiYn4+uuvleWLEFejOmO6nYnWrVvj5MmTyk1c+OGHH5CVlYXDR46o+WLVDg0NNX9etIuIcnHBFou2iMgjR45g4MCBSjSKpVyvLz9KWITkr7+vwve/LMf8jz/Gti2b8ee/G/DP+s04euQI3n37LavPFxebYDAWKwEuw4cffqhE7pYtW5SYls6DOXPmlLlNTSCLJVu+I8K5PDZs2ICFCxdiz549Kuu8uN7LXxHscvyakBe390ceeUQJ+s2bN+P333/HuHHj1H1QyMjIUB0b0pnxzDPPKB24ZMkStUzal3gaDB8+XE2LOJdjk3U//vjjGDVqFKoTlxLdX3zxherVefHFF9XJlDiI66+/vlQLQm5uLlq0aIE33njDqiETQgghNcGsWbMwYcIEWrpJtaF16Pz66688y8TlqI7s5baI2CquIUEv2xHRatuBIHHY0jk2efJk1Rm7fPlytLvgAhhN7sgpKHGDlu9o+2o6s568QgMmPvci/lq3CfMXf4affvkN4RERCI+MxuAhQ9HvjjuxeOnn6nOyHhmy84uQmV+kLNZ333c/3HUl4nzN6j/wwMDBKmZcBPuQYcOxetUfdo9Ddj+v0IjfVvyOQYMHQ+/hCTd3HR588CEldKuaG264wVzeTSzzIu7tIZ0W0vlw4403KnF/zTXXqPmalVo8A6RjQmPYsGHKXV8d/5o1ahudOnVS03Icsi2x4Iu4F/FdnbhUIrXZs2fj4YcfVidQkB6Pn3/+WblWSSyELeIy8P/2zgK+qasN40/dgFLc3d1dB0MGDBkwGGPY4BvOkMGAMWAMxtgYtjHcbbjb0OEMd3f3Cm2pfb/nZDdN07RNSyVJ3z+/0MhNcnNzcu557Xl5IaYeFwRBEISENIbYMqxZs2YqZY6LKFHTFeITChWxVjR37tzYvXu3MlocHBzkIAtWRYoUKeL9NTnfsnY5lPXKIToD2D8oWM3BjDjynjDNulT32YEl5dSntre3A/+Z9T4IUxFhGsg0crXMbApdOzvYw8nRHvb/qV4zYssLt3/t+xZLcmVHwcKFkdorjTKCb9y5jwwZdUHCm7duI2OWbAj6b9/JmzfemPTbRKzZsBkTJ4xH5arV8Wnbz1C1YlnUa9gIbm5ukfbPwyPqY2vO+Uh3HMP0joHA4FAEh+pu0wgPCtLVivO1OB/F9F2Ghf13vEJDERQcomrLed3J2UXdzz2ys7fHu6Cg/45paKTnM5KtZQwYwvRyCtQZagPQqGZqPjOjGUnXbEhmEfTq1UtFulkXzzp7TbgOyT3SzfoBpjZoHg3Cg8rbhw8fTtJ9EwRBEARjFi5cqDKy8uXLh8aNG4vBLcQ7rPVkqiSzAJl2yXRLQbAmaGyylpcXc9KlNaONUVhGc5/6BODOCz9cfuyNU3df4fCNFzh664UyDt8Fh+gNbt1z/0ufDqWgWJi6Hqq/TycwxufxtQOCghEYHKIuAcEhymDnhbeDlOiYLv2aRiO3p3FsqEPG9+Jr+Qboos8UK7t5576KSHP7n8f+iBo1ayFv3nxq+2YtPsGcWTPV9RP//otHDx+iWvWIRuDI74bhm2+HKcOS2by0mWnsMjVdq7OOjlq162DZ0sVqW4qMLZw/Dx/UqRun5/D45cydB4ePHoVPQDDuPXyCzVu2qGPJx1KmTKlSvfXp6sG6qD23DQhiJgDAr4bHPCRE9xwel7fvQvAuSOfA4HU3D93raFSpUgW3bt1SmREajFBH9/lpaE+dOlUFailQR/iajIpnzpxZjSmqwCc0VhPpfv78ufLgZswYUZaftyklH58nMF40vL291V96SXixVLhvKiXFgvdREAyRMSvY+pil55xpbKzXY10c69uo0CsI8QUXtrNmzVKL4WHDhmHHjh36DL+4jFlBSGqiGrM0cK8/9cWjNwHKYIsOe77G++6HCoVHfpUQpQAeu1fXth818nscPnQAwcEhqFCxIqZN1xnZ6rEfxqLbl51QungRODs7Yebc+coo1Dhy+BAC/P31RvKX//sKXTq0x6SJv6BN23YqfT0mOnX5Erdu3USNKhXU7WrVa6JHrz5xfk7Hzl/ii3ZtUL50ceTKlQflyldQ3xOdDB9+1AQLFi5SKeBNmjbD4KHDERdq1v4A0yb/hhIlSqioNbOcN23apMTdWMtNhwPPq2ylpqX0G6f2M908Z86cqr6bQmt8nCnlFG9j1Jwp502bNo30XC3d3/i2sU1o7vxqF2YlqgUPHz5E1qxZVToBD7oGa+Wo7scC/OigIAF7z/ESHVS1GzVqVKT7r169qk5ulgq/cHpt+KOz5ZYLgu0gY1aw9THLTCwuOCh6Q5VVCr9QcVVIGKZPn446deqgQIECyeYQ06FDlV8tPZJiTR4eHvrHZZ4VrA1TY/alXyAevg6IELWODgeEIq1zMLLnyAFnF5cE3mMhIbG3s4Ork2WUzDB9/s6dO2psGjpEKIjH8w7HbapUqaw/0p0uXTpVp2SsAMvb8SmSRoEDQwl+RrqzZ8+uGtJHdyAtYZJiign3U4xuwRqQMSvY8pil9529VamyWrhwYXUf09+oxiokDHTAc0FEnZfkAluFMZjg7++PevXqRXpc5lnB0mEW64EDB9T1atWqqTlWm2f59+IjbzwOAODqAnNNL/vQICDsjao5trO3GlNHMIGdvR0cHS3D6Gb5A8/9jIyzi5aG4fVonw8rgX3pqPa3a9cuJUqjnUx4m4Xw8QUV/XgxhgfZ0o1ZJQ5hBfspCBoyZgVbHbN0CHt5eamUONbf0WnLMimZnxMOOjfYzzU5HWP2n2XggYtB9vBt3769an1jqH8j86xgyXAtr/VR1saq9vfSYx888X4Xaz0MZbhbRR6vEBNKWC2W339CoTmEjNcA5p5zrOrMxAg0a5fYY+3SpUvo3r27OsloSnRffPGFilRrMKrA4nqtwJ493Hj9+vXrSfgpBEEQBFuHNWZcSNasWVOvPyK9uhMGpvYxjZ/RBraNuXfvHpILjx49UkJAhM4dCs6y3lEQrAUaLBUrVlQXQ+PlyhNvPHrNELcg2AZWZXSz4P2XX35RTdZ5gqUBvW3bNr24GuXfeQIyrAMvXbq0uvB+PpfXv/zyyyT8FIIgCIKto8mlaB56CriwbZgQ/yxduhTnzp1Dx44d1fFmBlxyNLr52T/88MME6aErCAmFStfNnB1vXdLizkt/PPMJwIPX/rj/UgxuwbawmvRyDaaSR5VOzqbnxuJpVqITJwiCINgQNLJZ98XsLDJu3Lik3iWbhOd4Cqix3RDbs7GdjJaqmhygsE/BggX1t2l0z5kzB/fv30e2bNmSdN8EwVyoeP34TYD+Nx3iFwgHj8g9pwXBmrE6o1sQBEEQEgoKn1H9+bfffnuv1zl16hRatmypv01V02fPnqme3UL8cezYMZw5cwY//fSTuv3PP/9YTP1fYsCovmFwoWrVquovMwHF6BasAY7fN69fI8DXGy4eltslSBCSVXq5IAiCICQkjExPmjTpvWuMb9++rXp0a/z666+oXbt2POyhYMjKlStVVpum3E2Dm8rxb9++TTYHytDJwFTzv/76S7WoEwSrUS/fsxM3Tx9EWGgIbJlxY0ar7JSkYsmihbh65bJZ2/6zfx/+3rE9wfcpOSFGtyAIgiD8R5o0ad47Ffz8+fPqr6HRTe2Rp0+fSslTPPPzzz+r0jJNgIkGN9W8Z8+eDVuHafTMnDh48KD+PrZWbdWqVby2UhWEhMbF1Q2OzuwcZNtZKj+NHROl0R0cHJzg7790MY3uK2Zte4BG984dCb5PyQkxugVBEAThP4Pt5cuXqr3X5s2b43xM2J+bxo/Wn5uwPze7aDDNXIg/aGznzJlTf9vJyUm1aUsOYmoUi71x40akNqfs3b1kyZIk2y9BiA1sd1e3YWMUqPAB7B0sox9zQtCvd0/1t+GHtVGtYjk8e/oU3bt1Qc//dUXDDz9ApXKl1OOe7s54/fq1/nm5s2fGnTu31fUb16+hVfOmqFWtMqpUKIOZ0/8w+V5bN29Sj/N9+LqbN27AgnlzcerkCXw7eJC6f8e2rbhw/hzq16mF6pUroEKZEpjw01j1/LNnTmPu7Fn4a8Uyte34sWPU/TTCuX2NKhVRu3oV7N8XUUtLiB6p6RYEQRAEAL6+vkphnJHqnj17qrpYinPFFvZKZsozW1gZGt2E0e7UqVPL8Y4HfvjhB+zbtw9///13hPvZo3r8+PEqcsQFva2idWvR1Ms11q1bhzt37qBdu3ZJtGeCYJk8fvQoktBiaq/UyJUrt4pAX750KdJzSpUurf5eu3oFfn4Ry1Zy5MypsqPMYdLU3zFvzixs3bknwjng9OmT2Pb3XqRMmTLGNPzOHdpj1tz5KFCwkCqhqVurGsqWr4CyRuUkY0Z/j0lT/0CFipVUH3Rvb2/1nn8tX4ruPXuj8cdN9aVQG7ZsV447f39/fFi7Bmp9UAflK1RE5y+7KifxTxN+VdveunUTP/34A9Zs2Kwc0zduXEfDuh/g3OVrkRx/gmls92wkCIIgCLHAy8sLFy5cUBHvtWvXYsaMGfjjD9ORhKhgL26mkteoUSPC/TS6Gf1mJF2IHy5fvqyyB4ypU6cOhg8fjuPHj6Ny5co2bXSznltrm6qRPXt2JSgnCEJEaPQyxduQ1m3aYtbcBXjw4D5qVq0Y6ZC9eaubY7p3+xLHjx2N8NjMOfPwadv3c241a/5JjAa3ZvRfvnQRnb/4XH+fj48vrly+GMnorlnrAwwe2B9Nm7fAB3XqokRJXRTdmAB/fwzo1xvnzp5RWUPsenD2zBlldBvz944duHnzBj768AP9feo59+4ib778sfzUyRMxugVBEBKR5s2bK3XsxYsXy3G3QBVdGjFMUe7SpQumTJmiaoZTpEhh1vNfvHiBQoUKqed07do1wmO8nwaiVnssvD93796NkFquQRExT09Plalg60Z3+vTpI0Xzc+TIoRbPjIwlJyV3wTrhOD1x9DAevQlAlgIlYGeXcHNkpy5d0bBRk0iRbpI1azbsOxjRqDZk+szZJiPd74uH0fmFztnQkHBBucD/asB5fvLySoMDR/+N8TXHjp+ASxcv4J99+1QKe6tP26Jf/4GRthv1/XdImzYd/jl8XM0j7dq0QmBgFEJvYWGo/UEdzJm/KPYfUlCI0S0IgpCInDhxAvfu3cOiRYtkQWxhLF26FD169FDphzSax44di+XLl+PLL7806/mjR49WC8hmzZpFeozGjxhA8W90G2cUEC4e+RszJ3pkzXz++eeoVq1apPtpdHMc0ijPkiVLkuybIJgLjcnHD+/D2z8YWfIXT1AttUyZM6uLKVgOpKWSmyJ/gYLv/f6ck7y930RbYpQnb178e/wY6jVoiA3r1sLPz0///ilTpcTihQvw+Rcd1H1M8aYhbpziToXywkWKqouDoyN279oZ4f01Xr9+hYKFCqk5k5H0vbt3oWq16rptU6XCvbt39dt+8OGH+GncGJw/dxbFipdQ9504fhxly5d/7+OSXBCXuyAIQiLC6Cl58OCBHHcLg/XWNFbc3NxUBHXQoEGR6mWj4urVqyoVfejQoSr6aIrGjRvrv38hYk9zHu+yZcvik08+wc6dugVidLBem78hGpim4OKSi/nEUAROKnLnzm2yDR2zKjp06CBK+YJVwOyfoiVLI1PeIgka5bYEevXph2aNG+qF1EwxbvwvGDxogBI3o6BZmrRp1f00jFesXoeN69cqkbSKZUuid/f/qRRxUxFsPl6tUnmsWLYE3w77Tt3fsfOX+HXCeL2Q2qDB32LJogXq9UZ+Nww1atbSvwbrvs+ePaMXUsubNx9mz1uoBOGqViyL8qWL44/f5XwWG+zCeFYSooTiA0xTo5gAhQMsFQolcMHIusHklL7IRRe/H3PTPwXLIbmNWc4hX3/9tYqaVq1aFWvWrFGp5oZcuXJFedtNpcwKCQ8NZka2b968Gasxy9No06ZNcebMGVVnTKPdFKVKlVLf/e+//x7rfVu2bJkSzmHau63RsmVL/Pvvv6rXNvubU8SOxzM66BxhzTZ/K6YcI4wOURSP2Qq2Kij222+/KQO7YcOGUW6T3OZZwTp57huI03df6+fTEL/XcPBIHefsIPvQIHiFvkb2HDnh7BIuaClYHw72dvBwsYzEbIrt3bp1Szk8DYVSzbUVZQYWLAK2R+CgjS3ZsmVDgwYNEmSfBCE+Yd9g1nFT1Tpv3ryRFFQJF9CczIWkQTNODLl48SK++eabaKOGfKxgwYLKCIrK4CZ8bb5HXJg+fbpy2FBt1pag8cwLhc9mzpyJHTt2oFGjRqodVnSw7rFSpUpRZiJQN4G9quncslUmTZqEAwcOmHyMC0Om2AuCIAiWgRjdQpISGBiICRMmqIXTyJEjY93ehxw8eDCB9k4QzIdRNxoPpmCK6+TJk9G2bVtVY8k+zt27d4+keq0ZcPSmCokPDWLj1HBm03CO2rRpk8nnsDUTI4jcpkWLFtG+/vsY3aw3p6G5YMEC2BL8TFSKN6yb//HHH1GmTJlon0djk+n/Uf3mCL+Pbdu2qQwBm6yDffw4SqcDMwXYNk0QrGEs+/n6INDfT0oiBJtGjG4hyeBCi+l/3377rUqzHTVqVIzPefH2BdZdWoe3QW9VSnmbNm2QNWtWWCusX2SKClNSBetl6tSpqFChQqR+wRqrVq1SUaf+/fur20yZo7FgGD2lEIqWdhxV9EpIWFiTzeihcc9n1s0OGzZMpeoasmTJEuTPn1+lRnNO2nVzF3bc2IGJhyeiw9oOGLZrGMYfGK9uLzu3DF4ZvOJkdDMTiJFLRoCnTZsWaT+sFTqaVq9eHclwZiYIH9Mcq6bgb2TOnDnKaI8KnldocDN6bmuw9RzV8KMyulnrTqE5QbB0+Pvfu3MbbpzYj7DQqJ1ogmDtWEaSvJAsFZwZhfjoo49UBKlw4cLq/i1btqi6CNY9alx5fgUz/p2BM0/OYM/tPQhDGNK5p8OdfnfwwQcfqMWo1urHmuCCUkt9pJAQU4sF62PdunXo27cv+vTpg/r165vchuJZ7B1csmRJdfvYsWNq7PJ7p9FG2KaK0W8aVDIWkgaWqxjDeYV1wWw9xXpvGuHMXGC698CBA/HFF1/AIYsDvMZ74V1I5J7Rhrh4uaD70O44ePcgSmcuDXcnd7P2i9k8FGFjeQIVq/fv349atcIFb44cOaKinqZU0y3dWUUnBx1S7JFuaHQT1taXKKFTyTWGBmVUImoaBQoUQNGiRbF3716rOzYxQWVyEp3RfejQoUTeK0GIG45OznAIkqMn2DZidAuJCj3zNC6oUsvoEP9q0HD+6aef1ALs7NmzSn2WUaMGixsoQ9uQ52+fo+34tsj/Or9acFoju3bt0l+/ceNGku6LEDc49pgyTiEo1vNSRMPf31/VkhpCY82wFpdGBYWemJJOo5tj/7PPPlMptr169ZKvI4mgSBmN6Jo1a0a4n7XDH3/8seq/nSdPHmV4s9abTpIBoweg8rzKURrcVbJXwaF7OuMnMDQQk25OUpfUrqmxrd02FM9YHOefnkexDMWiNMLPnz+v5kOOEWoCVKlSJcLj2bNnV07MlStXqki4oQFryU5HZhZ069Yt0v5qRjfnxfcxusmePXuQLl062BrUDuB8EZUGBI8N5x1BsHSoyl2/cdMIQmqCYItIermQaDx79kwtZlnbSgwNbi2iNH/+fDx//lyl4Z54eALNlzePYHBn8gg3Zrb4b8HlR5d1SpfR1PVZKlu3bkW+fPlUWrIY3dbJn3/+iXLlymHhwoVq/NI4+/777/WPa/XZXAAz4qaRNm1aZbzR6CbXr19XC2Ruy4glSy60Gm8hcWAa8ty5c3H//n2Tj3Pe4m+WDhVm5tBpOGL8CDRY2gBP/Z7Cyd5JbZfXK6+6kPxp8mN9m/XqL0nhFN5l4XXAa1SaUwlpxqdBxdkVkXNSTiw8vRArzq+AT2BEsbQLFy6gWLFiaowxC0grTzh69Khy/FAngDXlmzdvVkaqoUPPUmFqOB1R/fr1i/QY6+pZPhSdEJi5RjdfSzte1gyj9SwzMHRMzJo1K9pIN7UG6AQUBEEQkh4xuoVEgwrANC6qVasW5TY0RCZOnIjZ62aj7vy6eBv8Fm6OOjVgLlzP9TiHfR33oWTGkgh2DsaJ/CeQslhKzJw306q+SRpjTKVnej0XT9ZsdL969UplMCRXRXKWR7Aunwv71q1bq9ZOjGIT1nhzTJsyHsqXL683urkdvf01atRQr8OMj6jqw4WEcwqSqHpsM8JMA8fd3R0rVqxAxnwZUW1eNVx7eQ05PXPiQo8LOPrlUZztflZdeP30V6dVKQz/8vaammuAF9Ab4A5wQGBIoD57p8P6Dmizug08f/JElTlV0GdLH2y8shFnL51VRrehFgSj3k2aNMGIESPUfWyLxQwhOvLYQurFi//eyEKZN2+e+hymDGf+Buh8YslGVDDDhPOnOTALILrXsgaHEMsLmGnB0hTN6cDe8NEdHzqwo1PTFwRBEBIPMbqFRIN1iKxHZGQwOqp+XBUOXzrgddBrlMtSDrf63oqwgK2RswZm1J4BvAUe2z2GX2s/DLs/TAkZWRM0znr06IFPP/1ULZAsGSo007g2BQXAbK1e0hxo4DCKRA0CjU6dOqm0Wab50rFCg4hGhalaYRrdJ0+eVPXBNLAZJWcKccaMGVXtty2KP1kymsCZccswU/i980OBqQVw/eV1ONg5YGPbjcifNj8qZK2gUsR50a4T7XaBbAWAP4HJxSbjyaAnuNXvFjJ6ZFTbaM5Fwuyew/cPY+rxqfh4+ce4/MFlFCsdbnSzJOGvv/5C6tSp1Tyi6VlwrDHrgsZZdAJjSQ1/G127dsVXX30V5TZs+UU4r2+/vl2l6G+5tgV/Hv9TCdO1/l9r1PqwFo49OKaf+/nX8Lahw4TCndYqQLdx40blyGOZAY1vzjt0Tkc371qbxomQfGEWypkTx/Hw6lmEipBagvDHtCl4YtCmdNyY0RgyaMB7v+7M6X+ge7cu7/06yQWp6RYSbUFLURwaFtHx0v8lyswqgxDXEJWuubr1amRMkVFdDHl9+zWwB0Aj3e1Xdq9UXSQXttaAliZK2N/Xklm/fr1a3LHW1bhdkVZ7xbTb5AQ/d/Xq1TFkyBCVCm64uGe9LyPgNJ6Z+rt9+3aTC2Aa6DymfGz37t0R0mzr1auHRYsWWaVAYHIwusf8M0Zl4ZCQsBD4B5uXwqui6EFAGv80OuPc0x03+95Uc1cerzwquq1FztsVa4exB8eq5wW6ByJPnTz612HUlvMpBd5YqmBc320qZduS4Jju2bNnjJHwhSsX4kT1E/B5F7k3+YAdA5TDg8ffxcEF6T3S46HPQ4SGhSKtW1qs+3QdnBycVM08VcypucB0fAriWRt0rLAMiZ+B4nAsw6KQWlSp5YQOBpYasFTL3IwAQUgKeJ67f/c23vgHI1PeIoCc8uKd6dOmolr1GshopDcjJC4S6RYSLVLKBUJMC57O6zvr0y2DQoNw+b7pVlrFixfH9P9N10eSHIIclBCRtTBgwAC9cjlTB1mLyVRAS0SrvTeVIs20cqZFaz3Xkwu3b9+Gt7e3Xo3cEApDMUrHcgo6Vj788MMoMwRohHFxzPpWw2wHGt1Mr2Vky9JhtP7KlSuwdugwoQMlJtGtez738PtxXWs3rezF3LmHqemsVTas19ei4IZp6Bd7XsSwmsOQO3W4SFbfbX2x5eoWFcVlDTcj3UwlN4bjiY+xF7wlG5GHDx+OdptXAa+wN8feCAZ39lTZI2xDg5vwnHHf+74yuMkL/xeoPr+6qplnRkKp8qXUb5Iq8NYG67jp1OT8wFIkijb+8ssvSnsgOqOb9dyco6y5dElIHnCsFipWAhlzFYSdnX3C9QJ/52f25ZnfMxy4u1/9Nfc5MQnAMUOlY/vPUKFMCVStWBbNmuicYf/s34dK5Urh6z69UKVCGVQuXxrnz51VEWRe/6BGVTx88ECfFTB86BC1PS+D+vfTl/c9e/oU7dq0Us/hY3Nnz1L3jx87Bo8ePUSnL9qhWsVyOHvmtLr/8eNH+PSTZmp/Gjesp9oQakyZNBG1q1dB9coV0OLjxrh79466nzoc/AxlSxZF/Tq1cOFC1GuU4oXy44eRI/Bh7Rookj8P5syaicULF6BurerqsVUrV+i3PfHvv2ofalathGqVymPtmlXqfmYCNm38kcqQpS4Oy6q08j3qXLDsihmjXIvxcWqtWDJidAuJAlNpHzx4oKIwUbHg9AKsv7Jefzu7e3Y0rtAYp0/rJghDuOj8qstXWPbJMv3iK6Z2PZYCF0JsIaW1fKHiNVMGLam9Cyc1lgKwLpSp0YxKaTWvhri4uCjDcPTo0cnK6D5z5oz6a8ro5qKYUWqm9/K4RBepppH3448/qppTRrA0qHvAx1KlSgVLhmnxdKRZc72sBk/YjBw7OztHuQ2Nuv57+8MvyA+Vs1XG4S6HlaFsbusv0r59e1XnbwrDtHRezvc4j7ZoC3tfe9x8dRONljVCyekloy2l4Xjje2zbtg2WCBemjL4y3TsqLj27hPFvxgOZGfSy0zs3Tv7vpF6UDi+BrCmzqqtZUmbB1nZbkcNTVx/OyLfGA58H6LqpKwoWK2gVTixjOAez3SD1Igidecxy4PkiOqObSK9uwVqM7rz5CyJttjyws08Ys0Q5Kyd5mX3J93tWNFpeV/019zkxlTj+vWO7Wu8dO3kWB4+ewNwFi/WPXb1yBV907IxDx06iUeOP0eSj+vh6wCAcPn4KpcuUxR+/T1XbzZszG6dO/It9B4/iwJF/cevmTfw+VSdOPGhAP+QvUEA9Z+OWHfhl/DgcP3YUg4cOR+bMWTBv4RIcOPovSpQspbY/cfw4ps+co/aHAYB5c3RG+soVy3Dt6lX8vfcf/HP4GFq3aYMBfXXn+PHjflTrvn9Pn8dfa9bj0MF/oj/ub99i55792LhtB4YOHohHDx+o152/eBm+GfC13rHYt1d3zJ67APsOHsH6TVsxfMhg5WjgOmrugkXKmOb8zXI+tprUuHz5Mjp06KDWZL1798awYcNgyYjRLcQa1uExbS02Qj308HExGJUBcuz+Mfxv0//U9WHVh6loz/le55E9U3aMGTMm0vasZ2OkpEmBJiiUphDgDMw+Odsqvk0qC9N7R7EjQjVkRsAsKSJBkbclS5aoiC7JmjWrcpoYw4gdTyJDhw61eAMxvuu5mdZL548pmKLMCHDt2rWjfR1Gs0eNGhVJNI3CbDQAc+bMCUuFxhP3nZkOPB7WDk/amkhVVEw9NhUHHh6Aq4MrFjZfiErZKsXK4CZsk8V0Z3Pga4dcDEHhW4X1911/dV2lo0cF51guoEw5ySwBLpw47pnNYYrrL66j+PTieBr4FAgBfin6SyRRuvYB7ZFzS05c7X1VPXat9zU0yNcAl3peUrfv97+vV5AnKy6swMtPX6JE1xIqImVNMLrN+Viba5h5xEwpbV6OyeiOTgFeEITEo1iJErh6+TL69+2N1av+Uu1zNfLkzYvSZcqo6zSy8+TJiwIFC6nbZcuVw83rusylvXt24bPPv1CGL8+9HTp1xp7dum4Ve/fsRqcuXdX19BkyoEnTZtj732OmqPNhPaT5rzypQsVKyoAnmzZuUK9Vs0pFFRmf/NuvuH//rnps357daN+hkzrP0ABu1bpNtJ+5RctW6m/evPng4uqKps1bqNtlypbFq5cvlcF97Mhh3Ll9Cy2bNVHv17RRA7XNtWtX1Tpj2pTJKF26tLI7mBVqGIhjtlfFihXVdQYALGkdbQqp6RZiDdvqBAUFKeXlBg10P47ooIHJ+lYayuwraswjn0dKBZjp5FxkDqk2BCmcda11aMzxOWyZo7Vc4uvxfooF8Uc2qNogdNnQBVOOTkHfin1VHZ8lwwUU67i1aBcnL143nizoNY2pf29CGiBc5Gmp5UzfMSV6ROVufj/0QrKEgBHb5AAdDayxjC6KTe99TLBMgvD4sRbc2KGxdOlS/O9//1NOGUtCqzWnwBNr/jt27KiMvKiUv60BpuzSyfTPP6Y991efX1V1xCSlS0oVXY0LTOFjtgvT2c01UqvVroYXKV7gse9jFfn1cNKJjEWFJRvdFAikU8lUFwtmErRd01afNg4H4MnDJxG0OjgXBt8JRq6sufSZAYaPabepIM/584H3A/TY3AO3vW9jwN8DMHTPUExpMAXFMhZDqUylIs2tSTnvGsOSI55nOTcYLtA5VnmJCRrd1JUQBEtGtdb090dQYAAcncOzVOIT/pYf9jMtBmsM54APl9TArdc3kDt1Xuxst9+suSCmbXLnzoOjJ89g/949yqj9fthQ/HNE18HE1cVVvx2ju5wjDW8HB5tueRjdGiQmPZgI72HvgJCQ4PBspIHfoFOXyOv12L4HnQP693BwUIa39jzVyjE4WL1focJFVETcmBXLlmD/vj3Yt2+fCuwwS5QaOCY/gzpOus9gqUikW4gVjFjT4CY0hM3h3Llzqg6kUCGd184Q/tjarWmnDG5tsrv47KL+caZJMtrHiJoGaxWZykyvF/mkwCdwCXbBPe97WH1ptcV/o4zQf/DBBxHuM24bxh6+2SZmU/17M/+aGdOOTcPxB8dx5P6RRFFppyexVCldChKhR9PLyyvSdg8fPlRR3XXr1qF79+4x1jTZCr/++qvyuL4vWiTclNARVdCZhsv6XEuChkCVKlWUgUr1bEZtOXaNBb2sUUgtOhG1ftv7KVVx8uzts2ijzdFB8T2WIJgD5zm2hSpdrDSu976O8lnKq30Y/PfgaH9rNLotVSOCRnfNmjUjLJY0fj30K/59+K8+pTyzS2Z0/KijyYWcYQs1U2gGePPCzbGwxUL9/az//t/m/6Hq3KpI+3NafL7mc3z797f4eOnHKDejHFKOS6nm3XQ/p8OA7QOw7Nwy7L+zP1HmXQqJ/n3zb/17LV++HB9//LFy0sQFKsSzfv59iUoVXhDiA9Yp79q2CdeO70FYAqmX08DzcPYw60JRxkOdTmB3+4PqL2+b87yYDNAH9++rbT5q3ARjxo1Xc/iD+7HLRKlVuw6WLV2s6rhpYC6cPw8f1NE57GvV/gAL5s5R158/e4aN69eh9ge6x1KmSmn2PNK4yceYO3umvsaba/4zp0/p3uODD7Bk0QK173w9w7rsuFKhUmXcuX1bH7EnrDvnZ2QkPG3adMrgph1BEUlrRiLdQqxg/YSGuUY3Pe1Mg9GipoZMOjIJe25Thty0KBHrK0eOHImdO3eqCcYwlVWLEqZyTwUwK7QKMGrfKJVyzgnQUvnhhx8ipQVSJOLiRZ2z4cbLG2i8rLESEiLegd7ovbW3flsq8+5qvwuBoYEJFo2h0U1lbUODpHPnzsr5Yfg90uhmRJxGGMsAaHyZEneyJShUFV2pRGxgyhQXHKai4nTEMJNk2rRpqmbJUlTM6WygCrQWeePJ0BZKCzjGo6q1piG49frWOImnGUNnIYXzqGpfv379aLelwc15j1k+nNMWNV+kUq83X9uMDVc2oGmhpiafx9+opWos0OA2dZx5jIfuHqquT204FeWzlo9yfqOyeWyomr0qcnvmxq03t+Bu7463oTrjMSA4AEvOLTH5HCrSTzwyMcK8y9R1irTFZt7l77t56+b4qPNHKFeqHC4/v4wTj04gW6ps6jXocD5w9wCuvbiG0090aZO8f06TOVi4YiEqtqgIt1Rx67XNOSR37tx6ZX5DWErBFoWFCxdW9f90srLUyRga2gWnFsR9n/tI7ZIan5f4XHUWyeWVC1+W+TLJswEE24ACapZyjiMc12Uzl4/X17x44TxGjRiuHKec1z9t+xmKFS+hhNTMhdHnW7duokYVXUZPteo10aOXrt76519+w9d9eykhNRrFA74ZgnIVdNv9r3sv9On5Fdzd3PHHzOhLMVu3+UwZ3E0a6kRgua+ff9ERJUuVxjdDhqF3j/+hXKliSJcuPSpVrop3797vXOPl5aXqw4cPHYzh336jjPxs2XJg6V+r0Oazz7Fl00aVHUpnMrvGMKvSWrELSy6hqThCTw6jfEwnteSFJQ0BLVJjTlprXKFAFI0xRrdY48vFd0xw+0uXLqk0OUMO3j2IWgtqITg0GL/W+xXVclQzazEzfPhwleJOg0+jWNViuPDhBdVqIlvKbLjS+4pVLgYWn1mMrzZ/pYSa7O3sVbqll6sXcqXOhVOPdZ5GQzJ4ZMDxrsdVrWN8pURySmDLqzJlyugNbHoYOf5Z5031SA1GYTj2Fi9erCZOthQzNNYtYczGNxQwatSokVq0sl9yQhu4FNmj40qrW0pq2rVrp9TKDVVCKQZHLF3EJDroCGOphGFWjfZ74DzFaOenRT9FhwIdUL1gdaRw0ZXAxBYaYU2bNsWBAwfU92oqA8jwvSm4yCwCLU1v6K6hGHdgHDJ5ZMK5HufUb9/aeer3FOVmllPZSi0Kt8CqVqvUApyp/jzHDBw4UL+ttmSJ7QKdtdyZS2ZGr896YaXnSlUbnylFJjTK1whzTuuiQ4TH9bHfYzW3VsxSERuvbdQ/xgg8F8ysF2f6ekxzLfd19JrRGHlqJBCXqicG/Rx0Tp7YCvYR6q7wt/nJJ5+oUixtnqWDl4tXpvivXr1aiThyLh4xYgQGDRqkP7Ys/eq4riN23Nxh8vW5P4c6H0LJTJEFJQUhNjz3DcTpu6/1v5sQv9dw8EgdZ0PcPjQIXqGvkT1HTjgbpG4L1oeDvR08XCwjRhwQEIBbt24pZ6Zhtpa5tqL1rHQFi4ARTRq8NATM7S9NQSnjVmFcZLVc2VIZ3C2LtMTXlb7Wq/ZGZaAtXLhQtQhg1Me4vjhTvkz63o70yJ97cg6WCFOGGblkuxdjftr/E9qva68MbldHV5zselIvCnSg8wG9MJChMi+PY65JuVQqJFMiS/1Z6r1TAHmSY1qiYUSbERFOJMZiasw8YDSFacb8XqyxJU9sYaYFv0fWS8Y3xmmcjHRzcv/99/AWVYQpV0w95+8iMeH7UfTNuA3a9evXVYmBNcMon6k6a3ZUoMHN3+T4OuNROkPp93Jsse6Mtfo08qlIHd13yN8iM0kM6+I4VzraOyrDsNC0QlH+3i0x0s3521hBnPvPz0GDm323JzeYrF9o07HFTCfD2MDdu3fVfBRV7X1UMFOgVPpSuHXxFs50P6Pm1ht9bmDKR1P0iuj8S0cGH7vV9xaWt1qOfGl0mTuOdo768oIbr27gpwM/4ej9oyaPPw38qUenKkfCyPNRG9xlMuuEkzQyp9CpkXvYewBs/e6gu5+92/fdNj8aZjjW2Ntby6IijBJRxI7jj05SzuEnTpxAp06dMHjwYOUM4vGeeHgi8k3JF8HgpgO4bbHw1ob87GVmlMEXa77AK3/z6mUFQRCSK5bhOhCsBhpYvBCmGxvD+jEKK9FA1lrv8ISu9dUjz98+R52FdZQoEDn16JRK5YtuIctF2KxZs1TElUa8cWS0VJZS2Ou9FyGpdPVA7NlaEZYRGTSESrJsa8BUPrbi0vhuy3cYczxcpZ1pj1zQ5HPLpz8umjBQHq88KD+rPG6/vq0McNYo8vhpizNuYyguFFu4mGXUg9ERQ0wpmGu9xrUIqCWlhyUUFJljdNLQEIormmhT4XSFVXotHVGs62R/ZraL4nc/adKkSJ5TlnnwN0aBu8RM56c+A8eGsfI0U6ZZf8ooLhf61gjnKWOordBnqy51r3+l/sjumd1kqm5s4fdJETrWXUeX5UHHCkUoaQxp3Hp9SzkrCVOd6aSplatWhOcxU4UCfEzTs6Qskr59+6qSCsO6PO6/VkpDAbWHPg9V6jXhuYbnDooKaqnPNLp5X0z91E3BOvD9+3WiSIZzJKPIhplChtkDZ746ox5jthGdmo98da0ef9j/g7pkcM+AdW3WoWiGoth9a7f6HU84NCG8hSW/Kl51h95xSqOdBv72z7ejypwqat7m7UNdDqnWcEXTF8Xxk8fR6p9W6nxJ2q9pj54VeqJunrrq+NCJwHNAdNlNjLzQQaHN28w4a9asmTo3s7yBzlLCLKXJkycrYcw58+dgxrMZ+rR7nmM2t9uMlM4p9SUV/IzcZzdHN3XuWXRuEVZdWqWcGJlTRt/GTBAEIbliOWdjwSqgYqpWU82aj1evInq3mV66YsUKVYuoQeMkTZo0EQxuQxEiLkBiEiWiMTd+/Hhl8DDKzd5/hrRu3hqTC05G++Lt1W3WdjM129LQFIU1lWcaXb239NYb3O52usUTF2DLJi9Tqdd0NKjH/lsockF4occFFY15Ofglzn11LoKa8c2XurYPcYXOjQkTJkS6n0a3YUq/MewrTZEoW4dj0FR/7tjC777w74VVhkLq8anxwcIPlMGtGVYUevpu93eo/mF11KpVSx+95PdDcUGmjmr9whMLGviMaDPjxdjoZtqVpbfriApTVVb8fvJPza+PwPapGL+9yFnXTAV8fqdRCdxs2LAhkpFPw0ffq5qaGfcjq1PTiGIE3Xh+TkrYJoznDmOHjWFWknGtvObgNRxXWj1fXDJNunTpos4j0fVHj+oxpptf73Md+zvuR+dS4Q7np2+fosrcKkj9U2o0X9EcP/7zY7jBTZYBrR+0BmYBu1ruUs5T4xZo2u2/N/yNvxf8rQzqWpVq4U6/O1jaYimKpC+CFwEvMHr/aNSYXwN5puRBxl8yqrkjur7tPG9mz54dp07pSpM0XRRqpBj3+KZzpv2X7bEj3Y4Ide506tLgNuwfr+3zts/De8HT+Oa+US1eEGIDnbXnT5/EoxsXEJpAQmqCYAmI0S2YDReGrPfiwomLVC56KAikwegD06ZpsGnKsj/99JO+Bvjum7vKq3/2yVkVHcjpmTNWokRc6DPVlkYHa0oN4eK1Z7eemNRwEjxdPHHu6TmlOmvJRjcXSkwNn3Z8mrrP84wnuvl2U4uZ412OY+rEqfroYnSLRLa+YQp6ndx11GNUgx+zf0yc08yNlcs1+vXrpxatGoye0CnAyBHhmGCasanUeVuBhgzH//sY3fxeVl9cjSbLmqjfhHrdsFC4O7rrW+WR049PY8w/Y9Tiesy6MWj9aWv07NkTvXr1UsYUj31iG90eHh6qHtk4yq+JGlprv26WRTD6zPGrsfnqZn2UkRHYO28SRryF9bSmnFycT2/evBlJpVszeoZW+0907NhUBAZHTCXXnHqW1DaMrRJpBBqWJnDOmHlyprrOdo/Gdcua4Jrh98JIN2vcORZjC0tmqMcQV7hv1XNWx9SPpqpsFHWfo7u6aKnnWho2YWR70Y+L1LkJD4An959EMvANbzNziAax4fu1Ld4WMxrNiLAfFDLTYG36rltR9+JlpgSdN5y7+Dtler/mzDDk4tOLWJZqGR65P1KfR2uJZ+r8rO1zuSzl9A4gOqauv7yuHAHLzy0XpXPBbDgP3Ll1A68e3eUNOXKCzSJGt2A2Wl0YF4FcPFH11FDBnLVgTGdk+yMa6ExPpoHMqPSLty9QYGoBlZLGkzM95Bd7XtR7+M2tkWQ9KdMmjReifF+Keb24/wKDq+pSMb/b813EiIMFwKgV02+Z1jfv1DzVekijQFgB3L1xVy1mtm7QKSUzQmEcVTRFatfU6piy3i4UoeqzM4oaW8ObUTemIJoyKrlYNVRbZtSbi3qmL2pwX2fMiLhAtCU47pnazb7UceGJ7xNk+TWLSiNnKqpG1pRZ8XDAQzwZ+ET9Jv5qGd4mjOrG3535Dis9VmLO0TmY/MdkFClSRH1HiWl0s11g27ZtTTqB6ACYPn26Sh22RjiOmVGitcULCglSYmXxoVYeE9TGMK5z1uZbLkZNtcbifDmi5gjduPF5iIVnwltiWarRTcOP84NhL3fWyjPLiZ/n+5rfRzoP0LBmZhOjtYZGd1z1FGh4ssaZteLvA/eT5R/8rT775hmeDHoSwYl8tfdV9Rij2p9/+rm+vSUFeKKDTivNgWVImSxlItSdc67gd6/RZV0XzDoxy+R8z98lS7600h/jEqA3AW9Ua7Si04sqo5nn5/HFxuNa72sxnp8No97nup9T+/bA54HqtV7096JieAtmwSyL/IWKIH2OfErFXBBsFRndgtlwYcjJUVPbpXCWodFNJb82bdooY5zXmU7ISE2lSpXQYV0HlaamRY1oSESX0hcVjHz8+eef+nZF+oFsb69qzFmnxjRQqtIyRZdKv5bUW5TGCSOFFHv7fu/3+vu5WCmesbg+jZLRY/byZsSZafxMJY4JiisZpsAyihrbXsJc6DMF0VSkm32Zeew14Sct1ZxCT9pirmrVqjYtpsbPyFp8Q8PBXPbc2oPSM0rjTeAb/X2zm8xWC1Yu0j1dPfW/iUYFGukX2Wlc08DZ3hngmr4l8LPvz2pM8zuKj/pic6FTjXXbUdXt0zgyFUGzBjRnmGZ0szyF3QLYImlj241xUo42FxrVpoxuZpxwXouqH7WLowsGVtGpeo8/OF5f5020emdL6tXN0gS2vjPk9+M6gcDPi38OLzfdsTdlNHIuNGy5GNfe0zyeLJFiPf37Ynj+YoaKoROZaeOFUxVG145dVVYWnazMEjJO6TZ2al27dk1voBu/l2EaOl+fc8ba1mtV6vkz/2fotqkb8kzOE+l8xw4LzATj71YTaqTQG88NY/8Zq7KtDFuj8fzcu2dvPH3w1Kzzs3YcCqcvjN8/Chd8vP3mdrQReEEw/F0WKFwU6XPkh50FaVAIQnwjo1swGy4MuXDSZPJpdNNI04ww9l/lYogndwq1rFq1SqUSBhUPUn1lEzJqxAUzU89ptLIebkhVXW3xr4d/RbE/iuHB0wexVrtNCBj9X7JiCT756xMlglQqUyns67hPLaR++uEn7NunU6j9448/VHSCBjCdGBTTMocSGUsowR8NTawuNlCZ3lS0hVHV7t2766NnTC9nfaChAcpIFlv8sEbLFqHT4euvv47Vc7jQ/WrjV6pmmyJMdI5ovwOmjppa2Bousu/1v4eVrVfqH6MziXWw48aNw+HDh5FY7NixQxkN/N2bghFwc8epJRrdNFS5+Nt1c5cyRsisj2ehcYHGCdp+kMeT8xaNLkNoaDJ7x80t6h7NXct0Vf2jNTVtzeBi+jWjqsw6shRo7LIrggZrf9dc0gkxUiAsKjjfaCJ3dOjxOJnbOSM2To6oYOaPOZ1VjZ3ITBOnSr3mIKa4XY0aNaJ8vnYuNTX3mnp9/m1WuBn++OgP/TZP/J4ojRBT+8ssm7yT86r071Q/pVL93oftHobXga+VMrumnM6UeA8/DyXUqMFzUbdu3VRGWXRUzVEVeVKH92Dvv70/tl/fblGObyF+YScPdrSx1XN+UlK8UH6cPXNaXW9Uvy42bVj/Xq+3ZNFCXL1yOZ72TogLYnQLsVoctm/fPsJtGoWMaDOdfM+ePXj3Ljydmwv0nxb8hB9P6Xr4jqw5Mtbp5LGBUTYtUlw+S/kIRkqbvm2UU8CcvuIJCaPWXdd3VeqvjGCu+3QdauSsoY4HjVeqzVKEjoumFClSKKOWarNs7WJOeyi+DkXW2hRto26P2DMCIbEQJmF6MKNAfG9TQmpEU8JlpJvfsaE6MqOvLCdghoMtsnXr1gjZHTFx6dkl1TZoxkldyj31Bq72umrW78BwkU3FYi19lZx8dDLRVcJpRNCJFlWkm10F6JDQhP+sCa1fPFvwNVjSQNXnUjzqo/wJb7RyHuXvnVFO4/mM6fzRQQdj93Ld1XWWlGgtA/mbZPuz+FDYjw/o8GS/cUOmHZumoqpVs1dVzsKo0CLdNFjZV9pQRyShje5du3apUg52nYgtnEf5XK0unWMsOs0Dio0OHz48SqdWVJTPWj6CsN7c03PRelVr5Tj649gfKgui2YZmyDEph2ozp2lIGNaFB4cFY3nL5fqU+E9bfKoU5nnOoTAqz0EsFaMTKDo4V7Hl2vo261W2GdPV+XsqMb2EGN42ytq1a1UWx/ffh2fuxRbOf0FB7xASHGSWg8tW4Po5MVm6mEZ3RD0kIXERo1swG0YpuCjQoAotxX6YXsxaay6MaIBrUIm5xYoWKq28Yb6G+K7md7FOJ48NXNxoRnepzKUiGCkHbh1QkSxzvPXG/ZAZ3eXf+KBW91pYfF63cHFzckN6j/AoMRel5cqVU1GcefPm6e9n/TAXfbt3h9cARweP77SPpikD78yTM1h8NvqFkiFcjEZlNBkb3WxXZdgyjDBKw+3Y4scWMVdEjYbPqL2jlMF96fkl/f1MLWcdf2x/B9yW6au9yvdStwf/PRi3X91WqajGPbwTAn6fzHQwVp42RIvQxcYpYSlwXvvrr7/Qb1s/fZq2zzufWJdnxAWKe/E3Z5hWTGPnu+++U1HWmKiZq6b+utYykHzzzTcqMyOp4SKaZUeGYnHs6cy2WoQtsqKLhNJIplOXDl0q57/P4l57PZ4njDtgmILHj4azYU25OfD727x5M5o0aaK/j5+/RYsWUT6HmVpMnTfl8IwOw6yY6R9NVzXZqy6uUlHsnlt7YujuoTj66KgyrDVDmy3Hbve7HaFOnKJo2rzEcw4zJegs+fjjj5Wx3bx5c/z4448xGgp8/scFP1b91jWYiXH4XuJl5QiJh5Z1wsyruGYTMkq+Y9N6XDnyN8JsXL3c090ZY38YhVrVKmPkiOF49vQp2rVphcrlS6NSuVKYO3tWjK8R3XMMo+OkZtVK+Gf/PiyYNxenTp7At4MHoVrFctixTacbZAif+8PIEfiwdg0UyZ8Hc2bNxOKFC1C3VnX12KqVK/Tbnvj3XzRuWE+9frVK5bF2zSp1P+eH5h83UvdXLFsSXTq217cM/mf/PrW//fv2RtWKZdXjJ0206rRlrNLo5iKTXnymOVesWDFGUZSVK1eqOmRuz4UhVVSF2MEfzfHjx5XQlgajsFq0jREBpp5rAjc+gT5osLiBXvH3yosrqvd0QsLaca3+UTNS2FuXOH/kjD9X/qkWgMYRl+gWTqylZh0re1C/b/oUFYYv5QpfRFNwxnBRT0EyLY2SCxzDz8UTm2Fv25hI654WQ6vr1I256KJgUUwpfjw21atXx5QpU0w+zkggv2/N6KazhU4CQ9hLl+rljEjZGoxUsa69TJky0W7n+85XiaWN3DcSb4Pfolr2avqUy/cprVA9uxtMQpXsVZRB2GpVK9i52KkIc0LDFGdGuqIzummcMMJqSmjN0qHC81vPt1h+fnmiiKcZwvRjY0OLmSJjxowxK8LK8UAHG2FtsbbPR44cUXX4SQ3HJ7NiaLwZR7kJSy6ic24wysrsH5ZSUA/jffuOc83w5Zdf6heCUUEHMo18Cncy9ZtCklG1djOGGVVMi2epjqFRTRG4qM4jFJrTxEpji5YV81X5rzC1oa7rhSl2tt+pjPMrva4odXLDOnFDJyDnb87tLBPjb5/nPzo7OAea+/tmWUYOz3DBu0E7Bylni2BbUC+AgQzqubBTDdPNY01wIByeX4WL/1PYJ9Q6kRH0d37mX/yew/7uQfXX7OeYGaXnOmrvgcMYM/YnDBrQD/kLFMDh46ewccsO/DJ+HI4fiz4jMy7P6dCpM0qXKYtx43/G4YP7Uc9AFNcQOiN37tmPjdt2YOjggXj08AH+3vsP5i9ehm8G6ErrGITq26s7Zs9dgH0Hj2D9pq0YPmQwHj54oD7bnHkL1f1H/j2NVKk8MWN6eGDg6pUr+Kxdexw8egLdvuqJH0aNQHJCV1xoRTDVqX///soDzZMnawipqEyxEhoFxhw6dEil6NELxxMga6x4EudCICqBGiEyNLhr166tFgWsMTaMfvOkTKNbE7vxD/JH0+VNcfzhcf12jGZwYcWFQULx+eefq4vaB39/uLu5Y3T10SraS0GX7y58hwNHDsDL3bRgjyGsHezbt69KEWSdOmsjuejgQjiuTDw8EcHuwbCDnUpfNV7UawtvnsQ0QSfCdF4uEvkd0DCOKr3XGIqqsZ3Qfe/7qDm/pnq/6FKauSDkZGpKRI1wMuXCWds3RuL4GzRcWGpo/WBtCa01WnR1mfx+uqzvEkEs7ecPf0bJTCXV+Of3/T6ZHg72Dviz0Z8o+WdJVaLg2sAVDjsSPs2c7bQ4BqODTk22v7LGtmEDvxmIdenWqd9lyyItMajKoPf+rmLDyJEjlYGtpU5TRI2Yk1XBfdzabqvqFc251zvQW1+uYgnq5TQmKSTGRbnGpqubzHZuKGXj/OHp0+8LnUMzZ+ralEUHnZycw1hSxbIgRvGY9cSob0xzMN+DzsvKlStHMLppnNBpaUp9nedSvj7HwvvQoVQH/HbkN5X1wPpsLdLM48xUdMMxrRnrxvDzUV/EsDyBY5HOE3Mj8XztSz0vYe2ltei7ra8SJqSuBedDlhQk1m9LSFj4u2B2G9fWPEeaOz7OPD6D1ZdWo3C6wij56CI+e6NzFoVs+AWXy3wPP6f08HMpjzAnd9gH+yPFm6vw9SygttGuhzpGrXcRiaC3SPVbePZjQuD99R3A2UQrw7BQOIQEIMRBp4f0+RdfwCH4rbq9d89uHBi5R22TPkMGNGnaDHt370L5ChWNXwT2oYFqOz5n35gj6t7onwO12nQM8oFT4EvYhbyDm999ePjc5KshyJnOWjsEO3kgxJH7HYaWzZqo98ibNx9cXF3RtLkuO6dM2bJ49fKlWiMeO3IYd27f0m1rwLVrV5Epc2b8PnUytm/bipCQYHi/8UaFSpX02+TJmxflKujmnAoVK2Lq5N+QnLC6VfHEiRPVyalTp07qNo1vpnFRyGHIEJ14liGTJ09GgwYNVH9pwvQt1iZOmzbNIlLvrAWmHTMiw2i2sXG1evVqdTIePXo0Xge8RuOljXHw3kGkcEqhFGnved9LtKgRWbRoEUaNGqUi1Ezv+27yd9iDPbj47CKyT8qO7Q22Y+NfG5UjxtTiiYq/n376qVokceHK8TN27Fh8++23KsoZXYpgVNDwZc9l8lmKz9CnTR+Ti/o7d+6YVMYeOFCnUhwbXB1d0a1MN4zYOyJC6mlUjg+t/VRURjcxTCen2Ju7u3sko5vHnqm61phmHB10htAo0tLsTUHV678uhrf74rinwR3V4jYu+Af763sCBzgG4Nzzcwnu5OB3zVpT6iJEB51erE19X5hhQn2DxHLc/HnyT/hV91Ot96Y1nIaMKTIiMaGTkI5LDRrd1EtgBN4cKmevjMrZKuPw/cOYfXI2htcYruaRmFpUJQZ0XNJpqQmKnXh4AsceHlPiXRvablDp8YltgPG4MNIdneOdrQF5HqCTkReuFxjx5fnAUNvEFBy7vXv3jnAfzyfaexsb3SzfYBQ5KhG1uKSba04+Zmz9c+UfVC9YPXYlLe6Rt6VBxUU3z1PmOoTalWin5sDa82vj9OPTqLeonnIGsHZcDG/rhx0JWrdujZ9++klFuklUwQFm2/1982+VUbTsfHgHgk5hTpgLnQFtHxaI4id0WXo8y4X9F6bgq+nOerrbIfYueJr1QwR4ZEGIgxteZawCvxQ54O53H76p8isj194uEHYhAXAMCoLdu/gpETQH+5BAZejyL3F69wZ2CP3vUwCZgh/Cw8dbfR670BC4+91DytdheOeaBvah72Af4q8u/MQOQb5wDHwFh2B/uPo/QYo3V2BHTYZ3b+Ds/xSws4djiD8c33nDxf8JHB3sYO//Em6+92AXGoh3b73hEvAcbm8fwj4sSB073VEMhfM7XeaJ87uXuvtCg+AV8hwpvK/pjqGDgzK8ddvrvtOQoHcICwtB4UKFsGPPfvX+hk6FFcuW4p+9u7Fl+06k8kyNP/+Yhv179+iPjauL7vUIXz8kkevakxqrMrpZ08X0Wxo/hl7wunXrRqniy/sZGTeEkXGmjQmxM7qZom/cqosLcUYEKMBVvmp5ZP8tu0qv5bS4+tPVqJajWrxE+GIDU6Q56TMKywhd3iJ5EXpJJ0LmF+SHDn93wI3fbigjytiA5vO++OILlUZPo11b9A8ePFgthBkNji080XRe31mX3n0XaFKvSZQGWHT9Z5mWSAcTjVxzUyz7V+6vaieZjsxavwwekbNBNPj5qHqstQAzBY27N2/eqP65jP6Y2pb3MfMkICBAr3RvC/C7YVu6qJhwcIIyuskvH/6C6jmrJ8i452ty0croFQlOE6zSbxldSygYfevVq1eMRvewYcPe+71oJFB/oU+fPsppmtBceHwBfpV0qcY/1/050Q1uQuPv559/VrXdLDOhAyw655cpepTvoYzumSdmYki1IeoYJnXLMEZ2KQjXqlWrSG3CWhdrjYb5GybJfjGLiXM8W0xGBc9rhvXLNCg4/w4YMEClufPcEtVvhYtT49pzlsQx4v/qVeQUay1l21S7sLhg6OTj76l0htLxNg+xNeCpU6dU1pu5Yo6cs6Y0nILP1uiMMs5djHTSWSRYL/x9c02kOZQIf+s87zP4YcjR+0dRf3H9CFlgGns90uCqny8KwAFvEArP/ypfaaJqRqJ2WzO9HUIDkfleeMYMLoSfK7hFYIrsuFX1V3j42cHV0U6X+t1JV8esjN3/DHoaijQYdfmH/71PcADC1veEnfdDhKXKCrum0xDqlAIB7pnU48GOKXTGpv4NQ+AY7Ae3wOdweHtfGbemCDd4tb9A3eoVMGvJGvw4pBe8H1zDxg0bsHLGeKTwvqGMYDf/J3B/m1r/ue3DQlC3Wnksmj1dPefZi1dYv3Gzeo5LwDPkz5kZpw/vRZWCGXDs1HlcuaEr8SQpUnnilY+uzDAU9rBHuDivYWK8fWgQPPj+YaFwDngBl7eucPrPME/hfQV1CqVBn9s3cXTDPNSuXQsOIYE4c/4SChfIA/9Hl5HB0xVZQx/i+bNXWLpwntLEoBPCMcgHdmHBKsofau8M+5CAcNdKaAgcgv3070+HBSPwoY7ugJ2TLn3f0RWwT1wB2WRtdHMBQcPD2PvP2/RKR1WXZWp7Q8EvQ3giNqxb1mq4eOIyRz06qeC+0WBMqH2k0U0D2/j1tVRz1s0POzJMGdyE01cq51RwdXBFucy6ut/EOn40jvbu3avKCqiqWTV/VRVxZKSXzoAb/jeQqncq9JneB7Xr14aTo5PeMbBl/RZs/Xsrfln4CzzTeUbY5yVLlqjFVGw+Bw3tQr8XUvXbZOc3O1Vblbgci3///Vct9phZwBIJc3BzdMO1XtdQeU5l3HpzC19u+BJbP9uq0pSNYS0WI/kcR8YKomzvw/r8xb8sxoE9B/ROK0bjjD8LjT/+Trkoi85wSOgxG5/QwUCjiIYn+3QbQiXg0ftG44d/flC307ilwf/K/k+/yI3vz8ff1On/ncbw3cMx+dhkZG6VGdlzZU+w48g5kGnKXFiZ8x4sg2Ckj6nmcUFTvmc9aUKPjbOPz6L87PKAE+Bk54RPi34a7Xsm1JjV5lHOs3QW1qpVSznAYvM+LQq1wNfuX6vMoo1XNqJhw4Ymf5+JCY0yloTpNTLevsDSc0vV7R7leiTZvrFUisYzHauM0hnDEgk6QujcNNxHZkexXpsCd+XLh3fI0KBDktl4PXv2jPTZOJ6ZwUGMH6OThZFlGubxfUzie8zyuNFhzWMXk7q+IU0KNEG2lNlw3+e+uj3n1BxUyKLrHy5YJ8x4CLEPQWC6QPgG+qpznmtKV5x4cAKXnl7CXe+72HRlE/be2YvzzyLqNjAAwG4RXJvt77AfX/7ZAc5vn+Oa+ysctnNHCr97eOueDaer/YFSB3uoCPZbt8zgcHF7+wiBrunwNHNtZL8V3k5Tw3hEhdo5wh7BgJNbhMftNCPIXovm/oeTG+xazgVe3oJdmtzK2KOJ7f5O9/sNe/dCRddDHVxUBJpp34bvqRn14e9vD/uwULUfutu69VeonRMmjRmKnkNGo3id1ggNs8OwPp1RsUzUGS987q9jR6LPN8PVc/jbNnzOyCH90aXPYMxYvBqVy5ZA0QK6EpMQe2d80bUXhn07GL/NWYERI0ejeZUCcAh9px57myJXBLcGnRp0VLgEvoBLYHiZCbdIkzoVNi+cjIGjf8PA0RMRFByMHFkzYd2ciejQshE2bN+LQtWbIn1aL9QsXwx37j+Cu+8dFXG3Cw1W6e2E3ykdC6lemc6K1CLx2mo0zMEFSF8wosMjkdHWx8Y2obnzq1UZ3YkBT6pMjzWGi05G7iwVfuE84XMwvK/QTFSLJ6qDMwXOEM2hMXr7aCx7Fp4ulMczDzIgQ6TtEwtG5NknnPi+8sW2Zttw5dUVJWbWaXsnvE75Gt7VvJHmlzRwtnfGu9B3cHd0R177vHD81hEDrwzEuEnj8EPVH5TzoHLmyuqEQucOjRAuOsxh552deoObBAUGqe8pLuTMmVMtyDlG2Q87NsyrPw8N1zRUte2dVnfC6CqjI0U+mBHCyA6/M9aFLrm0BHd87uDQw0O49lrXzsg9rTscnjjoU8dZ82f8HWvaCqyLjy5qntBjNr5bhVF9mF58Lp41eJz67OmD7Xe2R1DtZzono0sJSd/ifVWK3qO3jzD9yHR8UeSLBHkfLQpHIzCm3zOdLVyI0/FFLYS4LKg18S9GFhNy/jj2+Bg+3/q5UnUmQWFBOHj1YLTfW0KNWR5bHitmZtG5wTRmEtvP36ZAG0w7PQ0/7P0BqxqvUlHmpJqDCecJlmMwwkt+P/276mZRPF1x5HGKfD5JLJg2znHGrCZ+j4Y6DdwnGtRMl9W+Bw3+9lm3yueY2neWYXCdQLX22Hw2jic6STSjPD6J7zHLqBUV92l016lTJ1bP3ddqH+acn4Oxx8YqozskMATfVfpO0sytlMP/Hgb6An3P90W/8/3g4egBvxx+CMsZhiLTI2ZeOcIRzo7OSlw0t2dubGy6EXd97qKgV0EEewejsVdLvHD1xudlauKggwM8XlyAX9qiCHNww4FqS5DS5wZ8UuoMSMPraZ4cgcfbe/Bzy6oiyO7+D/HWNRMCXXTrkBA7J/imyA0Pvzs6A5NRU65pw4KUERzgmg6uAc9UBFlnFNupSLWKbOeoDddA3WNamrveWGf6t0oBj8xb9+xwDXiiN2j9PHKq6G2Igwte+75VxqxvSKC67Z4yDxYuW62ukxS+t4D/9vPG8R1w4PPsnZUhy9fwcXBBypTAyrlTwz8Pd+i/7YpUaYCz+wpHem9fBxfUb5AH9Rs00O+nb1iYilKr97azw5lL19Ttt2FB8Hj7AM/P6zrmvHNMAcfgtwi+e1wdoxDYoUzxwvh71VwEuKTTHyM+5uSWAn+v+DNSJD3EzhG1qpTD6Z3hYqXFCuXD7aObox1jhg4MO0a/A3xVnX9SwTWyJrJsmPlrbqtUqzK6mS5H48+4HRFvUzXZFLw/Ntszdd0wHZ0GFk8yrI+LKp3MEuAg4KKN+5kQBgzrDVl3yLRiQ/h+tXvXxsJnC/W9uOvnrZ+o6eTmkisrPXnANMdp+HytTnCN0OAmPBmcwzm9pv+LgBfosauHuu7l6oVLPS4pAR56d81pjcEo92/rw0Uisrhmwc8Df8a6letUCmlc4PhklJs9fQ2FiWKChvBv9X7DV1u+wpLLS7D51mbc6HND1bASfrc0oDl22Fu67KKyanEc6TPZvQU+BY6HHkfv/r1VJNv4s/C96CBg2pkpccPEGrPxCSNR/ExcbGoce3AMrda0UvX6dNykdkut99zHtn4yrgyrPgx9t/fFmP1j0Ltab7g4xn9fZi0dloYIjcOY+PXXX1XZBrtKGLZMMheq32t/zakbNRf+Hs89OacUvnfe3Ikhu4YgKDQILvYuCAwNRO5UuWP83hJyzDJ7hJkmzLTiZ6djL7a92P9X8X/K6D719BRqrayFURlGoelHTfVGb2LDMUPnCR2FIaEhWHBpgbqfKtvm1qsnFMxcYubQ0KFD9U5EOj3YHYVlRWybZShoaQhF73hO5DaG5W/UlqGhHlVtNudvlvHQiWeIcQlcfJIQY5ZK7lOnTlXzQWzH6A9Zf4BnSk8M3jUY8y/Ox677u3Cxx0WLWy8IMbPXdy/wn24asxt9g30jh5n/Y0u7LaqcwLDcsDAK68dokaIlcPelP1J4ZlEGoI+9Kxw8UsP+P8etb6pM+pc2vH6k/kZ9DTLRrtMI9Arzhn+KnHB2dIFfqnwqnTn0PzEz/XU7e/i6pIn6MdZZ87a9s4rQ0pgNtXfCO+fUyljXYNSaxjoN3RDnlPBzThnhdULhEjH67eCkv214Pbr9NHyN6LaL8JiJ9zbE8L2126zRDgl4pjfcA1Loyh5jPEaGjxkcL2X8p8wT4baqV1eOAicVZWckPPwxhEfgU+ZWz6PjgJFuB1ej1P5EhucGzqOc+wzLJ80tpbQqo5teZi56ebLT0mv5Y+Vtpn2aguqhfLxfv376+yikZqgqaggND0PFTg0eZEs3DHhifZ/9pCHJhZBhWii9+nxd1kmbMhTZe3Nv2r3qOtvWDKwyEB6m1BstiOaFmyP/Pl26edYUWRGCEDz2fQy3EDcMqDwAM87PUL2UmcYboGpOgFcBr1BiRgnUKV4HJ46egN87P6R0jdpwZspxpw2dlGJrWre0mNVkFh7+8xC9/+6txGji+h3RiGEqKg0bfiexoXSW8Aje68DXqD6/Ola1WqXqvTfP24yFCxei+7zuKlXa0OAeWWskFpxegFuv/xNlSg/8ePZHeGXwwo+uP5r8LOwpzih3TJ/zfcdsYsHfAeuZVbppWCh+OfSL6pVNHO0dsavDLpTJXCbR9Qu6leuG4duGw8fORwnmjao9Kt7fm3MCI4J0epoTuebcTJ0NpqFSdMrUfBod1Idgmi1fh84gw8wCY6gQzegi032N941G9r7b+5SOA8W7phybEqltnoeTh2otyN+/ud9bQo1ZraUWf9sjRoxQDt/YvsfbkPDPd8/nHjpP7IxjBY+ZTIVOaOjcpvOA783PQaVipr4Tqmt3LN0xSQ0tLpIoDMn95BjlWGWNN41TRrmjczCtX78eX3/9tco8YtkVodo3lckpehnV98axzBICw8eZHUIhUpatJFSqdXyPWRrdzIBhBJ3zQmyplbuW/jrHxIVnF1AxW2TlZcFyufL8ClY+C0/tzpU6F5a1WIbmy5rjsf9jZHXLqn5jmnJ+1Zw6xfpK2cOVrDU4LouUKIV3d19HEGLTLtHBqKdP2nDnrHY9jEZaaIAy4BU0Plkf/B+G1819jMasoQo5xczCjcM8SgRNPfafURjhdcwlun0xdzujx+KyDxE+q4nPY87xM34NP6PjZ+5j/qkLwMM+CHYWUNOtjUnj+dTcudUuzLh408JhfRjVEmfMmKEiAWwZRqVkpv1qi0Oms9GzrrUM42KZJ1GeKJgSRSVqc1uGceHD+kSeXCw90s10NkYW43JiZcqElipBjz2vs18fjxHTyumoMJ78gkODUfyP4rj8Iryenj0/E7ItWHzBxbdmIPG7HTp5KJb+thTXLl5Duszp1GN5vPKgypwqyjinYcXPq5HZPTOu97tuctHIn9SQv4fg50M/K4Xe3R12K1EtjkkuqN9X3Gjt2rXqu2b/2Nh+5lJ/llKfx56TZJiWABQKu2DdRBLqoEsHcnFwUYa31maM8Jj8e+lf9DzYU/+aVEze/NlmpVKf2GM2sWCklwvw2bNno9rH1dBubTvVrsuQpBz3nWZ0wvzHuh7u+bzy4UTXE+r7jGs2RXzAyCGj1Jx346K8v2fPHtWCkIJ80dWGM42dUUdGh2nQvAl4gyXnlqha7bmn56pIdkz8UvAXNCrVSAlFJuWYZa0wjUAaYMxkYZ/t2MLfeIGpBVRJi0qG/DUMm5dvVurhic2WLVvUOZc1+kyZL/1naZx+optLLPFcwXRzGt907Mf03fIcSecnxx+dn6wL57nyxo0bKqU/KhgJp84InUlaZIRrF74WHZWsNY9vLHGe5Tgt9kcxvSN3Tes1yhkuWAePfB6h0uxKqmaba4AJH05A6cw6sb5Xvq+w7eQ2NCzbUOdkMtMR/dw3EKcNjO4Qv9cq0h1XRxQjo16hr5E9R044GyhmxyuGrcCSMPpq6zjY28HDxTJixCwfYgcKntMMo9vm2opWN0pYi/XLL55zQ50AAFu9SURBVL+oSABTW5mqtW3bNn2qGlNaHz16pN+eta/sH8i0YC4CWefLND7p0R0ROiEIo0Y0uDmw2AKMC8Dp06ebnPimHZumDG4acCQx24LFl7pr0NsgFC9UHPPHzEef7n3UIkp7LJ17OmVwcnH4aMAjdC4VrlzNOtoWK1pgw5UNEaJnTC9usqyJMrhJWve0KJulrF4XIC5RAWOaN28ea4Nb+8yd33VGN7tuuND9AoqkL6IMbhLmGKY3uAkNaX5ura+3dkzaV2uPvKn/W1SGQakll51ZFsvPLY9wHDiePvzwQ5NKvdYGI1H9h/XHKa9TKPx7Yb3Bnd49vUWM+/rF6uuvX391Ha17tVYLf0Pl5feBolKxdRQx+seabsP0W3OgMbNp0yb97+T69evRbs+Uf6bzcs6afnw60vycBj239MSMkzMiGNwsedG+rzyp8+j7F/O7G9tvrHLGJjU8kVOsjw6H2CqXa/B3eqXXFRRIU0CnxVtaN+8kBRR+ZPs4Zi0wI8rQ4E7q34wpqlWrpo/KxwSNifHjxyvBTqaj0zlEojO4iabyzBIlY80Ea1uTUHiTuh3mwnITHh/2bec4Pd/jPD4rplMz/37v96r8QLB8mOWXf2p+ZXBz+VD4YmElDqsZ1V4pvNC2Rluk9kitXzfEZHBrwlRh/4n+WQ129ghR6tpWZ0oJSYRVjhSmkvOkxegGowNM8dLgSZCtPgyh+BFPityeqV1J4fW3dLjQY732l19+qSa9li1bqkXF8OHDkT+/rlbGkLtv7ir1ZDK5weQIBpo1Qc8U0wpZu2fYik7D0ACf+tFU5E4d3hpj+43taLq8KdL+nBaj9o7CyL0jUfT3oth8LVwY4onfE+XpJVz8murBHReoDM42ZuYqJmonMjoZZn4/Ex4BHjjU+RCypvyv57Qv4OXipV8Qs/bK1MmSKfVne5xFmRNlUPduXWRPlV1FK9quaasiF5rhzbqXv//+2+p7dV94egED/hmAGW4zMO3sNL2Tgqxqvcoixn3jco0BXacNlZHRoFwDNdY4RuIDRiuZURRbOO/G1snEqB9LKNgLmFHHmIxuRrlTpU+FoiOKoseWHip7Q4NlHdp4XvPpGtzud1t9X+d6nFM9gnl9bf21ePnkpdnCiAmJZnQxRTmuRjdheQ/LDIhdJTs8ehbuhE5so4xRYDpsxx8cr+77osQXFvGbiQ94juQ6hL+1Tz75xKznMGuMGPZPp1OLKvPxdW5ILBYvXqxU9rUOL9FBByBr+/l75fqNWXT8/nlOpa7IuafnVIaKYDmwNITflzELzixQJTsKe8DZK3L5DwNjvMTGsb11/WpcOrQdYfHofOGyx6qMeMHied/xZBnxesEijG4quGop5kzhZ5Ri0KBBJgddry291MRbNXtV1R9Wi3ZbI0z5oxIhP290aN55GtGnHp3CV5u/UvcHBAdg5L6R+u0YQWbrNDomDCM6dGhwIRofMJuDUTFGErV2Q1GxYMECdWFGCLUNmOLOWliqcV/tfRV9f+yL7Wu2q7pY1tbFlAo2qN8gnNx4UtXsDvp4kOq9SWh8n350GlVyVFGpujS8GcVhBMlaeOjzENuubcO6y+tw6skpJZJm+L1SmZz1v/xey2UpZxGGQwqXFFhRfQW6nu0K72BvuJVxU9E6OiTft+8v02C5+MqXL1+cnj9r1ixV/8rotTkY9itmWVB0Ik1KQTTFCyzxWgLvMG8VdUnrkRYv/F+o7+dQl0O4+epmhPFsmM7M61o7q6SoeTbGMI2ede3vQ8siLfHtrm9xG7dxzlF3TBMbzk88V1x8dhHrr6xX6e7fVv8WhdLFnMZvLTAzKjawbpvaKYa/SxrdUQmvWTLMZKIxzUCHpkcQFTwXcA5gJgsdXCw7YSYd2yt+W+1bpY8xbPcw5dRmZpglzKvJGf5uKSBM/RljVeZt17eF33iBCEEvQ+FROpbiUloUXyiV7TDg2ZPH8EqbDo6OTvrybsG6sLe3g0OYo0X8LuhkpSPZULk8NiT9pxAsAi4+DduVMErFiymWnVuGjVc3qnrlmU1mWrXBraUKMtJgDlrkO21oWn3v79QuqZUwmcaMRjNQJktkUS0aEfFFuXLl9D2RozK6OUGMGTNGlWLQicIFEut8v/rqK1Uy8N1336nak1kjZ+FV31cqQmZOjSXrTQmF0qrlqIZcnrlw+81tdd/y88uV0c1jWrBgQZVZYukcvX8UEw5OwLln53D1xdUot5v78VwUz1g80cXSzKF1k9a4nea2WrxOPD4RRYsVVUZ3165d3+t1tZ7ZMaXNRrfYZm0v65yYVWKO0c3FHrelqFV0bL2+FX6t/HStkKhNsCAUG1ZugGMWR/33wwyVmFJemf5sCVFG/mbY4551vaYWsrGBGQ/9K/VHn219cNT+qErddUhEARrONVyY0Gky4dAEdV+zQs1syuCOCzwexk5IZnTEtad9UsI5gZH7HTt2RGl0U4CSzl3q7miaCcyeM0zh712hNyYfnawcnDXm19DriFjS/Jrc4PdGjIMEDCRomXzjyo7Dty2+ReF2hU2ODeoAxeZ3Ua9xU5y99wZ28TVP2dnB2ykNggO84f/woTK4xea2Tuzs7ODiaG8x+0LnaWy7NmiI0S0ouMg1Z1HMmuUv1un6AadySaUUK5MbPJnUq1cP56+ch5+HXwTBNS4YaHBrxrlxRJ3G8vtGHwm/Kxq1NBooHmgMDZFhw4Yp8TYa3ryu0bt3bxXtXr16tYqUcxKJqjWOKbSMABor/JwXel5Q9f00+KYen4rMKTOjb6W+Kl1Wi1xaIjRExv4zVql+R4WDnwNCPEKQM0VOZXCb+l4the7luuOnAz8px0GTRk3g6v3+4jFaendcI91sa8exyFZMLOOICTppDKN+LAkypX6+/fp2tPirha5umVENhCJLhiyYNEEnrGkuNLaZJmwp9OjRw6y2bObQuXRnlYFD9WBqTPSt2DfRDBk6TKi/8s+Zf7DkrC5teHBVndp/cofnATqhmHXEuZdZSNROsUZ4HqTRbYqgoCB069YtUtcDGt2GuDm5qbKDnw7+pG7zPErHpqXOs8kBKvETtn7UlMTJn//+qUp4aueqjYJBBYGg8JIJQ3i+YFcAGu2MlseELnLoDAdHXfQwvlLCw+wc4OeUGm8pFmtQeiRYFylcHVEoW9K0vTSGEe64GtxEjG4BkydPVrW3FJuLicE7ByMkTFdz8zLgZbI8OWpG86Wzl/QLdnrmo4uA8iTSs2dPVScfH0Y3YZoejW5TMIWRBvfEiRNVaxtDqO7P9C9GyKl/wNorCp+Z29qJJ1SmVfKETPh5v6n6DR77PMZvR3/D0N1DMfPkTMzqPgvB/vEj5hXfsBf5Z6s/iyDuRNzXusOpiRPeOL5BFpcsyHckH56HPMexTccsPvKS0iUl+lXqp0SJLma6iKUjl773a7K2mqrHmTJlitPzqQdBw5bK0OYY3XQmaS2YaJzQCPXz84twklt1cRXarm6ruglQpZ0igHR29e7cG3179FWOAnOdBEOGDIElwTkivmDmSo6nOfAy5UsM3TUU807NS7QIIkXUqOw694pOQb5a9mrSEuo/6IBiG1MKvM6ZM0edD+KaSZLUNGzYUDlW+RulJoxxacnVq1eVSKFxD1sa5D/88IP6ndKJ63HNQwlzMhSZLVU2ixPZS24wO4694w0NZpbRzTo5S5+d8FGej5RuiCndDm08U82fAsZJip0dO0IrA1ywUhyczO6DbelYRrxeSFK0FlQxcePljQhiJ5aoQJsYUCmfqdWnTp3S33fl/BV82fBLPLj9wORzuCihInx8prGyTY1m+BrD+nwa0sYGtwbTWOldZrSbi6XY9FKm0c7IjLGIW8ui4RHD269vI1W+VGYZWonNyYcnUWx6Mb3BncE9g/qbKigVnB86K6E4ij1dG3AN+7buw4UdFyy+97xGl9JdVO0so5v5JufD4xeP3+v1WJbw+PHjOLdt8Q/2R5EPi2DfoX1KZI+p/Ma9sg1ZsmQJhg4dqh9njHQbivmsvbQWrVa2Uga3M5zhNN0JR7ocUcZkt07dlPPQ3DpbOhQoWmbL5HMMdz5oEcTE4MSJEyhauSh+P/67un3z9c1ov/fkBBXMWe/Kzg6W3IbUHJhWToeascHNSP7333+v5g9TooAsO2EKMx+ncOLIQSOR6anOsVcndx2Ld3DaMtR24bmdBvfLly/1Qnkrzq/A87fPlXhqk4JN1JqBjntT5wbezwCDuUKafL+rly7g2d1rSsFcEGwVMbqTORRKoufdnP6g/Xf0V1ELnhS1hW5yPTmWKVNG32ZNE8Ohx5+RZVNobXvio2WYRuPGjU1G6uhEoUERkxgTRU42b96sBHFiAxdRTG2n4miE+zOVQk7PnPrbd17cUVkUlpRiznYnLVe2jKByvbL1SsyrMg/eP3tjzPdjkCNzDrPanFgi7M+spVw/efsEw2aElxXEldgY3DSs/rrwF6YenYrWK1vDc5wn9hXYhwM1D8BjrAcqzamEjL9kxKoLq/A64DWOPTimN8Y4F1FoSUOLVmsp7gfuHlARbo13eIeMBTKqCCq/K20RyNpoc2BPbNaPx5e4oSVSKk0pwD+8zpuO0sSAkW7vUt76sUiBwsQy+K3BUKXDlMYqS3SsHZ4HGNE0hHoSmqaIKTSHLzOyKNTI3/2KwTpRw9WXVqt5Wkh8aGBTT4LlZ/xeGWCgSj2/y6nHpqptKJzLuYQla2wxa4rUqVPjm2++UY5Tc43ua5cv4tnd6wiTNHDBhhGjO5lz6NAhdcL74IMPot2OipXsSc3JdmrDqfqFbnKFBq1hOyN67OnZX7hwocne1JrRHd+CTXSYsFe9BlvjMfqtCaFEB9v5kLp168bqPStXrqxqb43VGzkeLva8iLbFdIbR8H3D8f3o79XCy9zWZgkJFw7dN3dXKusO/6WaaSrkO+fvRInCJeLU/9ySYOZJvjTh0c3tT6IXIzPHufTbb7+ZtS0Xyll+zYJPV32qBLxWXlyJ4LDI5QVU9m+1qpVqtVdxdkUUmFoAj3weYdTsUfBM56lST7Ue3IyI8Xd25vEZNF7aGIEhgfp5J0VgCuRNETEtl06eUaN07bJigqUZzPgwp+bQWsmSPgswBUjnlk5lB8w7PS/B35O1nI+eP8IN53BDLLlmRZmCWhcULo1ryYalQZV6OsjYzULLHGGt9927d6M1uqhdQFE5bkNhtdz2uZHBMYOaH2h4C4kPS3ro/GR7N5b00ClEh8q+O/tw4tEJONs748syX6pt6TSJrqUjnUosozAHOmFy5s4Lr8w5VDq4INgqYnQnc9gqjNFXrY7SFO9C3qHP1j7qevey3VE4ffQtqpID7OlNA5f9RkeOHKnS6bp3765Ue1mnZwwNVBq35qqkm0ufPn0i9FBet24d3N3dzYpeN23aVCmRm5PlYC40iKY3mo4MHhlw9eVVNBnTRAlbUSk9qZl9cjYWnV2kDO4t7bZE6BfMxQZbW9HIs2b4Wc58dQZzPp6jaiQfpHmgRMfiAo1fZnBwPJnj0Oi0rhPeBL7R39exZEd9H3jPME+kd9Q5nNiXN41rGn22AaPzWSdmxfiX4xHcPRjvwnTRbn4XNLwP3DyADxZ8oF6bavl3+t1R313BPQWRK2uuSH2+GcE2Byr/W0KrsIREOfn8gW/Lf6tu/7D/B9X2LiFhdGzctnHwCfVBDs8cONj5YLLOirJ1+vbtq8TReN6jqBYd+CynMmfeMC5JeLpDV+aWGM4hIeL8zbmTrUSZhaE5S1ibfeXmFTRd3lTddnF0Ub9jOtFZIsFSiajga40dO9asw0wDv1ipMsictyjsE7HLgiAkNmJ0J3OoLkqhE8MWHsb029pP1QOSLde3SG0eFVfddL2Q2Q6FgmWMZHOxyRONqawBRsapes5t4hMaDYZiajS669evr/bPHOKqSh0dnq6eGFdnnG5/Xq1D53GdMXbC2AhCffSm0wvOOvfE4PC9w+i5RSdS9eMHP6Je3nr6FHLuC1OTbSHVk/AzUbm6UfpG6nbndZ2x9/beWP9u79y5o1IMYxojXLAN2jkIKy+tjBDZ/L3R76oPPA3kkgdKouKJiur6g/4PcKvfLXg5hCvma2nIwe7BKDitIIbtGoY2q9oguEcwlngsUaKNzg7OWNFyhWoDxu/u90m/R9IsYOslRl9iyqzguKNDgWKEtgwdardv30avar1QPENxldLfa0uvBJ3DGVGfcW6Guv5NlW9QJXsVMbhtGBrXFEVjZJu9t2mMsU1gbFGt1E5TS81OzVc3X+naFQoJD+dLZrBxHcO0cA0a3RdfXYR3oK6u2+edjyoTefTokcqQNKVcrsHzRnSRcEFIjojRbQMwYjTzxEwcfng41ospig7VqVMnysfZk3v6ien62xRoktq88DRBRrm58NcME6YnMyXXGB8fHxUVj29oNNBDzVosngiPHDmCZs2aIanpWKojSmcqDd8gX8wNnAvPwZ44cPSALsW7e3cVgWMKorme8PeBkb2a82sqPQIPJw/0rBCuEE2jkt569i23NX5r/BvwGnjo9xC1F9RGqT9LxWp+MLddGJWxfz38q7rO0hPDDAKtxVrNKjVx5J8jKJ+lvFIcX710NW71v4VGDxvBdZorMrmFp9oy8j32wFisuLACd3zuRMi4YS9fDdYeGncCoFo6f2cxtWCiYUDxJ1uPdPMzMlvA2ckZYz7Q1dcuO78MRf8ommCGd8OBDZWQIp0jnUp3SpD3ECwPisINHjxY/bZatWoV6+cz465YjmLIHKDLBhu7f6w4+BNBpZznPkaad+/erUpDDNXGaXQ/8X8SqUzk5k2dQyQmo5simInlWBcEa0CMbhuArY+6b+mOFhtboMzMMmafqFgP3KtXryiNQRrXX27U1e94ueqiUlKbFw5VnYlhD2ytR+1XX30V4T5GAhKidQaNBhqyTM2jeNpHH32ERo10Ec6kxN7OPoJxyxZcPUf1VLVbTF2jQjXTEtmyjCnuCQkzNWhwE78gP1x8djGC4JPxQsNWyJ8zP8a3GB9n9Woa3RQly5YtW5TbUBBN66+b3j29irCbEqFjFOv58+eqhdCMGTPQtWtXPH/0HMt/XY6caXMC09hjCFhQdAFG1IzYN90l0CXS3MNuC/zdGSqbq23y64TCYhpTFFyjMq8pB5ktwWhUmzZtVBlRphThjg0axQnhPOVctCdoj75vvKSUC7GhZs2aCD6p04GYc3pOrB2FQuxYtGiR3oBmNp5xSQBbGDYe2Fhdp1aL5kxlsIatRqPLDqPRzfmATpiYYFnelnWrcengNoSGWGabUUGID8TotgEM+2THZmFNhVHWsprqf0dho4+WfKROeHXz1NXXUUptXkQROqaopkyZMsKxoxoyDQtDdXOmbcWncrlGoUKFULVqVbW4piFBNXIK1FgCPEl7uniq6xTgy+Ol84qz/o9G948//qiilZrIXHxDI+/I/SNYfG6x/j5jpxHTINknlvthi/Sq1EvvMGPaZmwE7dq2basE+Qx7ZBvy1O8pum3qpr/97O2zKOeeSpUqqXKMHTt24KeffsIXX3yhoigUMWN5S6WylXDn0B20btwag6sO1qtss1d64KRAbGq6KcLcQ+OdWRJaOxsNZi2w5CO6WkPC48D9ia6sxhaglgS7GbCVGsc9a6w1EsKYmX9kPkLS6boasL2kGExCbKDDuFTOUknS5i65QacjS0+iczwG2QVh49WN6nr/yv318y+zZyicF10ZGx2gFGs17EgRHVQtp5EuCLaMba84kglflfsKKZ11hp+TvRPyekVU9I0KqlJy4Wu88GQ6br6p+XDP+54ylijKlNIlpdW2UUooWD9avHhxkwJlPCmx7YYGDcv4Vi4nNIioEsp6LBo05p7gEgOOlSu9riCjR0ZV5zn2n4ip5Dxhr1y5ElWqVIn396bTI1+hfPh81eeqXjjHmxyYUnxKJKcRjW6muUdlWFo7WzdsRYrZKVAhSwV1HD5b85nZQlp03kTljODiqNvGbngV8ErVWseUBcPU02nTpiml/RcvXijHi2ELOhqGjJ7QAcjvh98TnXwbG21UQmApvVNG+N60CDdbfhnCyDxb4cVUn8+oDo1/W4eZJZx3OP/w+F3qeQlN8jdRjw3fPTzeF7njj4RnVrAmVwwmITY0bNgQa39fq1/PUHRRVO8ThlOnTqm/0bUWXXdpHfyD/ZHJORPKZi6rv3/btm2q3Vt0UDSWTltzssh4/q3ToDHyl68NOxFSE2wYMbptAC6mLnS/gDQuaVQa7U8HfjI7fZRGtyGBwYFosqyJPkJBY+mxry6NWjAPqi4PGjRIRfCocJ6QRjfhwvn3339XAmpMlbYkMqbIiPnN5qvrk45OwpxTcyJFv5gKTK95bKKw/MxUqabTo2zZsirrQGPp0qWqbjx319y48eYG0rmkQ8p/UqLPJ31w4sgJ/XaMkl68eFGl5NsqHHP3rt/DL6V/Qe7UuVW7NGawUFArJvidRCWI9Oe/f2L9lfXKybe/436zsmAY3d60aZNqrxddLSDRasGL5C+iDEfjPsA0umnI82IMdQ0oKBgV7DTADJUsWbIgOUAHICPd+u4CjafDzdENB+8dxNrLa+PtfY7eP4or3leUaj6RUiQhLvi+8sW3xXRq+zS+OVaFhDG6qfmgleSY4q+Lf6m/+QPzq3lYg6U9prq0mOqAQSdrTPC1Xd3c4OTiGuF9BMHWEKPbRsiaKit+q6Xrp0tRozknIxs3poxuQ5GkV/6vUGdhHRy6d0ilohJZOMWNLl26KE8vo3sJbXQzSsh0bdZKG0f+LIEG+Rrgs+KfqetfbvgSJaeXjDA2WZ87ceJELFmyxKzXo3Ac05U/+eQTZTjRycH7yIYNG5RxV75XeZx2Pa2Pfh45cESl0dEZokX3aLAxBb1ly5awVeiQYBTh4omLWNpiqfpdH31wFJl/zYxbr6KutaPAHMeusbFLzj05p1eCZ/lA8YzFzcqCYRSb37NhlDsm+BzWlBur4NLojmqs0+nC30NUsI6f2LqImkbjxo2xdetWvXYHzxUDqwxU13us7YFzF8/Fy/uMP6iLcldJWQVHuhyRUiQhTrC95IKhC5ToJbPt2B9aiH8ooDt16tQos7yYEbXt+jZ13fOurkxMO1+zdM6cVqNsP9auXbt43GtBsG7E6LYh6uWqhzZF26g0UgqgFZxaMFrDm2JfXJCR52+fI/tv2VX0gwvzla1WSg33e0BjgYJqWor5+fPnY2VsxIZy5cqpv6zttlTYs1nj+qvrynDT4H7T8B0yZIhSeY8J1sazjp4tx3iMKQjYsWNHZUwzZbjIl0VwLG14G7WHPg9x8flFJfxCHYNVq1ap+xlZZ4q7ue3VrBFGMuig4HFiGYnWmisgOACV5lTCgbsHcOzBsUjzBGv9WKpgHAW59uIa6i6qq3+d5/7PzU4h5uKOC7DYtmb7+eefVcmG8Zj//PPPTW7PfaazgI6DqPpzc/wULFgQyQHW5jMTxjCCNKjKIKR3S48nQU9QdnDZ9669vvL8CtZd1mUXzP5iNipmqyilSEKcxdSunL+COjl0XVVWXdTN10L8wrTyTp2i7i6w5tIalTnpFeiF55ee6+9n61PCzLqYYCalOW3DeC6+ce0KXty/ibBYZLwJgrUhRreN0alU+CR63+c+Wq5oiZ03dppcVDF9tFatWmohXWl2JaXsTLigzu6ZXWq435OiRYuqKCujqVQFpWhUQsBa2IULF+LPP/+EpVI1R1VkTZlVf3vXrV0RHqdB/OrVK4waNcoso/vvv//W90PXBLF8Anzg3MIZ5zKfM6m4T898//79Va90qqUy7XbZsmVIDnWSPF4FvQrqBcqYFk4htOrzqqPi7IooMb1EhDni4MGD6q9hH2sa1zXm11DP4/MTKxOG6tvG/bSZok4nTVRGNx0G7BtsCva1p9Fu6yJqGnRy0Cll6FyiRkfHvDpHWFC5IOSdmPe9DO8Jhyao80b5lOVx5aCupEYQ4kKNGjXU37yButK31ZdWi8BWPEOxVwYEomutuOSsLvOslEupCBlPzJqhwZ4pU3g3hKhgJiUduNG1DeNcTaP78vmzeHL7ihJUEwRbJV5XHYwiCUlLlexV9CrRZOuNrai3uB7yTM4Dv3c6o5pwIly9YTXG7R+HsjPLqv7bDna6NCNJKY8/2KqHaeVM/aaIVELRvn17eHnpjExLhKnHV3tfxYBKA/QiTpuvbdY/TgOYaYWzZs1SxndUMFL6xx9/RLr/md8zZJ+cHfv89qnbI2qMwP3+9yNla3ChwUUdo+P379+PsQe1LcD2XMy08HT31AuUsRtB1ezhmRH8/Xdc1xHrL69XxhdFctiHnsruZP+d/ag6t6rSdyiRsYT6LhOrmwHTGdlyzzALgueaqMZJTG3D2KbOnHpEW4LaEsxsCgwM1N/XrEIz/fXHAY9x9snZOL32A+8HWHhmoboediBMpfcLQlxhOQk1H/xO+8HV0RXXX16P89gUTMO1CAUno+ocQhHEvXf2qutXPa5i9oLZesdHiRIl1DnFHCiSyowjir2agvN6gwYNlBM8W45cSJ0hKwu85WsTbJZ4NbpbtWoVny8nxKL1w2effaYmUi6Az3U/pxbE0xrq6onJE78nqLWgFs4+PosDdw7gu1XfoeXRlhi6Zyh83vmgSrYqSmla2oLFL5oyNz3KPLEkZzg2J9SbgG5luumUtFd/hnXX1+kjbMy8oMBVVM4DCp/99ddfEdJk+dzFZxcrx5F3YHj7qEYFGunFuIyNQqa70fBmOyXWPNs6GTJkUNkQRDsmmVNmxrbPtylleY2VF1ei2YpmSDM+Dfwr+KNpv6bos7WP6pVbc35NdXxdHFyw+bPNyJU6V6Jlwvj7+2PEiBFYv369us2oCVPmtdvG0IHDekV+v6ZgLX9MLcVsDRrbVPTfvXu3uk0nzLu775DLMzzVf/ct3WOxZcLBCSoNlecQv8t+yUagTkjY1mFOYU5KD0SLdgvxB2uymYVXpEgRk4//fux3/fUHfg+QqUQm/Xn3m2++UUKl5kChU84HxgEHCqyxvOf169fKwcuspZJlyyNLgRKwF/VywYZxjO0TKIxgCnrBaPwJiQ/rE6nkTA8k2x9pC2umfVIxmp5i1mn/+/BflJxh0L5B1+lHMeHDCcibJi/4T4g/XFxc1AmFtcayGNWplE79aCouPLug9AO67+qOH479gGu9r8Hd2V0ZTEx9u3TpUiShK9Zw03FBz7hWq8206Bf+OnVUZmqEhIXEmKnBOmeSnFKMKVLHCCT7uGukcE6Bm31vqvr6fXf2YfDfg9X9gSGBWPLwP1G7+xFfh4/xuGdLlS3R9p1jgs4rlgKwjpsZCiQqITUK6zGd3hS8n+JBPBbaOEgOcPHLrI7Vq1ercoMff/xRLYTPnzmvWvmNPTAWI/eORE7PnGheuLnZzpRLzy5hyrEp6vrtN7fh88RHZfUIwvswZYpuTNGhSq0A1nWPrj1aDmo8KpdzvRiVY5JaHxr5vPJh45yNsP/IXp1/PT09UbhwYbPP98yyMS6tY4eJq1evqtIhZjX06tUL+YsUR8FqOo0hQbBVYm10c9GyaNGiSD8iGt3sySckPpw4OQnSUDGEC6czX51RtZjuju7osqELjj0MF5hyCHZAiKPOSCmVuVQS7HnyYOzYsSr9W4xuHezrPKLmCNRfrBNioRHXf1t/dCrdSSlhf/vtt/p2a1p6s9YblN7xZ07PMGDFAGy5tkUZgRprPl2DTCkyKYM7OqOBqvIU00pOxgGdP2z/defOHWXEavA4UfSKx332ydm49vIavJy9wH83393Ub5clZRb1PSVV6QnFwL7++mvVfiaqHt3GkV2mTjJV1ZB9+/ap1HRqLCQnuPil2v/s2bOVwU0nLeclD2cPjPlgDI7dPYa/7/6Nz9d+jnx78+FM9zMxGt4851OwUxPV4/iAG2SeE+IF/oaLOxdX+hGXnl/CivMr0KRgExHoi6dIN7OFTHH79W21TmSgZlPbTaiZqyaypM+ClK4pVStGrmM0MVJzoK3AuYJ125pSOoU906ZNqzqK0PnN/Rky8Gss2FYVqVJbbpmcILwvsQ7zUHiLkVUqTBpeeD89Z0LSwPpLrW2SIfqod8Zi2N1htz5C5eTjhM4+nSWdPJEWvFGlcSVXquWopjzoGjNOzlBq2kX/KIqvv/lapRCPGzdO/zhP2hsPbETQJ0GoOKei6i9Mg9tQ0KtunrpmpzzzRE/jO7lQt25dteChCI4peMy0eu+2T9oieE6wXnSNf+m8S8rSE5YucdHGSK1mdBsb1IawZZipljYUUWMGRXLsBduiRQvltOjWrZv6PbG1HuGx6F+1f5TdBaKC5QhsL6mRxzMPWtZoKXOdEG9tN7t83gUf5NYJZrZZ3UaVuryv0n5yh799llcx48UUy88vV39r566Njwp8pBxzVCFnq0U6LLVMM3PhuZzR7MWLF+vvo9H94YcfqnMS55/Jkydj3E/j8ejyvwgNSd5leIJtY7bRrYnY0EOuqUsao7USEJImfZBGtyZ2YQpOnqzbZg/VaueqoWrpqqJQLiQJNNxO/e8UNjfbjL4V+0bwsnff1x2fDvgUk9ZNwuFLh5UB0G5NOzxt/RQ3ncOjr2Rn+53iODKD1KlTqxTtqIxu7Tuh0+LIP0dQs0pNvRHOv+nc0yXpXEHF+QkTJqgafPabZk12dNFqiqndunVL1Q5qcG5khoOxEnpygc6GSZMmqYy0Zs2aKYFHjZq5a8LLITzCtPNm9Ofy1wGv0Xeb7nc7tNpQNU7O9TyHlUtXKmVjQYiPuu4TJ04gv0d420Jm4pjbolCIpsRr6lQ1B0RndLct1lZ/H43ulStXKsdnbI1utk9lxxEa2oSOP36vhi3HuA2DeSHJXPdGsH3MTi+vXr26Su80p02AkETCI05OquZGS+EhXHRu2LBBRTk42WrppLu3x000RxDiC47FMhnLoEahGqon6D1vXQRzx40dAAORnwNV/tIJ0WnUylkLt17fwp03d1QEtnzW8pJuaCYfffQRxowZo9I2mW4elVgda30plKMZ4ZYC270RGt5U4o4OGt1UzWWXBk3NnG1vqHhurBWQXOD8z+NGNXjjHrv8rq/1uYbMn2VGUPkgjNgzQqnUf1zwY5OvNWDHAKVkz98gS0VcHF3UYvrF2xfRpv0LQmzmK65p0t5Oq78vd+rcSVLeYkvcvHlTGc+mOndQo+HMkzMqg6xF4RYRjG4tozK6DKOooNbQzJkz1fuyZIwBIgp8anDNWuvDBjj/wBt2IqQm2DBmR7rpva5YsWKkFGYu0Dg5CknfE5rtlIyFMZjS07Jlywgid0z3MYwACUJSwgX/5V6XVbTsYOeDqJO7TpTbjv9wPC72vCjR7TjAdGJGOaMSzyFspcaFEZ2slsiCBQuwdu3aGLcrUKCA+kuxHg1GdikoF1UtY3KAzhbWdJvKVkubKi3aZWiHDHcy6LsLzDs1L0I6b2BwIH7Y9wPmnpqrbvsH+SvxQsI2bMy4EoT4gIJd7EKwb8M+fXvDHuV7iJP1Pfn555/RvHlzk48tO79M/a2frz7SuIXrqWilOhSyjAt08j1//lzVbtP5x/mZ2VcavM8jRUq4uHkky9IfIflgttE9b948dOzYEdWqVVM997iYoZI5ow6GkVUh6aAyMftCG8J0SopPUbRCY+HChSqVJ7m3sBIsBy2qyj7zG9puQF4vnWedf/Ok1vWdT2+fXi+SlpSpztYKBXAoXBOdYjvnBRrnmtFqadDgZtYODcfooEiem5ubXulcW8SztaLhYk+IyISfJ+Dm7zdRI0cN+AX5ofOGzkj3czrMPzUfvx76FUX+KIIRe0fot7/vc1+f7vvgwYNkJU4oJDz8rTMzp1lBXSr05mvh3ReEuCuX8zxgDMtvNKPbMLVcM5r5+ODBug4XsaVy5crq3LJ3717lSNm0aVMc914QkpGQ2qhRo1SKHwUQmGbCOm9GRjZu3JhweyiYDdN3Zs2aFeE+TnJMDZo2bZq+3pt9irkAZ2sdQbA0aEyf7X5WRbN3ttiJm4NuArOAnZ/sFEP7PeFip2nTpqou2hSs+2Y02VKjDVQxJ1Rhjw46Fpjd87///U9/H2vCOR8KUcPaSw83DwyuHL649g/2R6cNnTBw50DcfHUTGdwz6Pu7G6rZP3z4UJTLhXjlyy+/xKFDh/BJkU/U7f139qsSBiFuMMPx7NmzJo3uk49OqvayLg4uSpQ0PmF21fnz55XtsHv3blXDbQizq27fvI6Xj+4gLDQ0Xt9bEKzS6H7y5An69u2ragKpxMwfESPfyVWUxhJh2zBObIbfGduIMcrdu3dvXLhwQW90m6rnEQRLQYtmZ8+UHWAlxAOgRGHpjvC+0AG3Y8cODBw4MNJjjChR7CYqg9wSaNKkifoblQiQIVzY0YHAdn0UEPv+++9VeqMQc1Zbl3pd9N0FUjqnjPD48pbLVX93YzV7iXQL8Q2df6rd1MtQlMxYEqFhodh4VYI8cYWZkCwvZMaqMQtOL1B/2RWk2txq8a4SnyNHDnXuYQaS8fvT6L5w5hQe37iIsDAxugXbxWyjm2qxrAekgiGVB9m6ha1HGD0QLMfoZm/jd+/e6evtmfo/evRoeHh4YP369ep+MboFa4HZGHQWHTlyxGKjr9Y2R0ycOBHTp09XAouGsCUMlWnpqLNUqFjORbi5OiJczF27dg1DhgyBv7+/xdaqWxIsGXt87zHG5RynDGsa2PnS5NNHtinEaarEgyJ1zKAShPjkjz/+UJmVjfI0UrfXXV4nBziOPHr0SGkzGUe6g0ODseTckgRViWdm7DfffKPmYeNIN8/tmbJkQ6p0mXgjXt9XECwJu7DoekwZsHz5crRp0ybCfYwaNG7cWIky/P7777BFqObLWsA3b94gVapUsFS4uGSaPyNATB/SBG040bGWhmJqd+/eVX0WeZuGuKYGLAhJNWappEwV0+jqjIX4hVM+54mDBw+quYKGEue3fv36YdWqVcp4srXSE6ZVMvMnLsq7yXHMlixZEoUKFcKKFSvUbUa9uAjXNBWiGldUjLe1sWPtWPuYZSkJ+zx/O+lb/PjyR7g6uuL5oOeqBaoQe/g7NXZgb7u+DQ2XNIS9nb3KJqBzzTCLJb6gs5SaIca2BHnuG4jTd1/r9zHE7zUcPFKLs11AKjcnVMgdLuxnzbai2TOwqR8JvWWst2GNhpD0cJHE9EvNj8K/NLAJ6zgpqkZPJ2vvWCslCELygwsuKk0zesS6Z2pBsM0Tlb0plGOLRhPLod7X4E5OUKWYmRBcQJBnD58hr2veaBfhHFe2OHaEpCVnzpwqaLB00lLVMiwgOEDXVlKIFWyfyPneVMbYorOL1N+upbsmaGeQLVu2mLQlBCG58N5uz1y5cinDW0h66GVZt24dSpQooaI6GTNmVErzWh9vGtpMPacXxpKj9oIgJLxgFkXFaHizdIiaD1yUDR8+XA69oATr6LTV6vsbNmyo+ptHlRhHLRFGxw1btAlCfEFn4K2bt1DYrrC6PevkrHivObZ1mNrN8iFjfAJ9sPaSrg1j5zKdk6wziIujPZwdrS8TQxBiQ7yMcC8vr/h4GSEeYGTi4sWL2LdvH549e6aEk0iaNGmUsjnbRbAkgClngiAIVJRlCy6pxxU0mBVAB27mzJnV7V69euHWrVuqRMkUdNiwVIHaIYIQ3zCrktkXxVx0Svlbr29FqT9LieFtJiwzZPkhMwaMWXNpjepQUCBtAZTPUh5JAdvX7tq2GS8uHEC+9G5wc5Y2xIJtIm4lG2PcuHEqKsEoFvtza4smreagR48eqpbTGmu7BEEQhMTBMCqmLdbZItQULFniOYXZVYKQECxatAjNPmqWoGJftgodaFQtN5XavfDsQvX3ixJfJGn9NAXWuI/ZvNxROU8a5E7vgSypXeEk0W/BhhDLy8ZguiijEVQqr1WrVoTHXrx4gcePH+P1a51YhSAIgiDEBEW42GYyqlIytgujwS013UJCkj40PZzhrK6nc0un7xEvRM+yZctQtWpV1bbLkPve97Hn1h51vV2Jdkl2GNllh04+Xnidxr+nmxMKZ06FGvnToXSO1HCX6LdgA1iN0U0BiHbt2qla5NSpU6NLly7w9fWN9jkUCKLhyefwR5wcjE1NtZyRB2Ojm7WbpH79+kmyb4IgCIJ10qdPH1SqVMnkYzzfZM2aNdH3SUhenD5+Gu926FqilslcJklqjy2FESNGYMqUKTFux1JCFxcXpRpuzLxT8xCGMFTNXhW5UudCUsH1OctUeTGOtvN22hQuSr06k2fEVmOCYG1YjdQoDW4qb+/cuVO1f+nUqZPqE7506dIon0MRGM179u233yK5KJjTUzhw4EAlnmYMU8ydnXWeYkEQBEEwB4rtRfcYW80JQkLC9rTZxmfDfdzHofuHEBQSBCcHp2R30CdOnIhff/0V1apVU86w6GDZx9q1OqE0Q/ze+WHM/jH6VH0K01myE8PRwR7FsnoiXQoXPPYOQCpXRxUNd3FywNl7r/H2XUhS76Ig2IbRfenSJWzbtk21vCpXrpy6b+rUqarn3y+//BKlABD7zhLWNycX6NFktJutwrR2YYaYuk8QBEEQYhI7YnvQ/Pnz67OmNNgxQxASGgYUahWuheXvlsMXvjjx6AQqZTOdfWGr0Lk1bNgwVf98+fLlGLc/efIkihYtqtaGhsw5NQfvQnVZA0/9nqr6eCqXJwWMxlOMUeuIFB2MdhtHvMvk9MLJO6+iNbwpY8TmC1E0YEg2UKTO3s4OIaFhCA4NVX+T+zFJTKwivZziLUwp1wxuUrduXeXBO3r0aJLumyXCSZaTsiAIgiDEB0zz/OSTT7BixYpIj40ZMwYnTpyQAy0kODlz5ITTQ110e+/t5BNQ0Vi8eLFygI0fP17p90RXZslsT9Zy//HHH5EeW3d5nf56/jT5k7Q+nkY31/K8xKWzjquTgzK8o6r7dnGyR7lcaVAye2o42McsFufgkHSCcuZAB0IKV0ez6twdHeyQObUrimZNhWr506FqvnSonDetul6rYAZUypMWXh4xZ78mocaeTWEVkW6Kf1HIxRAKtrANFh+LTwIDA9XFMB2bcCKw5DZb3Df2UOVfOiOi6qcqCJY4ZgXBGkjOY5ZGd8WKFVX3C8PPz/Pld999pzLOSpcunaT7KNj+mK1evToO7T2EPdiD3bd245sq38CaYVo3o8ya0Xvm8RkUTFcQro6u8H3ni1uvbqF4xuIq9ZvfI7WKPv74Y9SoUUNtz2g3W6qZYteuXSoizhJLw+//5KOT2HN7D+xhj9WtVqNu3rpwdXBNsjHCz5UpUyb99biMWWcHO5TK7okbz3zxzPcdQkJ0a2BPdycUz5oKLo4O3AilsnnizIM3CAqO/No0YvNl8EBqd2dcfuyDp97htkB84u7iADcnnWAcbdk3/kF4Z2J/DLdP4+GENG7O8HB11D+XBIeEwjcgGL7vglWknxf/dyHKKM+UygVpPVxgb+BoMD6mbk72KJ3dE0/eBODaM18EBkXej1RuumP4NjAYD98E4JlvIBJzqIRZwfxl7v4lqdE9ZMgQ5a2LKbU8sVtujRo1KtL97HnNycuSv3D26ObglHZggjUgY1awNpL7mGUa+YIFC/DkyRP9ok/r3e3u7o6nT58m8R4Ktj5mS5Ysie+yfYc9q/bgwN0DuP/oPpwdrE+nhvXoO+7sQL+9/eAb5AsHOCAUoUrYzBh3R3f0LNkTIc9DcP7KeZXJmD59erVWZQAqqt/d6tWrkTNnTpUparjN2L1j1d9m+ZqhUppK8H3FZP3ohYkTmsKFC+u77LzPmE3vCKRNFQbvwCAEvAtBBpcwvHn5IsI2ud1DcPuFH2iXcxajTZrGnQaqM8L8vfHKH8joBNg7BeKRt3+8GpjpUjoji7sb7O3DU+HTOoTi/su3eOMfrL+PHzt9Sp3R7KwstRCEBQTANwAmvyn+ApztgNSsIlCVBCEI8w/Ec3/z9ovHIW+KULzwfaeM6qBg3ThMk8IJ2Vzd4f1KdwwzOAFeqULxzCdQXRIjvhcY7ICnT4Ngyfj4+Fi+0T1gwAB07Ngx2m3y5MmjPGDGkwrTa6hornnH4gsKrvXv3z9CpDt79uxqgqMKuqXCSYqLIO6nLZxYBdtHxqxgbST3Mcuyrt9++02dF1nbTa5du6b+sm7UOCNNSHpsbcxSSNfllgvSuqbFi4AXuBN8B1UzV4W1cPnZZQzfOxz7bu/Dy4CX+vtDEHU98tvgt5hwYoK67jLcBaU/KI3c6XJj+PDhUT6HBuuePXtUVJzt/DRuv76NDTc2qOvDaw+3yN9sfIzZmCyD7GY0W+ChyR4QpIxLRo/9VCQ52Cwj3MPFUX0HWp05P0bBTCmRxdPN5PZZMwMP3/jj2hNfZPJ0Qa60HrrofCKTWR3/MOVsoEHNvulR7e+bt0G49NgHfoHhzoKEwMXNCRkyeMGScXV1tXyjmz8oXmKicuXKqt0Xa8bKli2r7qOgC3+YTHeLTyg2YSw4QfjDt/QTFicpa9hPQdCQMStYG8l5zFapUkWdj+nV1z4/u4qQbNmyJctjYg3Y0phlOUPVKlVR4ZcKeIEX2HdnH6rnrA5rYP3l9WjxVwuEhumstvRu6REUGoTXga+RJWUWONo74u6bu8jrlRd/f/E36i6sixuvbsDTxRNvAt+o5wSGBaLMrDIYXXs0UrxKgVT+qdC2ZdtI78WgFI3tJk2aRPjeJx+djJCwENTLWw+ls1huOYiljNnU7i7qokED88z913gbGLWTJEtqNxTKlFKldQcGhyjj1NXZAalco1faz+blgayp3SO1TUtseMizp0kR43ZeKVxQKY8zHrz219fWuzrZw8lB953xY9BB8eiNPx6+DkBAUMwK83xOVi835EjjDr/AEHgHBFlFlo65+2cVNd1MO2FNSteuXfHnn38qT2evXr3Qpk0bvXL5gwcPUKdOHSxcuBAVKugUGFnvzcv169fV7XPnzin17hw5cqh6cEEQBEEQzINpqocOHYpwX+bMmdG+fXv1mCAkNMw45FjLHMCYHFRt8vAaUUd8LQHWZg/YPgAzT86McP+6tutQKlOpCDXd2nXWcJ/tflbdxiug9uLaeOv2Fi4OLiryPXDnQLW9k68TmjZtGqndF9e4x44di3Dfi7cvMOvkLHW9d4WoWwAmNsxc3bp1q7resGFDizawGMGukCsNLjz0VhFwY4OxQMaUyJ4m/LtgtDpDKvMj1kltcMcWOhYMP68p8qRPgdzpPPDc9x1evX2njG9mDvgHhSD4v9p7wjr0wplSqTp84u7sqFLsbQnLHdlGLFmyRPWgpmHNVmHsT0hBCQ0a4leuXFFqjRo00CnsQmOdUHiCtzds0KXWCIIgCIJgPiEhIbh69aq6zjRzRr/p7La2xaJgvTBw4vZEl6bLuu5X/pbbI577lumXTHqDO5VLKr1iOA1uGsts1cW/hteJs50z9i/bj1oVaiHdqnSYVnIang16hsFVB+tfPyhFEP46/1ek92XZh6GgLq/32toL/sG6qGT/7f2ViJulQBX26JTYLa1nOJXQ82dMoZTBaXTmTu+Bsjm9YjRAkyuqXCCli3JKlMiWGhXzpFXq6R8UyqCU1CvkSYOKudPoDW5bxS5MZK6jhYsKT09PJepg6TXdrHtnfY4lewkFQUPGrGBtyJgF5s2bhy5dumDKlCn44YcflPho586dk/qrEZLRmGXKdLB9MHaW3alSpbOlzIYrva9EivYmNcGhwfhw0YcRWpvt67hPKZNr0ezoaNGiBdatW4e+ffuqtnweHh7qfhrLRX4vgjtv7uiN8+9qfYf+lfur12Q5Zrp06VRgSvttTj06FX229Ynw+ke/PJpkvbkNoRny/PlzdZ37zdu2NmYF28ZcW1FGsyAIgiAIZsGabi6Ke/fujQ8//BD16tWTIyckKgUKFICvm68yuMl9n/u6NGwLgnXbXTd2jWBwM7pdLku5CNHs6ChYsCCWLVumxAs1g5vwuRd7XsSyhsuAG8C7sHf4bs93yPJrFjzze4adO3eqjBQKH2rZAP136ASC07mns4je3IZoomm8SMaMYMtYRU23IAiCIAhJDw2BCRMmqLRyXgQhsfn1119VtDf35Nx46vcUbo5uFmNAEuWU2tIb80/PV72wl7RYgjxp8pgV3TaEWSRRwdf5tPynmPZuGg7ioLqPYmtlZ5ZFkctFkKd6HqTLnA7XX1xH0+VNVdS9TbE2mN1kNi48uxDrfREE4f0Ro1sQBEEQBLNgJGrgQJ2IkyAkFTQYd3y+A6VmlFIGpZ3qNBz/bNy4UfWgp56Qufz4z4/4498/1PX0HunxcaGPozRwt2/frtKoqTdkCHUT/P39VV/y6H6LOxbtQKk/S+Hay2twsHPAPe97uJf5HpAF8BgbHh1nL/MpDabAw9nDIlLKjUsg7t+/r++CIAi2iqSXC4IgCIIgCFbB6dOndfW+z+xVqy223Tp8/3C8vgcNXkasNe0Cc9l0dZNK9dZ44vckytR3vgc783zxxReRHps0aRI+++yzGN/PLtgOqz9creqzb/e7jcoZK8OU/+FdyDvcen0LlgiN7oMHD6oLrwuCrSJGtyAIgiAIgmAVpE2bFs+ePcO9e/dQO1dtdd+eW3vitYUVu+QMHjxYGcT79u3D7du3Y3zehacX8NlqnaHM3tox1U6vXbtW/V2zZk2kx06dOoUyZcrE+J6s965RuQbKZymPbKmyYWfnncjhmUM9xr+5U+eOcT+SGqnpFpILkl4uCIIgCIIgWAXsDe/g4IC7d++idoXaWHJuierXHV/Q2D5w4ABGjRqlDF+KmC1evBjDh0fdD/zem3uot7gefN75oFauWlj36TpceXEl2trpuXPnqla2efLkQUBAAFxdXdX9FEE7c+YMWrVqFeO+spUu1cqp9q21zL3U81KUvb8tEX6XmugbkWi3YKtIpFsQBEEQBEGwChwdHZE1a1ad0Z1bF+k+9uAY/N75vfdrL126FBMnTlRibTSIU6RIgU8++UT1oo+qw65PoA8KTiuIhz4P4WjviAXNFsDT1TNalfJbt25h165d6NChA7JkyYLZs2frH7ty5YpKPTcn0l24cGH19/LlyxgxYoTqKEBhuah6fwuCkHSI0S0IgiAIgiBYDTly5FBGN9OnmUbNuu6D93Qq3nGFhuuXX36J9u3bq5Z4Gj169ECvXr1U2rkp+m3rB/9gf3Wdom6PfR+bFa1nO7BPP/0U+fLlwz///KN/7MWLF8ifPz9KlSoV4+vkzZtXRYr37NmDFStWqH2VtluCYJlIerkgCIIgCIJgNfz5559IlSqVMjBZ173gzAJV110vb9z7xtOAHTlypDK4DQ3XihUrqosptl/fjrmn5+pvm1s7zVTyNm3aqOvVq1fXR9L5vrxN9XJzcHZ2VgY6U+EZle/SpQusDabT79ixQ12vV6+eOA0Em0Ui3YIgCIIgCILVULRoUWTPnl1dZw01iWtdN42+CxcuwMnJCd988w3c3NwibUMhNbbKCwwM1N933/s+Pl/7ubrepXQXpSB++qvTMaZy//3336pe29fXV92mkf3o0SPcvHlT3fbx8YnV/h8/fhzp0qVDp06d4OmpE3CzJuhsYF06L1Gl8AuCLSBGtyAIgiAIgmA10NCkkUmDWVMw//fhv6q+OrawFrpChQp48uRJlNtQpIx13ps3b1a3g0KC0PKvlnj+9jlKZiyJaR9NM7t2eubMmbh06ZISaCNVq1ZV0d1///1XGZ10JrBlmLmw/rtJkyYRUuKtCabH165dW114XRBsFTG6BUEQBEEQBKuBLcPmz5+vIsQ5U+dUtd0hYSE4cPdArF6HRi6F0/r06YOMGTNGuV2RIkVQrlw5lQZORu8bjaMPjqrrbwLfIDTMvP7Sz58/x7p161QauJbCnjp1amXws76bAmtv3rxBwYIFzf4M6dOnV0roTDO3RngcMmXKpC5Sjy7YMmJ0C4IgCIIgCFYlpEYopka0aPfSc0vxNkjXOsscXr58qdp1lS9fPsZtmzVrhr179+LKsysYf3C8/v7br2+rtlzmMHnyZJXGTrE2Y8NZ689NzFEuFwTBuhCjWxAEQRAEQbBao7tK9irq7+Jzi1Hqz1JmG94PHz7Uq4nHBA1zRqE7re6k1NK1VHJzxdNYDz5jxgz07NlT1WAbcuzYMRWp3rJli9qX6KLutgb7cj948EBdpEe3YMuIerkgCIIgCIJgNVC5nKJhmtGdKUUm/WPXXl5TkWfWWMcE24CVLVsW2bJlM8vo/mTUJ1j9ZDVcHV1x7Mtj8AvyUwb37Wu3kTt3bpMibBouLi4qkm1qG/Ydv379uro0atQIyQka2vv371fXKTBnby/xQME2EaNbEARBEARBsCqGDh2qTwuvnbs2XBxcEBgSiAzuGcyKPJPSpUsrATNz8HPwwy7nXUAgMKLGCBTNUFRfF16rVi107doVw4cPN2lUe3t7K5EwGtem4P002mlws/1XcoJ13GnSpNFfFwRbRdxJgiAIgiAIglXB9l5UvCZM9R5WfZi6XjxjcbNUxLWUb3PaVDFdvegfRfE68DUc4IDu5bvrH7ty5YoSdhs7dqyq2TbFmDFjUKpUKaW2HhVsHXbw4EG9AZpcoDOifv366iLq5YItI0a3IAiCIAiCYFVQ6Xvnzp36258W+1T93XdnH175vzLrNQYNGmSWaNm0o9PgHeitrocgBJeeXtI/duDAAZUSXa9ePaxevTrSc6lMPm3aNLRp0yZaozJXrlwq/Zz9qgVBsD3E6BYEQRAEQRCsiuXLlytDVqNA2gIqrTw4NBibrm4yW0jNWNTMmDuv72DsgbHhd7wAnF8762/+888/KordsWNHlaqu1Zlr/PTTT0qxvH///tG+z7Bhw7Bjxw7VQkwQBNtDjG5BEARBEATBqsiZM6dq+eXr66u/r3mh5urv2strzXoN9vnOkiVLlI/TgG+3pp3qxV02c1lsa7UN+BO4cPpChHpt1nSzHtvZ2Rlr1qzRP3bjxg38/vvvGDhwILy8vKLdFz73ww8/RHKDKffMWOAluvR7QbB2xOgWBEEQBEEQrLJt2J07d/T3tSjcQv3ddn0b/N75mWV0R9cubPiu4Th47yBSOqfEX63+Qv0i9ZE/V/4I4mtr167FL7/8ohTVmWJu+JiPj48ypAcMGBDnz2nrsKb++fPn6mJOfb0gWCuiXi4IgiAIgiBYrdFdtKhOSbxkxpLInTo3br2+he03tuuNcFPQwIvO6N5ybQvGHxqvrns4e+jbkjGlnQY2efv2rVIr11S3ly1bhhQpUuhfg2nnmzdvjrfPbIuwHp4ictp1QbBVZHQLgiAIgiAIVgXTwgsVKqR6bWvQ+I1NijnF2Dp06BDp/md+z/DF2i/0tx/7Pla9v8no0aNVujgZMmQIKleurN9OM7iZ8t6jRw+cPXv2vT5jcoCGNvuk8yJGt2DLiNEtCIIgCIIgWBWOjo64dOkSPv744wj3a9HttZfW4nVA1ErgNNAzZcoUSbgsNCwUHdd3xAv/F3Cyd1L35U+TX9/7mxHy69evq3pyiqgVLlw4wvO7deuG7NmzY/r06areWxAEgYjRLQiCIAiCIFglhpFuUjJTSTjYOcAvyE/11maPbVOcOHECLVu2xIsXLyLcP/nIZJVa7uLgggOdDuDol0dx+qvT+t7ffn5+KFCgABYuXKgi2dWqVYvw/NKlS6u2X3xt48eEyNCJwbZqvEhNt2DLiNEtCIIgCIIgWB09e/ZEzZo1I9x38dlFhITpVLAf+jzUp4Ubwyg5+2qzJlvj4N2DGLRzkLo+qcEkVMhWARWyVtAb3FoKOaPbU6dORWhoqL4eWaN169ZKyXzChAnx+lltFSqW7969W11EvVywZcToFgRBEARBEKyOtGnT4vbt2xHuYxp4Ts+c+tterqZbdVFEzdPTE+7uOoOaqei1F9RWBnsKpxRoX6J9lO9brlw53Lx5ExkyZED+/Pkj7dOmTZuQK1eu9/x0yQcK02nidIJgq4jRLQiCIAiCIFhlr24az+/evdPfx6j0xZ4XUTpTaXV78dnFJp/78OHDCMrlI/aMQFBokLruG+SLC8/Ce3GbMrpdXFxw+vRpvXK5EPfafGYG8MLrgmCriNEtCIIgCIIgWKXRzTrge/fuRbifhveQakPU9ZknZ+JdSLhRrkFjnQro6rrPI8w9NVf/mKFwWlRGt5eXF/z9/ePx0wiCYMuIS0kQBEEQBEGwSqNb69WdN2/eCI81K9RM9dZmu691l9ehddHWER7//PPPERSki2wP/nuwEl4rl7kcpn40FSUylohQx21MpUqVVKRcotyCIJiLRLoFQRAEQRAEqyN37ty4evWqSZVwZwdndC3TVV3/4/gfkR5v3LgxmjdvjkP3DmHR2UWwgx3+aPQHKmWrFK3BTWhsi8EdP4iQmpBcEKNbEARBEARBsDpYA0whM2dnZ5OPdyvbTbUP23dnH5afWx6hfRhbft24eQM9NvdQt78o+QXKZy2faPsu6JCWYUJyQYxuQRAEQRAEwSoZO3Ysfv75Z5OPZUuVDY0KNFLX265pi1J/llKGt4+PDzp07ICO6zrizJMz6vF/7v4TZU9vIeGwt7dH5cqV1YXXBcFWkdEtCIIgCIIgWCVnz57F1q1bo3y8ft76+uvXXl7DsQfHcO/+PaAZcMDngP6xm69uRtnTW0g4aGizvRovYnQLtowY3YIgCIIgCILViqlRSC0qOpbqiHTu6fS3u23shnbb2wEluQi2R+YUmc1SLBcEQUg2RvfLly/Rrl07pEqVCqlTp0aXLl3g6+sb7fa9e/dGwYIF4ebmhhw5cqBPnz548+ZNou63IAiCIAiCkDBGN1uGUZDLFBRFu9PvDmY0noGM7hlVtPv0m9NAGDCz4Uxc73MdR788itNfnY5RQE1ImJruFy9eqAuvC4KtYlVGNw3uCxcuYOfOndi0aRP279+Pbt26Rbk92znw8ssvv+D8+fOYP38+tm3bpox1QRAEQRAEwfqN7uDgYNV3OypoTFNUbV6zeeF32gHFsxZXj1XIWkEM7iSCzpIdO3aoS1SOE0GwBaymT/elS5eUwXz8+HGUK1dO3Td16lR89NFHyqjOkiVLpOcUK1YMq1ev1t9mD8cff/xR9WbkBE3VS0EQBEEQBME6KV26NH777TeV0RgTNXPVVGnkjHZLOrnl4O4uGQaC7WM1Vufhw4dVSrlmcJO6desq0YWjR4+qXovmwNRypqeLwS0IgiAIgmDdMOjSr18/s7ZlVJtp5MfvHEe5HOUkum0BcD3etGlT/e3Q0NAk3R9BQHI3uh8/fowMGTJE+qGmSZNGPWYOz58/xw8//BBtSnpgYKC6aHh7e+snAUueCLhvrIWx5H0UBENkzArWhoxZwdpILmN28+bNSJ8+PSpUqBDjtq4OrhjZdSQyZ86MxYsXJ8r+CeaTXMasYDuYO1aT3OgeMmQIxo8fH2Nq+ftC47lRo0YoUqQIRo4cGeV248aNw6hRoyLd/+zZMwQEBMCSv3BG8TlRScsFwRqQMStYGzJmBWsjuYzZoUOHqjRztp0yBwqvFShQAE+fPk3wfRNiR3IZs4Lt4OPjYx1G94ABA9CxY8dot8mTJw8yZcoUaXJkXTYVyvlYTAejQYMGSJkyJdauXQsnJ6cot/3222/Rv3//CMZ69uzZlQeVaemWPEnZ2dmp/ZRJSrAGZMwK1oaMWcHaSC5jlutErhGNMyKjgoEU6vyYu72QcFA87dChQ+p6lSpV1HhNDmNWsB1cXV2tw+jmj4qXmKhcuTJev36NEydOoGzZsuq+3bt3qxNKxYoVo3wejeb69evDxcUFGzZsiPHAcDtejOEP39J//JykrGE/BUFDxqxgbciYFayN5DBmc+fOje3bt5v1Gf38/NTakLXgtnxMrAWu49lpyHCsJocxK9gO5o5TqxnNhQsXVtHqrl274tixYzh48CB69eqFNm3a6JXLHzx4gEKFCqnHCSfVevXqqQl2zpw56jbrv3mRtgSCIAiCIAi20Tbszp07ZvV51lqLmep6IySNwVK+fHl1ESNbsGWSPNIdG5YsWaIM7Tp16qgf5ieffIIpU6boHw8KCsKVK1fw9u1bdfvkyZNK2Zzky5cvwmvdunXL7NofQRAEQRAEwTJhi1imJnP95+HhEWMq+pMnTyy6ZDA5wfW84RpdBNQEW8UuzBy3YDKG0XFPT099qzFLhZOUVs8knkLBGpAxK1gbMmYFa0PGrGBtyJgVbNVWtJr0ckEQBEEQBEEwBaPcvr6+MR6cBQsWoEOHDnIQLQTG/misaIrlgmCriNEtCIIgCIIgWC001jJmzIgZM2bEuO26detw//79RNkvIWaosbRlyxZ1Eb0lwZYRo1sQBEEQBEGwWqh2TZ2e3377DUOGDFGCuqaiprzvyJEj0Xa9ERKfqDoHCYItIUa3IAiCIAiCYNXMnTtXdblhtxoa1WfPno20zd27d1UHm0qVKiXJPgqRcXR0RIsWLdSF1wXBVhGjWxAEQRAEQbBq2HJq9uzZqiXY/v37UaJEiUjbMMpNJNItCEJiI0a3IAiCIAiCYBMwWlq9enVcuHABu3btivBY1apVVftZ1n8LgiAkJpLHIQiCIAiCINgUEydOVLXd58+f19+XLVs2fPbZZ0m6X0JEKJ529OhRfQYC6/MFwRaRSLcgCIIgCIJgU9SvX19FuzWl8sDAQPTv3x9Xr15N6l0TjMTt7ty5oy7SMkywZcToFgRBEARBEGyKunXrqqjp9u3b1e0zZ84odfPXr18n9a4JBtjb26N06dLqwuuCYKvI6BYEQRAEQRBsirRp06JChQp6o5sias7OzihZsmRS75pgAA3tQoUKqYsY3YItIzXdgiAIgiAIgs3Rvn17fXo5je4yZcpIP2hBEJIEMboFQRAEQRAEm6Nnz5766xTr+vjjj5N0f4TIsI777du36rq7u7scIsFmEaNbEARBEARBsEkY6X7y5Ak6deqEGjVqJPXuCCbUyzds2KCut2rVSlLMBZtFjG5BEARBEATBJvn6669x7949lV4uWCYODg5JvQuCkOCIkJogCIIgCIJgkzRo0ECllm/dujWpd0UwgaOjI1q3bq0uvC4ItooY3YIgCIIgCILN9usmH330UVLviiAIyRhxKQmCIAiCIAg2SbZs2VCvXj189tlnSb0rgiAkY8ToFgRBEARBEGwWrVe3YJlCav/++6+6Xq5cOdjZ2SX1LglCgiDp5YIgCIIgCIIgJEnLsJs3b6oLrwuCrSKRbkEQBEEQBEEQEh17e3uUKFFCf10QbBUxugVBEARBEARBSHRoaBctWlR/OzQ0VL4FwSYRl5IgCIIgCIIgCIIgJBAS6RYEQRAEQRAEIdFhHXdgYKC67uLiIt+AYLNIpFsQBEEQBEEQhCRRL1+7dq268Log2CoS6Y4BTUnR29sblgxrYHx8fODq6ipCFIJVIGNWsDZkzArWhoxZwdIJDg7G27dv9Wtt1njLelawJjQbMSb1fbsw0eePlvv37yN79uzx+d0IgiAIgiAIgiAINsK9e/eQLVu2KB8Xo9sML/HDhw+RMmVK2NnZwZK9LHQO8AtPlSpVUu+OIMSIjFnB2pAxK1gbMmYFa0PGrGBtMH7N7IwsWbJEm20s6eUxwIMXndfC0qDBLUa3YE3ImBWsDRmzgrUhY1awNmTMCtaEp6dnjNuIkJogCIIgCIIgCIIgJBBidAuCIAiCIAiCIAhCAiFGt43A3obff/+99DgUrAYZs4K1IWNWsDZkzArWhoxZwVYRITVBEARBEARBEARBSCAk0i0IgiAIgiAIgiAICYQY3YIgCIIgCIIgCIKQQIjRLQiCIAiCIAiCIAgJhBjdNsDvv/+OXLlywdXVFRUrVsSxY8eSepcEQTFy5EjY2dlFuBQqVEh/dAICAtCzZ0+kTZsWKVKkwCeffIInT57I0RMSjf3796NJkybIkiWLGp/r1q2L8HhYWBhGjBiBzJkzw83NDXXr1sW1a9cibPPy5Uu0a9dO9ZVNnTo1unTpAl9fX/kWhSQZsx07dow07zZo0EDGrJBkjBs3DuXLl0fKlCmRIUMGNGvWDFeuXImwjTnrgbt376JRo0Zwd3dXrzNo0CAEBwcn8qcRhLghRreVs2LFCvTv318pl588eRIlS5ZE/fr18fTp06TeNUFQFC1aFI8ePdJfDhw4oD8yX3/9NTZu3IiVK1di3759ePjwIVq0aCFHTkg0/Pz81LxJ56Upfv75Z0yZMgV//vknjh49Cg8PDzXHcoGoQYP7woUL2LlzJzZt2qSMom7dusm3KCTJmCU0sg3n3WXLlkV4XMaskJjw/E6D+siRI2qeDAoKQr169dRYNnc9EBISogzud+/e4dChQ1iwYAHmz5+vnKKCYBWECVZNhQoVwnr27Km/HRISEpYlS5awcePGJel+CQL5/vvvw0qWLGnyYLx+/TrMyckpbOXKlfr7Ll26FMZp6fDhw3IAhUSHY2/t2rX626GhoWGZMmUKmzBhQoRx6+LiErZs2TJ1++LFi+p5x48f12+zdevWMDs7u7AHDx4k8icQkvuYJR06dAhr2rRplM+RMSskNU+fPlVjd9++fWavB7Zs2RJmb28f9vjxY/0206dPD0uVKlVYYGBgEnwKQYgdEum2YujtO3HihEp31LC3t1e3Dx8+nKT7JggaTMVlGmSePHlUdIXpYYRjl95uw/HL1PMcOXLI+BUsglu3buHx48cRxqinp6cq49HmWP5lSnm5cuX023B7zsWMjAtCUrB3716VfluwYEF0794dL1680D8mY1ZIat68eaP+pkmTxuz1AP8WL14cGTNm1G/DrCNvb2+VaSQIlo4Y3VbM8+fPVbqN4QREeJsLRUFIamicMP1r27ZtmD59ujJiqlevDh8fHzVGnZ2dlcFiiIxfwVLQ5tHo5lj+pXFjiKOjo1pMyjwsJAVMLV+4cCF27dqF8ePHq1Tdhg0bqvWCjFkhqQkNDUW/fv1QtWpVFCtWTN1nznqAf03NxdpjgmDpOCb1DgiCYLtwoadRokQJZYTnzJkTf/31lxKlEgRBEOKXNm3a6K8zMsi5N2/evCr6XadOHTncQpLC2u7z589H0HcRhOSARLqtmHTp0sHBwSGSuiNvZ8qUKcn2SxCigl7sAgUK4Pr162qMskTi9evXEbaR8StYCto8Gt0cy7/GwpVU06WiuczDgiXA0h6uFzjvEhmzQlLRq1cvJTa5Z88eZMuWTX+/OesB/jU1F2uPCYKlI0a3FcNUnLJly6oUMsO0Hd6uXLlyku6bIJiCbZRu3Lih2i9x7Do5OUUYv2whwppvGb+CJZA7d261mDMco6wfZK22Nkb5lwtF1iRq7N69W83FzOwQhKTm/v37qqab8y6RMSskNtT8o8G9du1aNT9ybjXEnPUA/547dy6Ck5NK6GzVWKRIkUT8NIIQNyS93Mphu7AOHTooEZ8KFSpg0qRJqgVDp06dknrXBAEDBw5U/WSZUs72H2xtx+yMtm3bKkEq9jPmGGb9K0+cvXv3VifWSpUqydETEs0RpEUACXUHTp8+rcYkRXxYezhmzBjkz59fLRS/++47JQzIPrOkcOHCqoa2a9euqq0YxYC4uGSKL7cThMQcs7yMGjVK9Timw4hOzm+++Qb58uVTolMyZoWkSilfunQp1q9fr3p1azXYXAew1Myc9QBbjNG4bt++vWrlyNcYPny4em0XFxf5YgXLJ5Zq54IFMnXq1LAcOXKEOTs7qxZiR44cSepdEgTFp59+GpY5c2Y1NrNmzapuX79+XX90/P39w3r06BHm5eUV5u7uHta8efOwR48eydETEo09e/aotjTGF7Zd0tqGfffdd2EZM2ZUrcLq1KkTduXKlQiv8eLFi7C2bduGpUiRQrWv6dSpU5iPj498i0Kij9m3b9+G1atXLyx9+vSqBVPOnDnDunbtGqHNkoxZIbExNV55mTdvXqzWA7dv3w5r2LBhmJubW1i6dOnCBgwYEBYUFCRfqGAV2PG/pDb8BUEQBEEQBEEQBMEWkZpuQRAEQRAEQRAEQUggxOgWBEEQBEEQBEEQhARCjG5BEARBEARBEARBSCDE6BYEQRAEQRAEQRCEBEKMbkEQBEEQBEEQBEFIIMToFgRBEARBEARBEIQEQoxuQRAEQRAEQRAEQUggxOgWBEEQBEEQBEEQhARCjG5BEARBELB3717Y2dnh9evXcjQEQRAEIR6xCwsLC5MjKgiCIAjJi1q1aqFUqVKYNGmSuv3u3Tu8fPkSGTNmVMa3IAiCIAjxg2M8vY4gCIIgCFaMs7MzMmXKlNS7IQiCIAg2h6SXC4IgCEIyo2PHjti3bx8mT56sotq8zJ8/P0J6OW+nTp0amzZtQsGCBeHu7o6WLVvi7du3WLBgAXLlygUvLy/06dMHISEh+tcODAzEwIEDkTVrVnh4eKBixYoqdV0QBEEQkisS6RYEQRCEZAaN7atXr6JYsWIYPXq0uu/ChQuRtqOBPWXKFCxfvhw+Pj5o0aIFmjdvrozxLVu24ObNm/jkk09QtWpVfPrpp+o5vXr1wsWLF9VzsmTJgrVr16JBgwY4d+4c8ufPn+ifVRAEQRCSGjG6BUEQBCGZ4enpqdLJGb3WUsovX74cabugoCBMnz4defPmVbcZ6V60aBGePHmCFClSoEiRIqhduzb27NmjjO67d+9i3rx56i8NbsKo97Zt29T9Y8eOTeRPKgiCIAhJjxjdgiAIgiCYhEa5ZnATiqwxrZwGt+F9T58+VdcZzWaqeYECBSK8DlPO06ZNK0dZEARBSJaI0S0IgiAIgkmcnJwi3GbNt6n7QkND1XVfX184ODjgxIkT6q8hhoa6IAiCICQnxOgWBEEQhGQI08sNBdDig9KlS6vXZOS7evXq8fragiAIgmCtiHq5IAiCICRDmCZ+9OhR3L59G8+fP9dHq98HppW3a9cOX3zxBdasWYNbt27h2LFjGDduHDZv3hwv+y0IgiAI1oYY3YIgCIKQDKHAGVPAKYaWPn16JX4WH1AwjUb3gAEDVKuxZs2a4fjx48iRI0e8vL4gCIIgWBt2YWFhYUm9E4IgCIIgCIIgCIJgi0ikWxAEQRAEQRAEQRASCDG6BUEQBEEQBEEQBCGBEKNbEARBEARBEARBEBIIMboFQRAEQRAEQRAEIYEQo1sQBEEQBEEQBEEQEggxugVBEARBEARBEAQhgRCjWxAEQRAEQRAEQRASCDG6BUEQBEEQBEEQBCGBEKNbEARBEARBEARBEBIIMboFQRAEQRAEQRAEIYEQo1sQBEEQBEEQBEEQEggxugVBEARBEARBEAQBCcP/AUZOAHGTMq9AAAAAAElFTkSuQmCC",
+            "text/plain": [
+              "<Figure size 1000x500 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "fig, axes = plt.subplots(2, 1, figsize=(10, 5), sharex=True)\n",
+        "\n",
+        "for i, ax in enumerate(axes):\n",
+        "    ax.fill_between(\n",
+        "        future_times,\n",
+        "        forecast_lo[:, i],\n",
+        "        forecast_hi[:, i],\n",
+        "        alpha=0.3,\n",
+        "        label=\"90% rollout interval\",\n",
+        "    )\n",
+        "    ax.plot(obs_times, true_states[:, i], \"k--\", lw=1, label=\"true state\")\n",
+        "    ax.plot(obs_times, forecast_smoothed_mean[:, i], \"g.-\", markersize=3, label=\"smoothed mean\")\n",
+        "    ax.plot(future_times, forecast_mean[:, i], \"C1.-\", markersize=3, label=\"rollout mean\")\n",
+        "    ax.axvline(float(obs_times[-1]), color=\"gray\", linestyle=\":\", alpha=0.7)\n",
+        "    if i == 0:\n",
+        "        ax.scatter(\n",
+        "            obs_times,\n",
+        "            obs_values[:, 0],\n",
+        "            color=\"C3\",\n",
+        "            marker=\"x\",\n",
+        "            s=20,\n",
+        "            label=\"observations\",\n",
+        "            zorder=3,\n",
+        "        )\n",
+        "    ax.set_ylabel(f\"$x_{i}$\")\n",
+        "    ax.legend(loc=\"upper right\", fontsize=8)\n",
+        "    ax.grid(True, alpha=0.3)\n",
+        "\n",
+        "axes[-1].set_xlabel(\"time\")\n",
+        "fig.suptitle(\"Smoother + DiscreteTimeSimulator: future-only rollout\")\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Takeaways\n",
+        "\n",
+        "- `Filter` records causal distributions $p(x_t \\mid y_{1:t})$; `Smoother` records retrospective distributions $p(x_t \\mid y_{1:T})$.\n",
+        "- `Smoother` computes the marginal likelihood and smoothing distributions $p(x_t \\mid y_{1:T})$.\n",
+        "- For linear-Gaussian discrete-time systems, `KFSmootherConfig` gives exact RTS smoothing.\n",
+        "- Smoothed summaries are recorded through `record_smoothed_*` options.\n",
+        "- Future prediction under `Smoother` starts from the final smoothed state distribution.\n",
+        "- In-window prediction is intentionally rejected until its semantics are implemented safely."
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python",
+      "version": "3.12.0"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 4
+}
diff --git a/docs/tutorials/gentle_intro/10_continuous_smoothing.ipynb b/docs/tutorials/gentle_intro/10_continuous_smoothing.ipynb
new file mode 100644
index 00000000..3c83fd78
--- /dev/null
+++ b/docs/tutorials/gentle_intro/10_continuous_smoothing.ipynb
@@ -0,0 +1,801 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "continuous-smoothing-00",
+      "metadata": {},
+      "source": [
+        "# Part 10: Continuous-Time Smoothing\n",
+        "\n",
+        "The previous notebook introduced smoothing for discrete-time state-space models. Here we use the same handler interface for a continuous-time system observed at discrete times.\n",
+        "\n",
+        "The state evolves continuously between observations, but the data still arrive on an observation grid. The smoother combines the continuous-time dynamics with the full observation window to estimate\n",
+        "\n",
+        "$$\n",
+        "p(x_t \\mid y_{1:T})\n",
+        "$$\n",
+        "\n",
+        "at the observation times.\n",
+        "\n",
+        "The example model is a two-dimensional linear SDE. The latent state is $x_t = (x_{0,t}, x_{1,t})$, and only the second coordinate is observed:\n",
+        "\n",
+        "$$\n",
+        "\\begin{aligned}\n",
+        "\\rho &\\sim \\operatorname{Uniform}(0, 4), \\\\\n",
+        "x_0 &\\sim \\mathcal{N}\\!\\left(\n",
+        "\\begin{bmatrix}0 \\\\ 0\\end{bmatrix},\n",
+        "I_2\n",
+        "\\right), \\\\\n",
+        "dx_t &=\n",
+        "\\begin{bmatrix}\n",
+        "-0.8 & 0 \\\\\n",
+        "\\rho & -1.2\n",
+        "\\end{bmatrix}\n",
+        "x_t\\,dt + 0.15 I_2\\,dW_t, \\\\\n",
+        "y_t \\mid x_t &\\sim \\mathcal{N}\\!\\left(\\begin{bmatrix}0 & 1\\end{bmatrix}x_t,\\ 0.1^2\\right).\n",
+        "\\end{aligned}\n",
+        "$$\n",
+        "\n",
+        "The first coordinate is hidden, but it drives the observed coordinate through the off-diagonal term $\\rho x_{0,t}$ in the drift."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "continuous-smoothing-01",
+      "metadata": {},
+      "source": [
+        "## The example\n",
+        "\n",
+        "We will use a two-dimensional linear SDE. The first coordinate drives the second, but only the second coordinate is observed. This makes smoothing useful: later observations of the second coordinate help reconstruct the hidden first coordinate retrospectively.\n",
+        "\n",
+        "We will also include an observation gap to emphasize that continuous-time dynamics can carry information across irregularly spaced observations."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "continuous-smoothing-02",
+      "metadata": {},
+      "source": [
+        "### Imports"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "continuous-smoothing-03",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import arviz as az\n",
+        "import jax.numpy as jnp\n",
+        "import jax.random as jr\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpyro\n",
+        "import numpyro.distributions as dist\n",
+        "from numpyro.infer import MCMC, NUTS, Predictive\n",
+        "\n",
+        "import dynestyx as dsx\n",
+        "from dynestyx import Filter, Simulator, Smoother\n",
+        "from dynestyx.inference.filter_configs import ContinuousTimeKFConfig\n",
+        "from dynestyx.inference.smoother_configs import ContinuousTimeKFSmootherConfig"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "continuous-smoothing-04",
+      "metadata": {},
+      "source": [
+        "## Defining the Model\n",
+        "\n",
+        "The code below is a direct translation of the SDE above into an `LTI_continuous` model. The unknown parameter `rho` controls how strongly the hidden first coordinate drives the observed second coordinate. The model is linear-Gaussian, so `ContinuousTimeKFSmootherConfig` gives an exact continuous-discrete Kalman smoother through the `cd_dynamax` backend."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "continuous-smoothing-05",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "def continuous_lti_model(obs_times=None, obs_values=None, predict_times=None):\n",
+        "    rho = numpyro.sample(\"rho\", dist.Uniform(0.0, 4.0))\n",
+        "\n",
+        "    dynamics = dsx.LTI_continuous(\n",
+        "        A=jnp.array([[-0.8, 0.0], [rho, -1.2]]),\n",
+        "        L=0.15 * jnp.eye(2),\n",
+        "        H=jnp.array([[0.0, 1.0]]),\n",
+        "        R=jnp.array([[0.1**2]]),\n",
+        "        initial_mean=jnp.array([0.0, 0.0]),\n",
+        "        initial_cov=jnp.eye(2),\n",
+        "    )\n",
+        "\n",
+        "    return dsx.sample(\n",
+        "        \"f\",\n",
+        "        dynamics,\n",
+        "        obs_times=obs_times,\n",
+        "        obs_values=obs_values,\n",
+        "        predict_times=predict_times,\n",
+        "    )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "continuous-smoothing-06",
+      "metadata": {},
+      "source": [
+        "### Generate synthetic data\n",
+        "\n",
+        "We first simulate a dense reference trajectory. Then we remove observations in the middle of the window, producing an irregular observation grid. The smoother will only record summaries at the observation times we pass in, but the dynamics still encode what can happen across the gap."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "continuous-smoothing-07",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "((76,), (76, 1))"
+            ]
+          },
+          "execution_count": 3,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "rho_true = jnp.array(2.0)\n",
+        "full_times = jnp.linspace(0.0, 5.0, 101)\n",
+        "\n",
+        "data_predictive = Predictive(\n",
+        "    continuous_lti_model,\n",
+        "    params={\"rho\": rho_true},\n",
+        "    num_samples=1,\n",
+        "    exclude_deterministic=False,\n",
+        ")\n",
+        "\n",
+        "with Simulator(n_simulations=1):\n",
+        "    synthetic = data_predictive(jr.PRNGKey(0), predict_times=full_times)\n",
+        "\n",
+        "true_states_full = synthetic[\"f_states\"][0, 0]\n",
+        "observations_full = synthetic[\"f_observations\"][0, 0]\n",
+        "\n",
+        "observed_mask = (full_times <= 1.75) | (full_times >= 3.0)\n",
+        "obs_times = full_times[observed_mask]\n",
+        "obs_values = observations_full[observed_mask]\n",
+        "true_states_at_obs = true_states_full[observed_mask]\n",
+        "\n",
+        "obs_times.shape, obs_values.shape"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "continuous-smoothing-08",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 700x500 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "fig, axes = plt.subplots(2, 1, figsize=(7, 5), sharex=True, constrained_layout=True)\n",
+        "\n",
+        "axes[0].plot(full_times, true_states_full[:, 0], \"k--\", lw=1, label=\"true state\")\n",
+        "axes[0].set_ylabel(\"$x_0$\")\n",
+        "axes[0].legend(loc=\"upper right\", fontsize=8)\n",
+        "axes[0].grid(True, alpha=0.3)\n",
+        "\n",
+        "axes[1].plot(full_times, true_states_full[:, 1], \"k--\", lw=1, label=\"true state\")\n",
+        "axes[1].scatter(\n",
+        "    obs_times,\n",
+        "    obs_values[:, 0],\n",
+        "    color=\"C3\",\n",
+        "    marker=\"x\",\n",
+        "    s=20,\n",
+        "    label=\"observations\",\n",
+        "    zorder=3,\n",
+        ")\n",
+        "axes[1].set_ylabel(\"$x_1$\")\n",
+        "axes[1].set_xlabel(\"time\")\n",
+        "axes[1].legend(loc=\"upper right\", fontsize=8)\n",
+        "axes[1].grid(True, alpha=0.3)\n",
+        "\n",
+        "for ax in axes:\n",
+        "    ax.axvspan(1.75, 3.0, color=\"gray\", alpha=0.14)\n",
+        "\n",
+        "fig.suptitle(\"Synthetic continuous-time trajectory with an observation gap\")\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "continuous-smoothing-09",
+      "metadata": {},
+      "source": [
+        "## Smoothing at fixed parameters\n",
+        "\n",
+        "Now we condition on the irregular observations and record smoothed state summaries. The smoother adds the marginal log-likelihood factor and exposes deterministic sites for the smoothed means and covariance diagonals."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "continuous-smoothing-10",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Running KF smoother type = cd_smoother_1\n",
+            "Running KF smoother type 1\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "((76, 2), (76, 2))"
+            ]
+          },
+          "execution_count": 5,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "smooth_predictive = Predictive(\n",
+        "    continuous_lti_model,\n",
+        "    params={\"rho\": rho_true},\n",
+        "    num_samples=1,\n",
+        "    exclude_deterministic=False,\n",
+        ")\n",
+        "\n",
+        "with Smoother(\n",
+        "    smoother_config=ContinuousTimeKFSmootherConfig(\n",
+        "        record_smoothed_states_mean=True,\n",
+        "        record_smoothed_states_cov_diag=True,\n",
+        "    )\n",
+        "):\n",
+        "    smoothed = smooth_predictive(\n",
+        "        jr.PRNGKey(1),\n",
+        "        obs_times=obs_times,\n",
+        "        obs_values=obs_values,\n",
+        "    )\n",
+        "\n",
+        "smoothed_mean = smoothed[\"f_smoothed_states_mean\"][0]\n",
+        "smoothed_var = smoothed[\"f_smoothed_states_cov_diag\"][0]\n",
+        "smoothed_sd = jnp.sqrt(jnp.maximum(smoothed_var, 1e-9))\n",
+        "\n",
+        "smoothed_mean.shape, smoothed_var.shape"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "continuous-smoothing-11",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 700x500 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "fig, axes = plt.subplots(2, 1, figsize=(7, 5), sharex=True, constrained_layout=True)\n",
+        "\n",
+        "for i, ax in enumerate(axes):\n",
+        "    ax.fill_between(\n",
+        "        obs_times,\n",
+        "        smoothed_mean[:, i] - 2.0 * smoothed_sd[:, i],\n",
+        "        smoothed_mean[:, i] + 2.0 * smoothed_sd[:, i],\n",
+        "        alpha=0.3,\n",
+        "        label=\"approx. 95% smoothing interval\",\n",
+        "    )\n",
+        "    ax.plot(obs_times, smoothed_mean[:, i], \"C0.-\", markersize=3, label=f\"$x_{i}$ smoothed mean\")\n",
+        "    ax.plot(full_times, true_states_full[:, i], \"k--\", lw=1, label=\"true state\")\n",
+        "    if i == 1:\n",
+        "        ax.scatter(\n",
+        "            obs_times,\n",
+        "            obs_values[:, 0],\n",
+        "            color=\"C3\",\n",
+        "            marker=\"x\",\n",
+        "            s=20,\n",
+        "            label=\"observations\",\n",
+        "            zorder=3,\n",
+        "        )\n",
+        "    ax.axvspan(1.75, 3.0, color=\"gray\", alpha=0.14)\n",
+        "    ax.set_ylabel(f\"$x_{i}$\")\n",
+        "    ax.legend(loc=\"upper right\", fontsize=8)\n",
+        "    ax.grid(True, alpha=0.3)\n",
+        "\n",
+        "axes[-1].set_xlabel(\"time\")\n",
+        "fig.suptitle(\"Continuous-time Kalman smoothing at the true parameter\")\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "continuous-smoothing-12",
+      "metadata": {},
+      "source": [
+        "The first state is never observed directly. Its reconstruction comes entirely from the continuous-time coupling and the full observation window. The gap is also visible: uncertainty is typically larger when observations are sparse."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "continuous-smoothing-13",
+      "metadata": {},
+      "source": [
+        "## Filtering versus smoothing distributions\n",
+        "\n",
+        "As in the discrete-time tutorial, filtering and smoothing answer different conditioning questions. The continuous-discrete filter computes\n",
+        "\n",
+        "$$\n",
+        "p(x_{t_k} \\mid y_{t_0}, \\ldots, y_{t_k}),\n",
+        "$$\n",
+        "\n",
+        "while the smoother computes\n",
+        "\n",
+        "$$\n",
+        "p(x_{t_k} \\mid y_{t_0}, \\ldots, y_{t_T}).\n",
+        "$$\n",
+        "\n",
+        "Both summaries below use the same fixed `rho`, the same irregular observation times, and the same continuous-time dynamics. The only difference is whether future observations are available when estimating each state."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "continuous-smoothing-14",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "((76, 2), Array(0., dtype=float32), Array(0., dtype=float32))"
+            ]
+          },
+          "execution_count": 7,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "filter_predictive = Predictive(\n",
+        "    continuous_lti_model,\n",
+        "    params={\"rho\": rho_true},\n",
+        "    num_samples=1,\n",
+        "    exclude_deterministic=False,\n",
+        ")\n",
+        "\n",
+        "with Filter(\n",
+        "    filter_config=ContinuousTimeKFConfig(\n",
+        "        record_filtered_states_mean=True,\n",
+        "        record_filtered_states_cov_diag=True,\n",
+        "    )\n",
+        "):\n",
+        "    filtered = filter_predictive(\n",
+        "        jr.PRNGKey(11),\n",
+        "        obs_times=obs_times,\n",
+        "        obs_values=obs_values,\n",
+        "    )\n",
+        "\n",
+        "filtered_mean = filtered[\"f_filtered_states_mean\"][0]\n",
+        "filtered_var = filtered[\"f_filtered_states_cov_diag\"][0]\n",
+        "filtered_sd = jnp.sqrt(jnp.maximum(filtered_var, 1e-9))\n",
+        "\n",
+        "terminal_mean_gap = jnp.max(jnp.abs(filtered_mean[-1] - smoothed_mean[-1]))\n",
+        "terminal_sd_gap = jnp.max(jnp.abs(filtered_sd[-1] - smoothed_sd[-1]))\n",
+        "\n",
+        "filtered_mean.shape, terminal_mean_gap, terminal_sd_gap"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "id": "continuous-smoothing-15",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1000x500 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "fig, axes = plt.subplots(2, 1, figsize=(10, 5), sharex=True)\n",
+        "\n",
+        "for i, ax in enumerate(axes):\n",
+        "    ax.fill_between(\n",
+        "        obs_times,\n",
+        "        filtered_mean[:, i] - 2.0 * filtered_sd[:, i],\n",
+        "        filtered_mean[:, i] + 2.0 * filtered_sd[:, i],\n",
+        "        color=\"C1\",\n",
+        "        alpha=0.2,\n",
+        "        label=\"filter approx. 95% interval\",\n",
+        "    )\n",
+        "    ax.fill_between(\n",
+        "        obs_times,\n",
+        "        smoothed_mean[:, i] - 2.0 * smoothed_sd[:, i],\n",
+        "        smoothed_mean[:, i] + 2.0 * smoothed_sd[:, i],\n",
+        "        color=\"C0\",\n",
+        "        alpha=0.2,\n",
+        "        label=\"smoother approx. 95% interval\",\n",
+        "    )\n",
+        "    ax.plot(obs_times, filtered_mean[:, i], \"C1.-\", markersize=3, label=\"filtered mean\")\n",
+        "    ax.plot(obs_times, smoothed_mean[:, i], \"C0.-\", markersize=3, label=\"smoothed mean\")\n",
+        "    ax.plot(full_times, true_states_full[:, i], \"k--\", lw=1, label=\"true state\")\n",
+        "    if i == 1:\n",
+        "        ax.scatter(\n",
+        "            obs_times,\n",
+        "            obs_values[:, 0],\n",
+        "            color=\"C3\",\n",
+        "            marker=\"x\",\n",
+        "            s=20,\n",
+        "            label=\"observations\",\n",
+        "            zorder=3,\n",
+        "        )\n",
+        "    ax.axvspan(1.75, 3.0, color=\"gray\", alpha=0.14)\n",
+        "    ax.axvline(float(obs_times[-1]), color=\"gray\", linestyle=\":\", alpha=0.7)\n",
+        "    ax.set_ylabel(f\"$x_{i}$\")\n",
+        "    ax.legend(loc=\"upper right\", fontsize=8)\n",
+        "    ax.grid(True, alpha=0.3)\n",
+        "\n",
+        "axes[-1].set_xlabel(\"time\")\n",
+        "fig.suptitle(\"Continuous-time filtering and smoothing distributions\")\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "continuous-smoothing-16",
+      "metadata": {},
+      "source": [
+        "The two distributions agree at the final observation time, because there are no future observations left for the smoother to use. Earlier in the window, especially around the observation gap, the smoother can use data from both sides of a state estimate, so its mean and uncertainty can differ substantially from the causal filter."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "continuous-smoothing-17",
+      "metadata": {},
+      "source": [
+        "## Parameter inference with continuous-time smoothing\n",
+        "\n",
+        "As with filtering, the smoother marginal likelihood can be used inside NumPyro inference algorithms. Here NUTS samples only `rho`; the latent trajectory has been analytically marginalized by the smoother."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "id": "continuous-smoothing-18",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Running KF smoother type = cd_smoother_1\n",
+            "Running KF smoother type 1\n",
+            "Running KF smoother type = cd_smoother_1\n",
+            "Running KF smoother type 1\n",
+            "Running KF smoother type = cd_smoother_1\n",
+            "Running KF smoother type 1\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "  0%|          | 0/150 [00:00<?, ?it/s]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Running KF smoother type = cd_smoother_1\n",
+            "Running KF smoother type 1\n",
+            "Running KF smoother type = cd_smoother_1\n",
+            "Running KF smoother type 1\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "sample: 100%|██████████| 150/150 [00:04<00:00, 30.11it/s, 1 steps of size 1.55e+00. acc. prob=0.71]\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "(75,)"
+            ]
+          },
+          "execution_count": 9,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "def smoothed_parameter_model():\n",
+        "    with Smoother(\n",
+        "        smoother_config=ContinuousTimeKFSmootherConfig(\n",
+        "            record_smoothed_states_mean=True,\n",
+        "        )\n",
+        "    ):\n",
+        "        return continuous_lti_model(obs_times=obs_times, obs_values=obs_values)\n",
+        "\n",
+        "\n",
+        "nuts = NUTS(smoothed_parameter_model)\n",
+        "mcmc = MCMC(nuts, num_warmup=75, num_samples=75)\n",
+        "mcmc.run(jr.PRNGKey(2))\n",
+        "posterior = mcmc.get_samples()\n",
+        "\n",
+        "posterior[\"rho\"].shape"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "id": "continuous-smoothing-19",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/plain": [
+              "Array(2.3767934, dtype=float32)"
+            ]
+          },
+          "execution_count": 10,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "az.plot_posterior(posterior[\"rho\"], hdi_prob=0.95, ref_val=float(rho_true))\n",
+        "plt.title(\"Smoother-based posterior for rho\")\n",
+        "plt.show()\n",
+        "\n",
+        "rho_post_mean = jnp.mean(posterior[\"rho\"])\n",
+        "rho_post_mean"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "continuous-smoothing-20",
+      "metadata": {},
+      "source": [
+        "## Forecasting beyond the observation window\n",
+        "\n",
+        "Once the parameter is fit, we can forecast from the final smoothed state. This is the supported prediction semantics for `Smoother`: the prediction grid must start at or after `max(obs_times)`.\n",
+        "\n",
+        "Below we recompute the smoothing summary and the rollout using the same posterior-mean value of `rho`. This avoids the same boundary mistake as in the discrete-time case: comparing a true-parameter smoothed curve to a fitted-parameter rollout can create an artificial discontinuity at the forecast start.\n",
+        "\n",
+        "Internally, `Smoother` passes the final smoothed distribution downstream as the rollout initial condition, and the simulator handles the forward path."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "id": "continuous-smoothing-21",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Running KF smoother type = cd_smoother_1\n",
+            "Running KF smoother type 1\n"
+          ]
+        }
+      ],
+      "source": [
+        "future_times = jnp.linspace(obs_times[-1], obs_times[-1] + 1.5, 31)\n",
+        "\n",
+        "forecast_predictive = Predictive(\n",
+        "    continuous_lti_model,\n",
+        "    params={\"rho\": rho_post_mean},\n",
+        "    num_samples=1,\n",
+        "    exclude_deterministic=False,\n",
+        ")\n",
+        "\n",
+        "n_rollout = 40\n",
+        "\n",
+        "with Simulator(n_simulations=n_rollout):\n",
+        "    with Smoother(\n",
+        "        smoother_config=ContinuousTimeKFSmootherConfig(\n",
+        "            record_smoothed_states_mean=True,\n",
+        "            record_smoothed_states_cov_diag=True,\n",
+        "        )\n",
+        "    ):\n",
+        "        forecast = forecast_predictive(\n",
+        "            jr.PRNGKey(3),\n",
+        "            obs_times=obs_times,\n",
+        "            obs_values=obs_values,\n",
+        "            predict_times=future_times,\n",
+        "        )\n",
+        "\n",
+        "forecast_smoothed_mean = forecast[\"f_smoothed_states_mean\"][0]\n",
+        "forecast_smoothed_var = forecast[\"f_smoothed_states_cov_diag\"][0]\n",
+        "forecast_smoothed_sd = jnp.sqrt(jnp.maximum(forecast_smoothed_var, 1e-9))\n",
+        "\n",
+        "forecast_states = forecast[\"f_predicted_states\"][0]\n",
+        "forecast_mean = forecast_states.mean(axis=0)\n",
+        "forecast_lo = jnp.percentile(forecast_states, 5.0, axis=0)\n",
+        "forecast_hi = jnp.percentile(forecast_states, 95.0, axis=0)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "id": "continuous-smoothing-22",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1000x500 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "fig, axes = plt.subplots(2, 1, figsize=(10, 5), sharex=True)\n",
+        "\n",
+        "for i, ax in enumerate(axes):\n",
+        "    ax.fill_between(\n",
+        "        future_times,\n",
+        "        forecast_lo[:, i],\n",
+        "        forecast_hi[:, i],\n",
+        "        alpha=0.3,\n",
+        "        label=\"90% rollout interval\",\n",
+        "    )\n",
+        "    ax.plot(full_times, true_states_full[:, i], \"k--\", lw=1, label=\"true state\")\n",
+        "    ax.plot(obs_times, forecast_smoothed_mean[:, i], \"g.-\", markersize=3, label=\"smoothed mean\")\n",
+        "    ax.plot(future_times, forecast_mean[:, i], \"C1.-\", markersize=3, label=\"rollout mean\")\n",
+        "    ax.axvspan(1.75, 3.0, color=\"gray\", alpha=0.14)\n",
+        "    ax.axvline(float(obs_times[-1]), color=\"gray\", linestyle=\":\", alpha=0.7)\n",
+        "    if i == 1:\n",
+        "        ax.scatter(\n",
+        "            obs_times,\n",
+        "            obs_values[:, 0],\n",
+        "            color=\"C3\",\n",
+        "            marker=\"x\",\n",
+        "            s=20,\n",
+        "            label=\"observations\",\n",
+        "            zorder=3,\n",
+        "        )\n",
+        "    ax.set_ylabel(f\"$x_{i}$\")\n",
+        "    ax.legend(loc=\"upper right\", fontsize=8)\n",
+        "    ax.grid(True, alpha=0.3)\n",
+        "\n",
+        "axes[-1].set_xlabel(\"time\")\n",
+        "fig.suptitle(\"Smoother + Simulator: future-only continuous-time rollout\")\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "continuous-smoothing-23",
+      "metadata": {},
+      "source": [
+        "### Why not predict inside the smoothing window?\n",
+        "\n",
+        "Conceptually, continuous-time smoothing could support an augmented output grid inside the observation window. That is planned separately. For now, Dynestyx raises a clear error for any in-window `predict_times`, because the continuous-time backend missing-data behavior and the handler handoff semantics need to be correct before teaching that workflow."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "id": "continuous-smoothing-24",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Running KF smoother type = cd_smoother_1\n",
+            "Running KF smoother type 1\n",
+            "EquinoxRuntimeError\n",
+            "equinox.EquinoxRuntimeError: Smoother prediction only supports predict_times >= max(obs_times); in-window smoothing predictions are not implemented yet.\n"
+          ]
+        }
+      ],
+      "source": [
+        "bad_predict_times = jnp.linspace(obs_times[-5], obs_times[-1] + 0.5, 20)\n",
+        "\n",
+        "try:\n",
+        "    with Simulator(n_simulations=1):\n",
+        "        with Smoother(smoother_config=ContinuousTimeKFSmootherConfig()):\n",
+        "            forecast_predictive(\n",
+        "                jr.PRNGKey(4),\n",
+        "                obs_times=obs_times,\n",
+        "                obs_values=obs_values,\n",
+        "                predict_times=bad_predict_times,\n",
+        "            )\n",
+        "except Exception as exc:\n",
+        "    print(type(exc).__name__)\n",
+        "    message = str(exc)\n",
+        "    for line in message.split(\"\\n\"):\n",
+        "        if \"Smoother prediction\" in line:\n",
+        "            print(line)\n",
+        "            break\n",
+        "    else:\n",
+        "        print(message.split(\"\\n\")[0])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "continuous-smoothing-25",
+      "metadata": {},
+      "source": [
+        "## Takeaways\n",
+        "\n",
+        "- The same `Smoother` handler works for continuous-discrete linear-Gaussian systems.\n",
+        "- `Filter` records causal distributions $p(x_{t_k} \\mid y_{t_0:k})$; `Smoother` records retrospective distributions $p(x_{t_k} \\mid y_{t_0:T})$.\n",
+        "- `ContinuousTimeKFSmootherConfig` computes the marginal likelihood and smoothing distributions through `cd_dynamax`.\n",
+        "- Smoothed summaries can reconstruct unobserved state components by using the full observation window.\n",
+        "- Future prediction starts from the final smoothed distribution.\n",
+        "- In-window prediction is deliberately rejected until the continuous-time semantics are implemented completely."
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": ".venv",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.10"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
diff --git a/dynestyx/__init__.py b/dynestyx/__init__.py
index b5f21e51..287bd127 100644
--- a/dynestyx/__init__.py
+++ b/dynestyx/__init__.py
@@ -7,6 +7,7 @@
 from dynestyx.discretizers import Discretizer, euler_maruyama
 from dynestyx.handlers import plate, sample
 from dynestyx.inference.filters import Filter
+from dynestyx.inference.smoothers import Smoother
 from dynestyx.models import (
     ContinuousTimeStateEvolution,
     DiracIdentityObservation,
@@ -41,6 +42,7 @@
     "Discretizer",
     "ObservationModel",
     "Filter",
+    "Smoother",
     "flatten_draws",
     "plate",
     "sample",
diff --git a/dynestyx/inference/__init__.py b/dynestyx/inference/__init__.py
index 7a7d3176..d17e572c 100644
--- a/dynestyx/inference/__init__.py
+++ b/dynestyx/inference/__init__.py
@@ -5,4 +5,6 @@
     "integrations",
     "mcmc",
     "mcmc_configs",
+    "smoother_configs",
+    "smoothers",
 ]
diff --git a/dynestyx/inference/distribution_utils.py b/dynestyx/inference/distribution_utils.py
new file mode 100644
index 00000000..682e5164
--- /dev/null
+++ b/dynestyx/inference/distribution_utils.py
@@ -0,0 +1,134 @@
+from typing import Literal
+
+import jax
+import jax.numpy as jnp
+import numpyro.distributions as dist
+
+from dynestyx.inference.integrations.utils import (
+    WeightedParticles,
+    covariance_from_cholesky,
+)
+from dynestyx.inference.plate_utils import _slice_time_axis, _time_len_from_array
+
+MissingPolicy = Literal["raise", "empty"]
+
+
+def _handle_missing_gaussian_sequence(
+    *,
+    missing: MissingPolicy,
+    missing_message: str | None,
+) -> list[dist.Distribution]:
+    if missing == "empty":
+        return []
+    if missing == "raise":
+        raise ValueError(
+            missing_message or "Gaussian means/covariances were unavailable."
+        )
+    raise ValueError(f"Unknown missing Gaussian sequence policy: {missing!r}.")
+
+
+def _gaussian_sequence_to_dists(
+    means: jax.Array | None,
+    covariances: jax.Array | None,
+    *,
+    plate_shapes: tuple[int, ...] = (),
+    missing: MissingPolicy = "raise",
+    missing_message: str | None = None,
+) -> list[dist.Distribution]:
+    """Convert time-indexed Gaussian parameters to per-time distributions."""
+    if means is None or covariances is None:
+        return _handle_missing_gaussian_sequence(
+            missing=missing,
+            missing_message=missing_message,
+        )
+
+    t_len = _time_len_from_array(means, plate_shapes)
+    return [
+        dist.MultivariateNormal(
+            _slice_time_axis(means, t, plate_shapes),
+            covariance_matrix=_slice_time_axis(covariances, t, plate_shapes),
+        )
+        for t in range(t_len)
+    ]
+
+
+def _particle_sequence_to_dists(
+    particles: jax.Array,
+    log_weights: jax.Array,
+    *,
+    plate_shapes: tuple[int, ...] = (),
+) -> list[dist.Distribution]:
+    """Convert time-indexed particle arrays to per-time weighted particles."""
+    if particles.ndim == len(plate_shapes) + 2:
+        particles = particles[..., None]
+
+    normalized_log_weights = jax.nn.log_softmax(log_weights, axis=-1)
+    t_len = _time_len_from_array(normalized_log_weights, plate_shapes)
+    return [
+        WeightedParticles(
+            particles=_slice_time_axis(particles, t, plate_shapes),
+            log_weights=_slice_time_axis(normalized_log_weights, t, plate_shapes),
+        )
+        for t in range(t_len)
+    ]
+
+
+def _posterior_sequence_to_dists(
+    posterior,
+    *,
+    means_attr: str,
+    covariances_attr: str,
+    particle_mode: bool,
+    plate_shapes: tuple[int, ...] = (),
+    missing: MissingPolicy = "raise",
+    missing_message: str | None = None,
+) -> list[dist.Distribution]:
+    """Convert a backend posterior object to per-time distributions."""
+    if particle_mode:
+        return _particle_sequence_to_dists(
+            posterior.particles,
+            posterior.log_weights,
+            plate_shapes=plate_shapes,
+        )
+
+    return _gaussian_sequence_to_dists(
+        getattr(posterior, means_attr),
+        getattr(posterior, covariances_attr),
+        plate_shapes=plate_shapes,
+        missing=missing,
+        missing_message=missing_message,
+    )
+
+
+def _cholesky_state_sequence_to_dists(
+    states,
+    *,
+    particle_mode: bool,
+    plate_shapes: tuple[int, ...] = (),
+) -> list[dist.Distribution]:
+    """Convert cuthbert state objects to per-time distributions."""
+    if particle_mode:
+        return _particle_sequence_to_dists(
+            states.particles,
+            states.log_weights,
+            plate_shapes=plate_shapes,
+        )
+
+    return _gaussian_sequence_to_dists(
+        states.mean,
+        covariance_from_cholesky(states.chol_cov),
+        plate_shapes=plate_shapes,
+    )
+
+
+def _categorical_log_probs_to_dists(
+    log_probs: jax.Array,
+    *,
+    plate_shapes: tuple[int, ...] = (),
+) -> list[dist.Distribution]:
+    """Convert time-indexed categorical log-probs to per-time distributions."""
+    t_len = _time_len_from_array(log_probs, plate_shapes)
+    return [
+        dist.Categorical(probs=jnp.exp(_slice_time_axis(log_probs, t, plate_shapes)))
+        for t in range(t_len)
+    ]
diff --git a/dynestyx/inference/filter_configs.py b/dynestyx/inference/filter_configs.py
index 00d4a000..7c5b3f36 100644
--- a/dynestyx/inference/filter_configs.py
+++ b/dynestyx/inference/filter_configs.py
@@ -1,5 +1,6 @@
 """Filter configuration dataclasses. Shared by dispatchers and integration backends."""
 
+import abc
 import dataclasses
 import math
 from typing import Literal
@@ -22,7 +23,7 @@
 
 
 @dataclasses.dataclass
-class BaseFilterConfig:
+class BaseFilterConfig(abc.ABC):
     r"""Shared configuration options inherited by all filter configs.
 
     You do not instantiate this class directly; use one of the concrete
diff --git a/dynestyx/inference/filters.py b/dynestyx/inference/filters.py
index 6b4a3b22..1a782543 100644
--- a/dynestyx/inference/filters.py
+++ b/dynestyx/inference/filters.py
@@ -10,6 +10,11 @@
 
 from dynestyx.handlers import HandlesSelf, _sample_intp
 from dynestyx.inference.checkers import _validate_batched_plate_alignment
+from dynestyx.inference.distribution_utils import (
+    _categorical_log_probs_to_dists,
+    _cholesky_state_sequence_to_dists,
+    _posterior_sequence_to_dists,
+)
 from dynestyx.inference.filter_configs import (
     BaseFilterConfig,
     ContinuousTimeConfigs,
@@ -45,225 +50,14 @@
 from dynestyx.inference.integrations.cuthbert.discrete import (
     run_discrete_filter as run_cuthbert_discrete,
 )
-from dynestyx.inference.integrations.utils import (
-    WeightedParticles,
-    covariance_from_cholesky,
-    particles_to_delta_mixtures,
-)
+from dynestyx.inference.plate_utils import _array_plate_axis, _make_plate_in_axes
 from dynestyx.models import DynamicalModel
 from dynestyx.types import FunctionOfTime
-from dynestyx.utils import (
-    _array_has_plate_dims,
-    _dist_has_plate_batch_dims,
-    _ensure_continuous_bm_dim,
-    _leaf_is_plate_batched,
-    _path_field_names,
-)
+from dynestyx.utils import _ensure_continuous_bm_dim
 
 type SSMType = ContDiscreteNonlinearGaussianSSM | ContDiscreteNonlinearSSM
 
 
-def _make_plate_in_axes(tree, plate_shapes: tuple[int, ...]):
-    """Build ``in_axes`` for a pytree based on which leaves carry plate batch dims.
-
-    Returns a pytree shaped like ``tree`` whose leaves are either ``0`` (the
-    leading axis is a plate batch axis to ``vmap`` over) or ``None`` (leaf is
-    shared across plate members and should be broadcast). Distributions are
-    handled in two cases:
-
-    - **Unbatched distributions** are stopped at as opaque leaves (via
-      ``is_leaf``) and assigned ``None`` — they're shared across plate members
-      and ``vmap`` would error if asked to map over a non-existent axis.
-    - **Batched distributions** are descended into so their parameter arrays
-      (loc, scale_tril, …) can each be marked individually. This matters
-      because, e.g., a ``MultivariateNormal`` initial condition may have a
-      plate-batched ``loc.shape == (N, d)`` paired with a shared
-      ``scale_tril.shape == (d, d)``.
-
-    Initial-condition arrays use a stricter rule (``min_suffix_ndim=1``): a
-    rank-1 suffix is required for the leaf to count as plate-batched. This
-    encodes the formal version of the ambiguity rule from ``dsx.plate``: an IC
-    ``loc`` of shape ``(N,)`` could be either a per-member scalar IC or a
-    shared length-``N`` vector IC, so we conservatively treat it as shared.
-    Users should add an explicit event axis (``(N, 1)`` or ``.to_event(1)``).
-
-    The same ``in_axes`` tree is reusable across nested ``vmap`` calls: each
-    ``vmap`` peels exactly one leading axis, and a leaf marked ``0`` here means
-    "has *some* plate prefix" — which stays correct as plate axes are consumed
-    one at a time from the outside in.
-    """
-
-    def _is_unbatched_distribution_leaf(node) -> bool:
-        return isinstance(
-            node, numpyro.distributions.Distribution
-        ) and not _dist_has_plate_batch_dims(node, plate_shapes)
-
-    def _axis(path, leaf):
-        if isinstance(leaf, numpyro.distributions.Distribution):
-            # Reached only for shared (unbatched) distributions: see is_leaf.
-            return None
-        if "initial_condition" in _path_field_names(path):
-            # IC params have a vector event (loc) or matrix event (cov).
-            # Require min_suffix_ndim=1 so a bare (N,) loc is treated as
-            # shared rather than as a per-member scalar IC.
-            return (
-                0
-                if _array_has_plate_dims(leaf, plate_shapes, min_suffix_ndim=1)
-                else None
-            )
-        return 0 if _leaf_is_plate_batched(leaf, plate_shapes, path=path) else None
-
-    return jax.tree_util.tree_map_with_path(
-        _axis,
-        tree,
-        is_leaf=_is_unbatched_distribution_leaf,
-    )
-
-
-def _array_plate_axis(arr, plate_shapes: tuple[int, ...]):
-    """Return 0 if arr has leading dims matching plate_shapes, else None."""
-    return 0 if _array_has_plate_dims(arr, plate_shapes, min_suffix_ndim=1) else None
-
-
-def _get_time_axis(plate_shapes: tuple[int, ...]) -> int:
-    """Return the axis index corresponding to time after plate dimensions."""
-    return len(plate_shapes)
-
-
-def _time_len_from_array(arr: jax.Array, plate_shapes: tuple[int, ...]) -> int:
-    """Infer sequence length from an array with plate dims followed by time."""
-    return int(arr.shape[_get_time_axis(plate_shapes)])
-
-
-def _slice_time_axis(
-    arr: jax.Array, t: int, plate_shapes: tuple[int, ...]
-) -> jax.Array:
-    """Slice an array at time index t where time axis follows plate dims."""
-    time_axis = _get_time_axis(plate_shapes)
-    return arr[(slice(None),) * time_axis + (t, ...)]
-
-
-def _cuthbert_states_to_dists(
-    states,
-    config: BaseFilterConfig,
-    *,
-    plate_shapes: tuple[int, ...],
-) -> list[numpyro.distributions.Distribution]:
-    """Convert vmapped cuthbert outputs to per-time filtered distributions."""
-    if isinstance(config, PFConfig):
-        particles = states.particles
-        log_weights = states.log_weights
-        return _particle_to_batched_dists(
-            particles,
-            log_weights,
-            plate_shapes=plate_shapes,
-        )
-
-    mean = states.mean
-    chol_cov = states.chol_cov
-    cov = covariance_from_cholesky(chol_cov)
-    t_len = _time_len_from_array(mean, plate_shapes)
-    return [
-        numpyro.distributions.MultivariateNormal(
-            _slice_time_axis(mean, t, plate_shapes),
-            covariance_matrix=_slice_time_axis(cov, t, plate_shapes),
-        )
-        for t in range(t_len)
-    ]
-
-
-def _posterior_to_dists(
-    posterior,
-    *,
-    plate_shapes: tuple[int, ...],
-    particle_mode: bool,
-) -> list[numpyro.distributions.Distribution]:
-    """Convert vmapped cd-dynamax posterior objects to per-time distributions."""
-    if particle_mode:
-        particles = posterior.particles
-        log_weights = posterior.log_weights
-        return _particle_to_batched_dists(
-            particles,
-            log_weights,
-            plate_shapes=plate_shapes,
-        )
-
-    means = posterior.filtered_means
-    covs = posterior.filtered_covariances
-    if means is None or covs is None:
-        raise ValueError(
-            "Filtered means/covariances were unavailable for a Gaussian rollout path."
-        )
-    t_len = _time_len_from_array(means, plate_shapes)
-    return [
-        numpyro.distributions.MultivariateNormal(
-            _slice_time_axis(means, t, plate_shapes),
-            covariance_matrix=_slice_time_axis(covs, t, plate_shapes),
-        )
-        for t in range(t_len)
-    ]
-
-
-def _hmm_to_dists(
-    log_filt_seq: jax.Array,
-    *,
-    plate_shapes: tuple[int, ...],
-) -> list[numpyro.distributions.Distribution]:
-    """Convert vmapped HMM filtered log-probs to Categorical distributions."""
-    t_len = _time_len_from_array(log_filt_seq, plate_shapes)
-    return [
-        numpyro.distributions.Categorical(
-            probs=jnp.exp(_slice_time_axis(log_filt_seq, t, plate_shapes))
-        )
-        for t in range(t_len)
-    ]
-
-
-def _particle_to_batched_dists(
-    particles: jax.Array,
-    log_weights: jax.Array,
-    *,
-    plate_shapes: tuple[int, ...],
-) -> list[numpyro.distributions.Distribution]:
-    """Build per-time plate-batched WeightedParticles from canonical PF outputs."""
-    if particles.ndim == len(plate_shapes) + 2:
-        particles = particles[..., None]
-
-    # Flatten plate members -> use the canonical per-member helper from
-    # dynestyx.inference.integrations.utils.
-    n_members = math.prod(plate_shapes) if plate_shapes else 1
-    t_len = _time_len_from_array(log_weights, plate_shapes)
-    part_tail = particles.shape[len(plate_shapes) :]
-    w_tail = log_weights.shape[len(plate_shapes) :]
-    flat_particles = particles.reshape((n_members, *part_tail))
-    flat_log_weights = log_weights.reshape((n_members, *w_tail))
-    per_member = [
-        particles_to_delta_mixtures(flat_particles[i], flat_log_weights[i])
-        for i in range(n_members)
-    ]
-
-    if not plate_shapes:
-        return per_member[0]
-
-    result: list[numpyro.distributions.Distribution] = []
-    for t in range(t_len):
-        logits_t = jnp.stack(
-            [per_member[i][t].log_weights for i in range(n_members)],  # type: ignore[attr-defined]
-            axis=0,
-        ).reshape(*plate_shapes, -1)
-        values_t = jnp.stack(
-            [per_member[i][t].particles for i in range(n_members)],  # type: ignore[attr-defined]
-            axis=0,
-        ).reshape(*plate_shapes, *per_member[0][t].particles.shape)  # type: ignore[attr-defined]
-        result.append(
-            WeightedParticles(
-                particles=values_t,
-                log_weights=logits_t,
-            )
-        )
-    return result
-
-
 class BaseLogFactorAdder(ObjectInterpretation, HandlesSelf):
     """Base for filter handlers."""
 
@@ -633,26 +427,36 @@ def compute_output(dyn, ot, ov, ct, cv, k):
 
         if output_kind == "continuous":
             particle_mode = isinstance(config, ContinuousTimeDPFConfig)
-            return _posterior_to_dists(
+            return _posterior_sequence_to_dists(
                 outputs,
+                means_attr="filtered_means",
+                covariances_attr="filtered_covariances",
                 plate_shapes=plate_shapes,
                 particle_mode=particle_mode,
+                missing_message=(
+                    "Filtered means/covariances were unavailable for a Gaussian rollout path."
+                ),
             )
         if output_kind == "cd_dynamax_discrete":
-            return _posterior_to_dists(
+            return _posterior_sequence_to_dists(
                 outputs,
+                means_attr="filtered_means",
+                covariances_attr="filtered_covariances",
                 plate_shapes=plate_shapes,
                 particle_mode=False,
+                missing_message=(
+                    "Filtered means/covariances were unavailable for a Gaussian rollout path."
+                ),
             )
         if output_kind == "hmm":
-            return _hmm_to_dists(
+            return _categorical_log_probs_to_dists(
                 log_filt_seq,
                 plate_shapes=plate_shapes,
             )
         if output_kind == "cuthbert":
-            return _cuthbert_states_to_dists(
+            return _cholesky_state_sequence_to_dists(
                 states,
-                config,
+                particle_mode=isinstance(config, PFConfig),
                 plate_shapes=plate_shapes,
             )
 
diff --git a/dynestyx/inference/integrations/cd_dynamax/continuous.py b/dynestyx/inference/integrations/cd_dynamax/continuous.py
index 47345e69..81f5931f 100644
--- a/dynestyx/inference/integrations/cd_dynamax/continuous.py
+++ b/dynestyx/inference/integrations/cd_dynamax/continuous.py
@@ -1,294 +1,19 @@
-"""Continuous-time filters via CD-Dynamax: KF, EnKF, DPF, EKF, UKF."""
+"""Compatibility wrapper for cd-dynamax continuous-time filtering/smoothing."""
 
-import jax
-import jax.numpy as jnp
-import numpyro
-import numpyro.distributions as dist
-from cd_dynamax import (
-    ContDiscreteLinearGaussianSSM,
-    ContDiscreteNonlinearGaussianSSM,
-    ContDiscreteNonlinearSSM,
+from dynestyx.inference.integrations.cd_dynamax.continuous_filter import (
+    ContinuousTimeFilterConfig,
+    compute_continuous_filter,
+    run_continuous_filter,
 )
-from cd_dynamax.src.continuous_discrete_linear_gaussian_ssm.models import (
-    PosteriorGSSMFiltered,
+from dynestyx.inference.integrations.cd_dynamax.continuous_smoother import (
+    compute_continuous_smoother,
+    run_continuous_smoother,
 )
 
-from dynestyx.inference.filter_configs import (
-    ContinuousTimeDPFConfig,
-    ContinuousTimeEKFConfig,
-    ContinuousTimeEnKFConfig,
-    ContinuousTimeKFConfig,
-    ContinuousTimeUKFConfig,
-    _config_to_record_kwargs,
-)
-from dynestyx.inference.integrations.cd_dynamax.utils import (
-    dsx_to_cd_dynamax,
-    dsx_to_cdlgssm_params,
-)
-from dynestyx.inference.integrations.utils import particles_to_delta_mixtures
-from dynestyx.models import DynamicalModel
-from dynestyx.utils import _should_record_field
-
-type SSMType = ContDiscreteNonlinearGaussianSSM | ContDiscreteNonlinearSSM
-
-ContinuousTimeFilterConfig = (
-    ContinuousTimeKFConfig
-    | ContinuousTimeEnKFConfig
-    | ContinuousTimeDPFConfig
-    | ContinuousTimeEKFConfig
-    | ContinuousTimeUKFConfig
-)
-
-
-def _config_to_cd_dynamax_filter_kwargs(
-    config: ContinuousTimeFilterConfig,
-    params,
-    obs_values,
-    obs_times,
-    ctrl_values,
-    key,
-) -> dict:
-    """Build the filter_kwargs dict passed to cd_dynamax_model.filter()."""
-
-    # cd-dynamax uses the legacy PRNG key interface, but newer numpyro uses typed keys.
-    # We should convert accordingly.
-    # https://docs.jax.dev/en/latest/jax.random.html#module-jax.random
-    if jnp.issubdtype(key.dtype, jax.dtypes.prng_key):
-        key = jax.random.key_data(key)
-
-    base = {
-        "params": params,
-        "emissions": obs_values,
-        "t_emissions": obs_times,
-        "inputs": ctrl_values,
-        "key": key,
-        "filter_state_order": config.filter_state_order,
-        "filter_state_cov_rescaling": config.cov_rescaling
-        if config.cov_rescaling is not None
-        else 1.0,
-        "diffeqsolve_max_steps": config.diffeqsolve_max_steps,
-        "diffeqsolve_dt0": config.diffeqsolve_dt0,
-        "diffeqsolve_kwargs": config.diffeqsolve_kwargs,
-        "extra_filter_kwargs": config.extra_filter_kwargs,
-        "warn": config.warn,
-    }
-    if isinstance(config, ContinuousTimeEnKFConfig):
-        base["filter_type"] = "EnKF"
-        base["enkf_N_particles"] = config.n_particles
-        base["enkf_inflation_delta"] = (
-            config.inflation_delta if config.inflation_delta is not None else 0.0
-        )
-        base["extra_filter_kwargs"] = {
-            "perturb_measurements": config.perturb_measurements
-            if config.perturb_measurements is not None
-            else True,
-            **config.extra_filter_kwargs,
-        }
-    elif isinstance(config, ContinuousTimeEKFConfig):
-        base["filter_type"] = "EKF"
-        base["filter_emission_order"] = config.filter_emission_order
-        base["filter_num_iter"] = 1
-    elif isinstance(config, ContinuousTimeUKFConfig):
-        base["filter_type"] = "UKF"
-        base["extra_filter_kwargs"] = {
-            "alpha": config.alpha,
-            "beta": config.beta,
-            "kappa": config.kappa,
-            **config.extra_filter_kwargs,
-        }
-    elif isinstance(config, ContinuousTimeDPFConfig):
-        if config.resampling_method.base_method != "multinomial":
-            raise ValueError(
-                "Only multinomial resampling is supported for CD-Dynamax DPF."
-            )
-        base["filter_type"] = "DPF"
-        base["N_particles"] = config.n_particles
-        base["extra_filter_kwargs"] = {
-            "resample_method": config.resampling_method.differential_method,
-            "softness": config.resampling_method.softness,
-            "ess_threshold_ratio": config.ess_threshold_ratio,
-            **config.extra_filter_kwargs,
-        }
-    return base
-
-
-def _add_filter_sites(
-    name: str,
-    filter_config: ContinuousTimeFilterConfig,
-    filtered,
-) -> None:
-    """Add marginal log-likelihood factor and filtered state deterministic sites."""
-    record_kwargs = _config_to_record_kwargs(filter_config)
-    numpyro.factor(f"{name}_marginal_log_likelihood", filtered.marginal_loglik)
-    numpyro.deterministic(f"{name}_marginal_loglik", filtered.marginal_loglik)
-
-    max_elems = record_kwargs["record_max_elems"]
-    means_shape = filtered.filtered_means.shape
-    cov_shape = filtered.filtered_covariances.shape
-    add_mean = _should_record_field(
-        record_kwargs["record_filtered_states_mean"], means_shape, max_elems
-    )
-    add_cov = _should_record_field(
-        record_kwargs["record_filtered_states_cov"], cov_shape, max_elems
-    )
-    add_cov_diag = _should_record_field(
-        record_kwargs["record_filtered_states_cov_diag"],
-        (cov_shape[0], cov_shape[1]),
-        max_elems,
-    )
-    if add_mean:
-        numpyro.deterministic(f"{name}_filtered_states_mean", filtered.filtered_means)
-    if add_cov:
-        numpyro.deterministic(
-            f"{name}_filtered_states_cov", filtered.filtered_covariances
-        )
-    if add_cov_diag:
-        diag_cov = jnp.diagonal(filtered.filtered_covariances, axis1=1, axis2=2)
-        numpyro.deterministic(f"{name}_filtered_states_cov_diag", diag_cov)
-
-
-def _run_linear_kf(
-    dynamics: DynamicalModel,
-    obs_times,
-    obs_values,
-    ctrl_values,
-    filter_config: ContinuousTimeKFConfig,
-) -> PosteriorGSSMFiltered:
-    """Run exact continuous-discrete KF (AffineLinearDrift + constant diffusion + LinearGaussianObservation)."""
-    params = dsx_to_cdlgssm_params(dynamics)
-    cd_model = ContDiscreteLinearGaussianSSM(
-        state_dim=dynamics.state_dim,
-        emission_dim=dynamics.observation_dim,
-        input_dim=dynamics.control_dim,
-    )
-    filtered = cd_model.filter(
-        params=params,
-        emissions=obs_values,
-        t_emissions=obs_times,
-        inputs=ctrl_values,
-        warn=filter_config.warn,
-    )
-    return filtered
-
-
-def compute_continuous_filter(
-    dynamics: DynamicalModel,
-    filter_config: ContinuousTimeFilterConfig,
-    key: jax.Array | None = None,
-    *,
-    obs_times: jax.Array,
-    obs_values: jax.Array,
-    ctrl_times=None,
-    ctrl_values=None,
-):
-    """Pure-JAX continuous-time filter computation (no numpyro side-effects).
-
-    Returns:
-        The cd-dynamax filtered posterior object (contains marginal_loglik,
-        filtered_means, filtered_covariances, and for DPF: particles, log_weights).
-    """
-    obs_times_arr = jnp.asarray(obs_times)
-    if obs_times_arr.ndim == 1:
-        obs_times_arr = obs_times_arr[:, None]
-
-    control_dim = dynamics.control_dim
-    ctrl_vals = (
-        ctrl_values
-        if ctrl_values is not None
-        else jnp.zeros((obs_times_arr.shape[0], control_dim))
-    )
-
-    if isinstance(filter_config, ContinuousTimeKFConfig):
-        filtered = _run_linear_kf(
-            dynamics, obs_times_arr, obs_values, ctrl_vals, filter_config
-        )
-    else:
-        if isinstance(
-            filter_config, (ContinuousTimeEnKFConfig, ContinuousTimeDPFConfig)
-        ):
-            if key is None:
-                raise ValueError(
-                    f"{type(filter_config).__name__} requires a PRNG key: set 'crn_seed' in the filter config, "
-                    "or run inside a NumPyro seeded context (e.g., with numpyro.handlers.seed)."
-                )
-
-        if isinstance(filter_config, ContinuousTimeDPFConfig):
-            cd_dynamax_model: SSMType = ContDiscreteNonlinearSSM(
-                state_dim=dynamics.state_dim,
-                emission_dim=dynamics.observation_dim,
-                input_dim=dynamics.control_dim,
-            )
-        else:
-            cd_dynamax_model = ContDiscreteNonlinearGaussianSSM(
-                state_dim=dynamics.state_dim,
-                emission_dim=dynamics.observation_dim,
-                input_dim=dynamics.control_dim,
-            )
-
-        params, _ = dsx_to_cd_dynamax(dynamics, cd_model=cd_dynamax_model)
-        filter_kwargs = _config_to_cd_dynamax_filter_kwargs(
-            filter_config, params, obs_values, obs_times_arr, ctrl_vals, key
-        )
-
-        filtered = cd_dynamax_model.filter(**filter_kwargs)  # type: ignore
-
-    return filtered
-
-
-def run_continuous_filter(
-    name: str,
-    dynamics: DynamicalModel,
-    filter_config: ContinuousTimeFilterConfig,
-    key: jax.Array | None = None,
-    *,
-    obs_times: jax.Array,
-    obs_values: jax.Array,
-    ctrl_times=None,
-    ctrl_values=None,
-    **kwargs,
-) -> list[numpyro.distributions.Distribution]:
-    """Run continuous-time filter via CD-Dynamax.
-
-    Args:
-        name: Name of the factor.
-        dynamics: Dynamical model to filter.
-        filter_config: ContinuousTimeKFConfig, ContinuousTimeEnKFConfig,
-            ContinuousTimeDPFConfig, ContinuousTimeEKFConfig, or ContinuousTimeUKFConfig.
-        key: Random key (optional).
-        obs_times: Observation times.
-        obs_values: Observed values.
-        ctrl_times: Control times (optional).
-        ctrl_values: Control values (optional).
-
-    Returns:
-        list[numpyro.distributions.Distribution]: A list of distributions for the filtered states.
-    """
-    filtered = compute_continuous_filter(
-        dynamics,
-        filter_config,
-        key,
-        obs_times=obs_times,
-        obs_values=obs_values,
-        ctrl_times=ctrl_times,
-        ctrl_values=ctrl_values,
-    )
-
-    _add_filter_sites(name, filter_config, filtered)
-
-    if not isinstance(filter_config, ContinuousTimeDPFConfig):
-        means = filtered.filtered_means
-        covs = filtered.filtered_covariances
-        if means is None or covs is None:
-            raise ValueError(
-                "Filtered means/covariances unexpectedly None for non-DPF config"
-            )
-        return [
-            dist.MultivariateNormal(means[i], covs[i]) for i in range(means.shape[0])
-        ]
-    else:
-        # PF: filtered has particles and log_weights (DPF-specific, not in base type)
-        particles = filtered.particles  # type: ignore[attr-defined]
-        log_weights = filtered.log_weights  # type: ignore[attr-defined]
-        if particles.ndim == 2:
-            particles = particles[..., None]
-        return particles_to_delta_mixtures(particles, log_weights)
+__all__ = [
+    "ContinuousTimeFilterConfig",
+    "compute_continuous_filter",
+    "run_continuous_filter",
+    "compute_continuous_smoother",
+    "run_continuous_smoother",
+]
diff --git a/dynestyx/inference/integrations/cd_dynamax/continuous_filter.py b/dynestyx/inference/integrations/cd_dynamax/continuous_filter.py
new file mode 100644
index 00000000..6fc63b8b
--- /dev/null
+++ b/dynestyx/inference/integrations/cd_dynamax/continuous_filter.py
@@ -0,0 +1,270 @@
+"""Continuous-time filters via CD-Dynamax: KF, EnKF, DPF, EKF, UKF."""
+
+import jax
+import jax.numpy as jnp
+import numpyro
+from cd_dynamax import (
+    ContDiscreteLinearGaussianSSM,
+    ContDiscreteNonlinearGaussianSSM,
+    ContDiscreteNonlinearSSM,
+)
+from cd_dynamax.src.continuous_discrete_linear_gaussian_ssm.models import (
+    PosteriorGSSMFiltered,
+)
+
+from dynestyx.inference.distribution_utils import _posterior_sequence_to_dists
+from dynestyx.inference.filter_configs import (
+    ContinuousTimeDPFConfig,
+    ContinuousTimeEKFConfig,
+    ContinuousTimeEnKFConfig,
+    ContinuousTimeKFConfig,
+    ContinuousTimeUKFConfig,
+    _config_to_record_kwargs,
+)
+from dynestyx.inference.integrations.cd_dynamax.utils import (
+    dsx_to_cd_dynamax,
+    dsx_to_cdlgssm_params,
+)
+from dynestyx.models import DynamicalModel
+from dynestyx.utils import _should_record_field
+
+type SSMType = ContDiscreteNonlinearGaussianSSM | ContDiscreteNonlinearSSM
+
+ContinuousTimeFilterConfig = (
+    ContinuousTimeKFConfig
+    | ContinuousTimeEnKFConfig
+    | ContinuousTimeDPFConfig
+    | ContinuousTimeEKFConfig
+    | ContinuousTimeUKFConfig
+)
+
+
+def _config_to_cd_dynamax_filter_kwargs(
+    config: ContinuousTimeFilterConfig,
+    params,
+    obs_values,
+    obs_times,
+    ctrl_values,
+    key,
+) -> dict:
+    """Build the filter_kwargs dict passed to cd_dynamax_model.filter()."""
+
+    # cd-dynamax uses the legacy PRNG key interface, but newer numpyro uses typed keys.
+    # We should convert accordingly.
+    # https://docs.jax.dev/en/latest/jax.random.html#module-jax.random
+    if jnp.issubdtype(key.dtype, jax.dtypes.prng_key):
+        key = jax.random.key_data(key)
+
+    base = {
+        "params": params,
+        "emissions": obs_values,
+        "t_emissions": obs_times,
+        "inputs": ctrl_values,
+        "key": key,
+        "filter_state_order": config.filter_state_order,
+        "filter_state_cov_rescaling": config.cov_rescaling
+        if config.cov_rescaling is not None
+        else 1.0,
+        "diffeqsolve_max_steps": config.diffeqsolve_max_steps,
+        "diffeqsolve_dt0": config.diffeqsolve_dt0,
+        "diffeqsolve_kwargs": config.diffeqsolve_kwargs,
+        "extra_filter_kwargs": config.extra_filter_kwargs,
+        "warn": config.warn,
+    }
+    if isinstance(config, ContinuousTimeEnKFConfig):
+        base["filter_type"] = "EnKF"
+        base["enkf_N_particles"] = config.n_particles
+        base["enkf_inflation_delta"] = (
+            config.inflation_delta if config.inflation_delta is not None else 0.0
+        )
+        base["extra_filter_kwargs"] = {
+            "perturb_measurements": config.perturb_measurements
+            if config.perturb_measurements is not None
+            else True,
+            **config.extra_filter_kwargs,
+        }
+    elif isinstance(config, ContinuousTimeEKFConfig):
+        base["filter_type"] = "EKF"
+        base["filter_emission_order"] = config.filter_emission_order
+        base["filter_num_iter"] = 1
+    elif isinstance(config, ContinuousTimeUKFConfig):
+        base["filter_type"] = "UKF"
+        base["extra_filter_kwargs"] = {
+            "alpha": config.alpha,
+            "beta": config.beta,
+            "kappa": config.kappa,
+            **config.extra_filter_kwargs,
+        }
+    elif isinstance(config, ContinuousTimeDPFConfig):
+        if config.resampling_method.base_method != "multinomial":
+            raise ValueError(
+                "Only multinomial resampling is supported for CD-Dynamax DPF."
+            )
+        base["filter_type"] = "DPF"
+        base["N_particles"] = config.n_particles
+        base["extra_filter_kwargs"] = {
+            "resample_method": config.resampling_method.differential_method,
+            "softness": config.resampling_method.softness,
+            "ess_threshold_ratio": config.ess_threshold_ratio,
+            **config.extra_filter_kwargs,
+        }
+    return base
+
+
+def _add_filter_sites(
+    name: str,
+    filter_config: ContinuousTimeFilterConfig,
+    filtered,
+) -> None:
+    """Add marginal log-likelihood factor and filtered state deterministic sites."""
+    record_kwargs = _config_to_record_kwargs(filter_config)
+    numpyro.factor(f"{name}_marginal_log_likelihood", filtered.marginal_loglik)
+    numpyro.deterministic(f"{name}_marginal_loglik", filtered.marginal_loglik)
+
+    max_elems = record_kwargs["record_max_elems"]
+    means_shape = filtered.filtered_means.shape
+    cov_shape = filtered.filtered_covariances.shape
+    add_mean = _should_record_field(
+        record_kwargs["record_filtered_states_mean"], means_shape, max_elems
+    )
+    add_cov = _should_record_field(
+        record_kwargs["record_filtered_states_cov"], cov_shape, max_elems
+    )
+    add_cov_diag = _should_record_field(
+        record_kwargs["record_filtered_states_cov_diag"],
+        (cov_shape[0], cov_shape[1]),
+        max_elems,
+    )
+    if add_mean:
+        numpyro.deterministic(f"{name}_filtered_states_mean", filtered.filtered_means)
+    if add_cov:
+        numpyro.deterministic(
+            f"{name}_filtered_states_cov", filtered.filtered_covariances
+        )
+    if add_cov_diag:
+        diag_cov = jnp.diagonal(filtered.filtered_covariances, axis1=1, axis2=2)
+        numpyro.deterministic(f"{name}_filtered_states_cov_diag", diag_cov)
+
+
+def _run_linear_kf(
+    dynamics: DynamicalModel,
+    obs_times,
+    obs_values,
+    ctrl_values,
+    filter_config: ContinuousTimeKFConfig,
+) -> PosteriorGSSMFiltered:
+    """Run exact continuous-discrete KF (AffineLinearDrift + constant diffusion + LinearGaussianObservation)."""
+    params = dsx_to_cdlgssm_params(dynamics)
+    cd_model = ContDiscreteLinearGaussianSSM(
+        state_dim=dynamics.state_dim,
+        emission_dim=dynamics.observation_dim,
+        input_dim=dynamics.control_dim,
+    )
+    filtered = cd_model.filter(
+        params=params,
+        emissions=obs_values,
+        t_emissions=obs_times,
+        inputs=ctrl_values,
+        warn=filter_config.warn,
+    )
+    return filtered
+
+
+def compute_continuous_filter(
+    dynamics: DynamicalModel,
+    filter_config: ContinuousTimeFilterConfig,
+    key: jax.Array | None = None,
+    *,
+    obs_times: jax.Array,
+    obs_values: jax.Array,
+    ctrl_times=None,
+    ctrl_values=None,
+):
+    """Pure-JAX continuous-time filter computation (no numpyro side-effects)."""
+    obs_times_arr = jnp.asarray(obs_times)
+    if obs_times_arr.ndim == 1:
+        obs_times_arr = obs_times_arr[:, None]
+
+    control_dim = dynamics.control_dim
+    ctrl_vals = (
+        ctrl_values
+        if ctrl_values is not None
+        else jnp.zeros((obs_times_arr.shape[0], control_dim))
+    )
+
+    if isinstance(filter_config, ContinuousTimeKFConfig):
+        filtered = _run_linear_kf(
+            dynamics, obs_times_arr, obs_values, ctrl_vals, filter_config
+        )
+    else:
+        if isinstance(
+            filter_config, (ContinuousTimeEnKFConfig, ContinuousTimeDPFConfig)
+        ):
+            if key is None:
+                raise ValueError(
+                    f"{type(filter_config).__name__} requires a PRNG key: set 'crn_seed' in the filter config, "
+                    "or run inside a NumPyro seeded context (e.g., with numpyro.handlers.seed)."
+                )
+
+        if isinstance(filter_config, ContinuousTimeDPFConfig):
+            cd_dynamax_model: SSMType = ContDiscreteNonlinearSSM(
+                state_dim=dynamics.state_dim,
+                emission_dim=dynamics.observation_dim,
+                input_dim=dynamics.control_dim,
+            )
+        else:
+            cd_dynamax_model = ContDiscreteNonlinearGaussianSSM(
+                state_dim=dynamics.state_dim,
+                emission_dim=dynamics.observation_dim,
+                input_dim=dynamics.control_dim,
+            )
+
+        params, _ = dsx_to_cd_dynamax(dynamics, cd_model=cd_dynamax_model)
+        filter_kwargs = _config_to_cd_dynamax_filter_kwargs(
+            filter_config, params, obs_values, obs_times_arr, ctrl_vals, key
+        )
+
+        filtered = cd_dynamax_model.filter(**filter_kwargs)  # type: ignore
+
+    return filtered
+
+
+def run_continuous_filter(
+    name: str,
+    dynamics: DynamicalModel,
+    filter_config: ContinuousTimeFilterConfig,
+    key: jax.Array | None = None,
+    *,
+    obs_times: jax.Array,
+    obs_values: jax.Array,
+    ctrl_times=None,
+    ctrl_values=None,
+    **kwargs,
+) -> list[numpyro.distributions.Distribution]:
+    """Run continuous-time filter via CD-Dynamax."""
+    filtered = compute_continuous_filter(
+        dynamics,
+        filter_config,
+        key,
+        obs_times=obs_times,
+        obs_values=obs_values,
+        ctrl_times=ctrl_times,
+        ctrl_values=ctrl_values,
+    )
+
+    _add_filter_sites(name, filter_config, filtered)
+
+    return _posterior_sequence_to_dists(
+        filtered,
+        means_attr="filtered_means",
+        covariances_attr="filtered_covariances",
+        particle_mode=isinstance(filter_config, ContinuousTimeDPFConfig),
+        missing_message="Filtered means/covariances unexpectedly None for non-DPF config",
+    )
+
+
+__all__ = [
+    "ContinuousTimeFilterConfig",
+    "compute_continuous_filter",
+    "run_continuous_filter",
+]
diff --git a/dynestyx/inference/integrations/cd_dynamax/continuous_smoother.py b/dynestyx/inference/integrations/cd_dynamax/continuous_smoother.py
new file mode 100644
index 00000000..f3f55f81
--- /dev/null
+++ b/dynestyx/inference/integrations/cd_dynamax/continuous_smoother.py
@@ -0,0 +1,187 @@
+"""Continuous-time smoothers via CD-Dynamax: KF, EKF."""
+
+import jax
+import jax.numpy as jnp
+import numpyro
+from cd_dynamax import (
+    ContDiscreteNonlinearGaussianSSM,
+    EKFHyperParams,
+    KFHyperParams,
+    cdlgssm_smoother,
+    cdnlgssm_smoother,
+)
+
+from dynestyx.inference.distribution_utils import _posterior_sequence_to_dists
+from dynestyx.inference.integrations.cd_dynamax.utils import (
+    dsx_to_cd_dynamax,
+    dsx_to_cdlgssm_params,
+)
+from dynestyx.inference.smoother_configs import (
+    ContinuousTimeEKFSmootherConfig,
+    ContinuousTimeKFSmootherConfig,
+    _config_to_smoother_record_kwargs,
+)
+from dynestyx.models import DynamicalModel
+from dynestyx.utils import _should_record_field
+
+ContinuousTimeSmootherConfig = (
+    ContinuousTimeKFSmootherConfig | ContinuousTimeEKFSmootherConfig
+)
+
+
+def _add_smoother_sites(
+    name: str,
+    smoother_config: ContinuousTimeSmootherConfig,
+    smoothed,
+) -> None:
+    """Add marginal log-likelihood factor and smoothed state deterministic sites."""
+    record_kwargs = _config_to_smoother_record_kwargs(smoother_config)
+    numpyro.factor(f"{name}_marginal_log_likelihood", smoothed.marginal_loglik)
+    numpyro.deterministic(f"{name}_marginal_loglik", smoothed.marginal_loglik)
+
+    max_elems = record_kwargs["record_max_elems"]
+    means = smoothed.smoothed_means
+    covs = smoothed.smoothed_covariances
+    means_shape = means.shape
+    cov_shape = covs.shape
+    add_mean = _should_record_field(
+        record_kwargs["record_smoothed_states_mean"], means_shape, max_elems
+    )
+    add_cov = _should_record_field(
+        record_kwargs["record_smoothed_states_cov"], cov_shape, max_elems
+    )
+    add_cov_diag = _should_record_field(
+        record_kwargs["record_smoothed_states_cov_diag"],
+        (cov_shape[0], cov_shape[1]),
+        max_elems,
+    )
+    if add_mean:
+        numpyro.deterministic(f"{name}_smoothed_states_mean", means)
+    if add_cov:
+        numpyro.deterministic(f"{name}_smoothed_states_cov", covs)
+    if add_cov_diag:
+        diag_cov = jnp.diagonal(covs, axis1=1, axis2=2)
+        numpyro.deterministic(f"{name}_smoothed_states_cov_diag", diag_cov)
+
+
+def compute_continuous_smoother(
+    dynamics: DynamicalModel,
+    smoother_config: ContinuousTimeSmootherConfig,
+    key: jax.Array | None = None,
+    *,
+    obs_times: jax.Array,
+    obs_values: jax.Array,
+    ctrl_times=None,
+    ctrl_values=None,
+):
+    """Pure-JAX continuous-time smoother computation (no numpyro side-effects)."""
+    obs_times_arr = jnp.asarray(obs_times)
+    if obs_times_arr.ndim == 1:
+        obs_times_arr = obs_times_arr[:, None]
+
+    control_dim = dynamics.control_dim
+    ctrl_vals = (
+        ctrl_values
+        if ctrl_values is not None
+        else jnp.zeros((obs_times_arr.shape[0], control_dim))
+    )
+
+    if isinstance(smoother_config, ContinuousTimeKFSmootherConfig):
+        params = dsx_to_cdlgssm_params(dynamics)
+        kf_hparams = KFHyperParams(
+            dt_final=float(smoother_config.extra_filter_kwargs.get("dt_final", 1e-10)),
+            diffeqsolve_settings={
+                "dt0": smoother_config.diffeqsolve_dt0,
+                "max_steps": smoother_config.diffeqsolve_max_steps,
+                **smoother_config.diffeqsolve_kwargs,
+            },
+        )
+        return cdlgssm_smoother(
+            params=params,
+            emissions=obs_values,
+            t_emissions=obs_times_arr,
+            filter_hyperparams=kf_hparams,
+            inputs=ctrl_vals,
+            smoother_type=smoother_config.cdlgssm_smoother_type,
+            warn=smoother_config.warn,
+        )
+
+    if isinstance(smoother_config, ContinuousTimeEKFSmootherConfig):
+        cd_model = ContDiscreteNonlinearGaussianSSM(
+            state_dim=dynamics.state_dim,
+            emission_dim=dynamics.observation_dim,
+            input_dim=dynamics.control_dim,
+        )
+        params, _ = dsx_to_cd_dynamax(dynamics, cd_model=cd_model)
+        ekf_hparams = EKFHyperParams(
+            dt_final=float(smoother_config.extra_filter_kwargs.get("dt_final", 1e-4)),
+            state_order=smoother_config.filter_state_order,
+            emission_order=smoother_config.filter_emission_order,
+            smooth_order=str(
+                smoother_config.extra_filter_kwargs.get("smooth_order", "first")
+            ),
+            cov_rescaling=(
+                smoother_config.cov_rescaling
+                if smoother_config.cov_rescaling is not None
+                else 1.0
+            ),
+            diffeqsolve_settings={
+                "dt0": smoother_config.diffeqsolve_dt0,
+                "max_steps": smoother_config.diffeqsolve_max_steps,
+                **smoother_config.diffeqsolve_kwargs,
+            },
+            dt_average=float(
+                smoother_config.extra_filter_kwargs.get("dt_average", 0.1)
+            ),
+        )
+        return cdnlgssm_smoother(
+            params=params,
+            emissions=obs_values,
+            t_emissions=obs_times_arr,
+            filter_hyperparams=ekf_hparams,
+            inputs=ctrl_vals,
+            num_iter=1,
+            key=key if key is not None else jax.random.PRNGKey(0),
+            warn=smoother_config.warn,
+        )
+
+    raise ValueError(
+        f"{type(smoother_config).__name__} smoothing is not supported in cd_dynamax. "
+        "Supported continuous-time smoothers: ContinuousTimeKFSmootherConfig, ContinuousTimeEKFSmootherConfig."
+    )
+
+
+def run_continuous_smoother(
+    name: str,
+    dynamics: DynamicalModel,
+    smoother_config: ContinuousTimeSmootherConfig,
+    key: jax.Array | None = None,
+    *,
+    obs_times: jax.Array,
+    obs_values: jax.Array,
+    ctrl_times=None,
+    ctrl_values=None,
+    **kwargs,
+) -> list[numpyro.distributions.Distribution]:
+    """Run continuous-time smoother via CD-Dynamax."""
+    smoothed = compute_continuous_smoother(
+        dynamics,
+        smoother_config,
+        key,
+        obs_times=obs_times,
+        obs_values=obs_values,
+        ctrl_times=ctrl_times,
+        ctrl_values=ctrl_values,
+    )
+
+    _add_smoother_sites(name, smoother_config, smoothed)
+    return _posterior_sequence_to_dists(
+        smoothed,
+        means_attr="smoothed_means",
+        covariances_attr="smoothed_covariances",
+        particle_mode=False,
+        missing_message="Smoothed means/covariances unexpectedly None.",
+    )
+
+
+__all__ = ["compute_continuous_smoother", "run_continuous_smoother"]
diff --git a/dynestyx/inference/integrations/cd_dynamax/discrete.py b/dynestyx/inference/integrations/cd_dynamax/discrete.py
index af9c19ca..8c761b17 100644
--- a/dynestyx/inference/integrations/cd_dynamax/discrete.py
+++ b/dynestyx/inference/integrations/cd_dynamax/discrete.py
@@ -1,213 +1,21 @@
-"""Discrete-time filters via cd-dynamax (dynamax): KF, EKF, UKF."""
+"""Compatibility wrapper for cd-dynamax discrete-time filtering/smoothing."""
 
-import jax
-import jax.numpy as jnp
-import numpyro
-import numpyro.distributions as dist
-from cd_dynamax.dynamax.linear_gaussian_ssm.inference import (
-    PosteriorGSSMFiltered,
-    lgssm_filter,
+from dynestyx.inference.integrations.cd_dynamax.discrete_filter import (
+    _lti_to_lgssm_params,
+    _prepare_inputs,
+    compute_cd_dynamax_discrete_filter,
+    run_discrete_filter,
 )
-from cd_dynamax.dynamax.linear_gaussian_ssm.models import LinearGaussianSSM
-from cd_dynamax.dynamax.nonlinear_gaussian_ssm.inference_ekf import (
-    extended_kalman_filter,
+from dynestyx.inference.integrations.cd_dynamax.discrete_smoother import (
+    compute_cd_dynamax_discrete_smoother,
+    run_discrete_smoother,
 )
-from cd_dynamax.dynamax.nonlinear_gaussian_ssm.inference_ukf import (
-    UKFHyperParams,
-    unscented_kalman_filter,
-)
-
-from dynestyx.inference.filter_configs import (
-    BaseFilterConfig,
-    EKFConfig,
-    KFConfig,
-    UKFConfig,
-    _config_to_record_kwargs,
-)
-from dynestyx.inference.integrations.cd_dynamax.utils import gaussian_to_nlgssm_params
-from dynestyx.inference.integrations.utils import squeeze_leading_singletons
-from dynestyx.models import (
-    DynamicalModel,
-    LinearGaussianObservation,
-    LinearGaussianStateEvolution,
-)
-from dynestyx.utils import _should_record_field
-
-
-def _lti_to_lgssm_params(dynamics: DynamicalModel):
-    """Build dynamax ParamsLGSSM from LinearGaussianSSM.initialize for an LTI model."""
-    state_dim = dynamics.state_dim
-    emission_dim = dynamics.observation_dim
-    control_dim = dynamics.control_dim
-
-    if (
-        isinstance(dynamics.state_evolution, LinearGaussianStateEvolution)
-        and isinstance(dynamics.observation_model, LinearGaussianObservation)
-        and isinstance(dynamics.initial_condition, dist.MultivariateNormal)
-    ):
-        evo = dynamics.state_evolution
-        obs = dynamics.observation_model
-        ic = dynamics.initial_condition
-        model = LinearGaussianSSM(
-            state_dim=state_dim,
-            emission_dim=emission_dim,
-            input_dim=control_dim,
-            has_dynamics_bias=evo.bias is not None,
-            has_emissions_bias=obs.bias is not None,
-        )
-        params, _ = model.initialize(
-            initial_mean=squeeze_leading_singletons(ic.loc, 1),
-            initial_covariance=squeeze_leading_singletons(ic.covariance_matrix, 2),
-            dynamics_weights=evo.A,
-            dynamics_bias=evo.bias,
-            dynamics_input_weights=evo.B,
-            dynamics_covariance=evo.cov,
-            emission_weights=obs.H,
-            emission_bias=obs.bias,
-            emission_input_weights=obs.D,
-            emission_covariance=obs.R,
-        )
-        return params
-    else:
-        raise TypeError(
-            "filter_type='kf' expects a DynamicalModel with LinearGaussianStateEvolution and LinearGaussianObservation and initial_condition as MultivariateNormal."
-        )
-
-
-def _prepare_inputs(dynamics, obs_values, obs_times, ctrl_times, ctrl_values):
-    """Prepare emissions and inputs arrays for cd-dynamax discrete filters."""
-    emissions = obs_values
-    T1 = emissions.shape[0]
-    control_dim = dynamics.control_dim
-    if ctrl_values is None:
-        inputs = jnp.zeros((T1, control_dim))
-    elif ctrl_values.shape[0] > T1:
-        inds = jnp.searchsorted(ctrl_times, obs_times, side="left")
-        inputs = ctrl_values[inds]
-    else:
-        inputs = ctrl_values
-    return emissions, inputs
-
-
-def compute_cd_dynamax_discrete_filter(
-    dynamics: DynamicalModel,
-    filter_config: BaseFilterConfig,
-    *,
-    obs_times: jax.Array,
-    obs_values: jax.Array,
-    ctrl_times=None,
-    ctrl_values=None,
-):
-    """Pure-JAX cd-dynamax discrete filter computation (no numpyro side-effects).
-
-    Returns:
-        PosteriorGSSMFiltered: The filtered posterior (contains marginal_loglik,
-        filtered_means, filtered_covariances).
-    """
-    emissions, inputs = _prepare_inputs(
-        dynamics, obs_values, obs_times, ctrl_times, ctrl_values
-    )
-
-    if isinstance(filter_config, KFConfig):
-        params = _lti_to_lgssm_params(dynamics)
-        return lgssm_filter(params, emissions, inputs=inputs)
-
-    # EKF and UKF share the same nonlinear params representation.
-    params_nl = gaussian_to_nlgssm_params(dynamics)
-
-    if isinstance(filter_config, EKFConfig):
-        return extended_kalman_filter(params_nl, emissions, inputs=inputs)
-    elif isinstance(filter_config, UKFConfig):
-        hyperparams = UKFHyperParams(
-            alpha=filter_config.alpha,
-            beta=filter_config.beta,
-            kappa=filter_config.kappa,
-        )
-        return unscented_kalman_filter(
-            params_nl, emissions, hyperparams=hyperparams, inputs=inputs
-        )
-    else:
-        raise ValueError(
-            f"Unsupported cd-dynamax discrete config: {type(filter_config).__name__}. "
-            "Expected KFConfig, EKFConfig, or UKFConfig."
-        )
-
-
-def _add_kf_sites(
-    name: str, posterior: PosteriorGSSMFiltered, record_kwargs: dict
-) -> None:
-    """Add filtered means/covariances as deterministic sites (dynamax KF posterior)."""
-    max_elems = record_kwargs["record_max_elems"]
-    if posterior.filtered_means is None:
-        return
-    means = posterior.filtered_means
-    covs = posterior.filtered_covariances
-    T1, state_dim = means.shape
-    add_mean = _should_record_field(
-        record_kwargs["record_filtered_states_mean"], means.shape, max_elems
-    )
-    add_cov = _should_record_field(
-        record_kwargs["record_filtered_states_cov"],
-        (T1, state_dim, state_dim),
-        max_elems,
-    )
-    add_cov_diag = _should_record_field(
-        record_kwargs["record_filtered_states_cov_diag"], (T1, state_dim), max_elems
-    )
-    if add_mean:
-        numpyro.deterministic(f"{name}_filtered_states_mean", means)
-    if add_cov and covs is not None:
-        numpyro.deterministic(f"{name}_filtered_states_cov", covs)
-    if add_cov_diag and covs is not None:
-        diag_cov = jnp.diagonal(covs, axis1=1, axis2=2)
-        numpyro.deterministic(f"{name}_filtered_states_cov_diag", diag_cov)
-
-
-def run_discrete_filter(
-    name: str,
-    dynamics: DynamicalModel,
-    filter_config: BaseFilterConfig,
-    *,
-    obs_times: jax.Array,
-    obs_values: jax.Array,
-    ctrl_times=None,
-    ctrl_values=None,
-    **kwargs,
-) -> list[dist.Distribution]:
-    """Run discrete-time filter via cd-dynamax (KF, EKF, UKF).
-
-    Args:
-        name: Name of the factor.
-        dynamics: Dynamical model to filter.
-        filter_config: KFConfig, EKFConfig, or UKFConfig.
-        obs_times: Observation times.
-        obs_values: Observed values.
-        ctrl_times: Control times (optional).
-        ctrl_values: Control values (optional).
-
-    Returns:
-        list[dist.Distribution]: Filtered state distributions at each obs time.
-    """
-    posterior = compute_cd_dynamax_discrete_filter(
-        dynamics,
-        filter_config,
-        obs_times=obs_times,
-        obs_values=obs_values,
-        ctrl_times=ctrl_times,
-        ctrl_values=ctrl_values,
-    )
-
-    record_kwargs = _config_to_record_kwargs(filter_config)
-    marginal_loglik = posterior.marginal_loglik
-    numpyro.factor(f"{name}_marginal_log_likelihood", marginal_loglik)
-    numpyro.deterministic(f"{name}_marginal_loglik", marginal_loglik)
-    _add_kf_sites(name, posterior, record_kwargs)
 
-    if posterior.filtered_means is None or posterior.filtered_covariances is None:
-        return []
-    return [
-        dist.MultivariateNormal(
-            posterior.filtered_means[i], posterior.filtered_covariances[i]
-        )
-        for i in range(posterior.filtered_means.shape[0])
-    ]
+__all__ = [
+    "compute_cd_dynamax_discrete_filter",
+    "run_discrete_filter",
+    "compute_cd_dynamax_discrete_smoother",
+    "run_discrete_smoother",
+    "_lti_to_lgssm_params",
+    "_prepare_inputs",
+]
diff --git a/dynestyx/inference/integrations/cd_dynamax/discrete_filter.py b/dynestyx/inference/integrations/cd_dynamax/discrete_filter.py
new file mode 100644
index 00000000..61398247
--- /dev/null
+++ b/dynestyx/inference/integrations/cd_dynamax/discrete_filter.py
@@ -0,0 +1,201 @@
+"""Discrete-time filters via cd-dynamax (dynamax): KF, EKF, UKF."""
+
+import jax
+import jax.numpy as jnp
+import numpyro
+import numpyro.distributions as dist
+from cd_dynamax.dynamax.linear_gaussian_ssm.inference import (
+    PosteriorGSSMFiltered,
+    lgssm_filter,
+)
+from cd_dynamax.dynamax.linear_gaussian_ssm.models import LinearGaussianSSM
+from cd_dynamax.dynamax.nonlinear_gaussian_ssm.inference_ekf import (
+    extended_kalman_filter,
+)
+from cd_dynamax.dynamax.nonlinear_gaussian_ssm.inference_ukf import (
+    UKFHyperParams,
+    unscented_kalman_filter,
+)
+
+from dynestyx.inference.distribution_utils import _posterior_sequence_to_dists
+from dynestyx.inference.filter_configs import (
+    BaseFilterConfig,
+    EKFConfig,
+    KFConfig,
+    UKFConfig,
+    _config_to_record_kwargs,
+)
+from dynestyx.inference.integrations.cd_dynamax.utils import gaussian_to_nlgssm_params
+from dynestyx.inference.integrations.utils import squeeze_leading_singletons
+from dynestyx.models import (
+    DynamicalModel,
+    LinearGaussianObservation,
+    LinearGaussianStateEvolution,
+)
+from dynestyx.utils import _should_record_field
+
+
+def _lti_to_lgssm_params(dynamics: DynamicalModel):
+    """Build dynamax ParamsLGSSM from LinearGaussianSSM.initialize for an LTI model."""
+    state_dim = dynamics.state_dim
+    emission_dim = dynamics.observation_dim
+    control_dim = dynamics.control_dim
+
+    if (
+        isinstance(dynamics.state_evolution, LinearGaussianStateEvolution)
+        and isinstance(dynamics.observation_model, LinearGaussianObservation)
+        and isinstance(dynamics.initial_condition, dist.MultivariateNormal)
+    ):
+        evo = dynamics.state_evolution
+        obs = dynamics.observation_model
+        ic = dynamics.initial_condition
+        model = LinearGaussianSSM(
+            state_dim=state_dim,
+            emission_dim=emission_dim,
+            input_dim=control_dim,
+            has_dynamics_bias=evo.bias is not None,
+            has_emissions_bias=obs.bias is not None,
+        )
+        params, _ = model.initialize(
+            initial_mean=squeeze_leading_singletons(ic.loc, 1),
+            initial_covariance=squeeze_leading_singletons(ic.covariance_matrix, 2),
+            dynamics_weights=evo.A,
+            dynamics_bias=evo.bias,
+            dynamics_input_weights=evo.B,
+            dynamics_covariance=evo.cov,
+            emission_weights=obs.H,
+            emission_bias=obs.bias,
+            emission_input_weights=obs.D,
+            emission_covariance=obs.R,
+        )
+        return params
+    raise TypeError(
+        "filter_type='kf' expects a DynamicalModel with LinearGaussianStateEvolution and LinearGaussianObservation and initial_condition as MultivariateNormal."
+    )
+
+
+def _prepare_inputs(dynamics, obs_values, obs_times, ctrl_times, ctrl_values):
+    """Prepare emissions and inputs arrays for cd-dynamax discrete filters."""
+    emissions = obs_values
+    t1 = emissions.shape[0]
+    control_dim = dynamics.control_dim
+    if ctrl_values is None:
+        inputs = jnp.zeros((t1, control_dim))
+    elif ctrl_values.shape[0] > t1:
+        inds = jnp.searchsorted(ctrl_times, obs_times, side="left")
+        inputs = ctrl_values[inds]
+    else:
+        inputs = ctrl_values
+    return emissions, inputs
+
+
+def compute_cd_dynamax_discrete_filter(
+    dynamics: DynamicalModel,
+    filter_config: BaseFilterConfig,
+    *,
+    obs_times: jax.Array,
+    obs_values: jax.Array,
+    ctrl_times=None,
+    ctrl_values=None,
+):
+    """Pure-JAX cd-dynamax discrete filter computation (no numpyro side-effects)."""
+    emissions, inputs = _prepare_inputs(
+        dynamics, obs_values, obs_times, ctrl_times, ctrl_values
+    )
+
+    if isinstance(filter_config, KFConfig):
+        params = _lti_to_lgssm_params(dynamics)
+        return lgssm_filter(params, emissions, inputs=inputs)
+
+    # EKF and UKF share the same nonlinear params representation.
+    params_nl = gaussian_to_nlgssm_params(dynamics)
+
+    if isinstance(filter_config, EKFConfig):
+        return extended_kalman_filter(params_nl, emissions, inputs=inputs)
+    if isinstance(filter_config, UKFConfig):
+        hyperparams = UKFHyperParams(
+            alpha=filter_config.alpha,
+            beta=filter_config.beta,
+            kappa=filter_config.kappa,
+        )
+        return unscented_kalman_filter(
+            params_nl, emissions, hyperparams=hyperparams, inputs=inputs
+        )
+    raise ValueError(
+        f"Unsupported cd-dynamax discrete config: {type(filter_config).__name__}. "
+        "Expected KFConfig, EKFConfig, or UKFConfig."
+    )
+
+
+def _add_kf_sites(
+    name: str, posterior: PosteriorGSSMFiltered, record_kwargs: dict
+) -> None:
+    """Add filtered means/covariances as deterministic sites (dynamax KF posterior)."""
+    max_elems = record_kwargs["record_max_elems"]
+    if posterior.filtered_means is None:
+        return
+    means = posterior.filtered_means
+    covs = posterior.filtered_covariances
+    t1, state_dim = means.shape
+    add_mean = _should_record_field(
+        record_kwargs["record_filtered_states_mean"], means.shape, max_elems
+    )
+    add_cov = _should_record_field(
+        record_kwargs["record_filtered_states_cov"],
+        (t1, state_dim, state_dim),
+        max_elems,
+    )
+    add_cov_diag = _should_record_field(
+        record_kwargs["record_filtered_states_cov_diag"], (t1, state_dim), max_elems
+    )
+    if add_mean:
+        numpyro.deterministic(f"{name}_filtered_states_mean", means)
+    if add_cov and covs is not None:
+        numpyro.deterministic(f"{name}_filtered_states_cov", covs)
+    if add_cov_diag and covs is not None:
+        diag_cov = jnp.diagonal(covs, axis1=1, axis2=2)
+        numpyro.deterministic(f"{name}_filtered_states_cov_diag", diag_cov)
+
+
+def run_discrete_filter(
+    name: str,
+    dynamics: DynamicalModel,
+    filter_config: BaseFilterConfig,
+    *,
+    obs_times: jax.Array,
+    obs_values: jax.Array,
+    ctrl_times=None,
+    ctrl_values=None,
+    **kwargs,
+) -> list[dist.Distribution]:
+    """Run discrete-time filter via cd-dynamax (KF, EKF, UKF)."""
+    posterior = compute_cd_dynamax_discrete_filter(
+        dynamics,
+        filter_config,
+        obs_times=obs_times,
+        obs_values=obs_values,
+        ctrl_times=ctrl_times,
+        ctrl_values=ctrl_values,
+    )
+
+    record_kwargs = _config_to_record_kwargs(filter_config)
+    marginal_loglik = posterior.marginal_loglik
+    numpyro.factor(f"{name}_marginal_log_likelihood", marginal_loglik)
+    numpyro.deterministic(f"{name}_marginal_loglik", marginal_loglik)
+    _add_kf_sites(name, posterior, record_kwargs)
+
+    return _posterior_sequence_to_dists(
+        posterior,
+        means_attr="filtered_means",
+        covariances_attr="filtered_covariances",
+        particle_mode=False,
+        missing="empty",
+    )
+
+
+__all__ = [
+    "compute_cd_dynamax_discrete_filter",
+    "run_discrete_filter",
+    "_lti_to_lgssm_params",
+    "_prepare_inputs",
+]
diff --git a/dynestyx/inference/integrations/cd_dynamax/discrete_smoother.py b/dynestyx/inference/integrations/cd_dynamax/discrete_smoother.py
new file mode 100644
index 00000000..1686ccc1
--- /dev/null
+++ b/dynestyx/inference/integrations/cd_dynamax/discrete_smoother.py
@@ -0,0 +1,135 @@
+"""Discrete-time smoothers via cd-dynamax (dynamax): KF, EKF, UKF."""
+
+import jax
+import jax.numpy as jnp
+import numpyro
+import numpyro.distributions as dist
+from cd_dynamax.dynamax.linear_gaussian_ssm.inference import lgssm_smoother
+from cd_dynamax.dynamax.nonlinear_gaussian_ssm.inference_ekf import (
+    extended_kalman_smoother,
+)
+from cd_dynamax.dynamax.nonlinear_gaussian_ssm.inference_ukf import (
+    UKFHyperParams,
+    unscented_kalman_smoother,
+)
+
+from dynestyx.inference.distribution_utils import _posterior_sequence_to_dists
+from dynestyx.inference.filter_configs import (
+    EKFConfig,
+    KFConfig,
+    UKFConfig,
+)
+from dynestyx.inference.integrations.cd_dynamax.discrete_filter import (
+    _lti_to_lgssm_params,
+    _prepare_inputs,
+)
+from dynestyx.inference.integrations.cd_dynamax.utils import gaussian_to_nlgssm_params
+from dynestyx.inference.smoother_configs import (
+    BaseSmootherConfig,
+    _config_to_smoother_record_kwargs,
+)
+from dynestyx.models import DynamicalModel
+from dynestyx.utils import _should_record_field
+
+
+def compute_cd_dynamax_discrete_smoother(
+    dynamics: DynamicalModel,
+    filter_config: BaseSmootherConfig,
+    *,
+    obs_times: jax.Array,
+    obs_values: jax.Array,
+    ctrl_times=None,
+    ctrl_values=None,
+):
+    """Pure-JAX cd-dynamax discrete smoother computation (no numpyro side-effects)."""
+    emissions, inputs = _prepare_inputs(
+        dynamics, obs_values, obs_times, ctrl_times, ctrl_values
+    )
+
+    if isinstance(filter_config, KFConfig):
+        params = _lti_to_lgssm_params(dynamics)
+        return lgssm_smoother(params, emissions, inputs=inputs)
+
+    params_nl = gaussian_to_nlgssm_params(dynamics)
+    if isinstance(filter_config, EKFConfig):
+        return extended_kalman_smoother(params_nl, emissions, inputs=inputs)
+    if isinstance(filter_config, UKFConfig):
+        hyperparams = UKFHyperParams(
+            alpha=filter_config.alpha,
+            beta=filter_config.beta,
+            kappa=filter_config.kappa,
+        )
+        return unscented_kalman_smoother(
+            params_nl, emissions, hyperparams=hyperparams, inputs=inputs
+        )
+    raise ValueError(
+        f"Unsupported cd-dynamax discrete config: {type(filter_config).__name__}. "
+        "Expected KFConfig, EKFConfig, or UKFConfig."
+    )
+
+
+def _add_smoother_sites(name: str, posterior, record_kwargs: dict) -> None:
+    """Add smoothed means/covariances as deterministic sites."""
+    max_elems = record_kwargs["record_max_elems"]
+    means = posterior.smoothed_means
+    covs = posterior.smoothed_covariances
+    if means is None or covs is None:
+        return
+    t1, state_dim = means.shape
+    add_mean = _should_record_field(
+        record_kwargs["record_smoothed_states_mean"], means.shape, max_elems
+    )
+    add_cov = _should_record_field(
+        record_kwargs["record_smoothed_states_cov"],
+        (t1, state_dim, state_dim),
+        max_elems,
+    )
+    add_cov_diag = _should_record_field(
+        record_kwargs["record_smoothed_states_cov_diag"], (t1, state_dim), max_elems
+    )
+    if add_mean:
+        numpyro.deterministic(f"{name}_smoothed_states_mean", means)
+    if add_cov:
+        numpyro.deterministic(f"{name}_smoothed_states_cov", covs)
+    if add_cov_diag:
+        diag_cov = jnp.diagonal(covs, axis1=1, axis2=2)
+        numpyro.deterministic(f"{name}_smoothed_states_cov_diag", diag_cov)
+
+
+def run_discrete_smoother(
+    name: str,
+    dynamics: DynamicalModel,
+    filter_config: BaseSmootherConfig,
+    *,
+    obs_times: jax.Array,
+    obs_values: jax.Array,
+    ctrl_times=None,
+    ctrl_values=None,
+    **kwargs,
+) -> list[dist.Distribution]:
+    """Run discrete-time smoother via cd-dynamax (KF, EKF, UKF)."""
+    posterior = compute_cd_dynamax_discrete_smoother(
+        dynamics,
+        filter_config,
+        obs_times=obs_times,
+        obs_values=obs_values,
+        ctrl_times=ctrl_times,
+        ctrl_values=ctrl_values,
+    )
+
+    numpyro.factor(f"{name}_marginal_log_likelihood", posterior.marginal_loglik)
+    numpyro.deterministic(f"{name}_marginal_loglik", posterior.marginal_loglik)
+    _add_smoother_sites(
+        name, posterior, _config_to_smoother_record_kwargs(filter_config)
+    )
+
+    return _posterior_sequence_to_dists(
+        posterior,
+        means_attr="smoothed_means",
+        covariances_attr="smoothed_covariances",
+        particle_mode=False,
+        missing="empty",
+    )
+
+
+__all__ = ["compute_cd_dynamax_discrete_smoother", "run_discrete_smoother"]
diff --git a/dynestyx/inference/integrations/cuthbert/discrete.py b/dynestyx/inference/integrations/cuthbert/discrete.py
index 5a5a7dce..3c657fbb 100644
--- a/dynestyx/inference/integrations/cuthbert/discrete.py
+++ b/dynestyx/inference/integrations/cuthbert/discrete.py
@@ -1,702 +1,19 @@
-import warnings
-from typing import NamedTuple, cast
+"""Compatibility wrapper for cuthbert discrete-time filtering/smoothing."""
 
-import jax
-import jax.numpy as jnp
-import numpyro
-import numpyro.distributions as dist
-from cuthbert import filter as cuthbert_filter
-from cuthbert.enkf import ensemble_kalman_filter
-from cuthbert.gaussian import kalman, taylor
-from cuthbert.smc import particle_filter
-from cuthbertlib.resampling import (
-    adaptive,
-    multinomial,
-    stop_gradient_decorator,
-    systematic,
+from dynestyx.inference.integrations.cuthbert.discrete_filter import (
+    CuthbertInputs,
+    compute_cuthbert_filter,
+    run_discrete_filter,
 )
-from numpyro.distributions import Distribution
-
-from dynestyx.inference.filter_configs import (
-    BaseFilterConfig,
-    EKFConfig,
-    EnKFConfig,
-    KFConfig,
-    PFConfig,
-    _config_to_record_kwargs,
-)
-from dynestyx.inference.integrations.utils import (
-    covariance_from_cholesky,
-    particles_to_delta_mixtures,
-    squeeze_leading_singletons,
+from dynestyx.inference.integrations.cuthbert.discrete_smoother import (
+    compute_cuthbert_smoother,
+    run_discrete_smoother,
 )
-from dynestyx.models import (
-    DynamicalModel,
-    GaussianObservation,
-    LinearGaussianObservation,
-    LinearGaussianStateEvolution,
-)
-from dynestyx.utils import _should_record_field
-
-
-class CuthbertInputs(NamedTuple):
-    """Model inputs pytree for cuthbert; leading time dim must be T+1."""
-
-    y: jax.Array  # (T+1, emission_dim)
-    u: jax.Array  # (T+1, control_dim) or (T+1, 0)
-    u_prev: jax.Array  # (T+1, control_dim) or (T+1, 0)
-    time: jax.Array  # (T+1,)
-    time_prev: jax.Array  # (T+1,)
-    is_first_step: jax.Array  # (T+1,) bool — True only at index 1
-
-
-def _extract_gaussian_chol(d: dist.Distribution, obs_dim: int) -> jax.Array:
-    """Extract a Cholesky factor of the covariance from a Gaussian distribution."""
-    if isinstance(d, dist.MultivariateNormal):
-        return jnp.asarray(d.scale_tril)
-    if isinstance(d, dist.Independent) and isinstance(d.base_dist, dist.Normal):
-        scale = jnp.atleast_1d(jnp.asarray(d.base_dist.scale))
-    elif isinstance(d, dist.Normal):
-        scale = jnp.atleast_1d(jnp.asarray(d.scale))
-    else:
-        raise TypeError(
-            "cuthbert EnKF requires Gaussian observation distributions. "
-            "Expected LinearGaussianObservation, GaussianObservation, or a "
-            "callable returning Normal, Independent(Normal), or "
-            f"MultivariateNormal; got {type(d).__name__}."
-        )
-    if scale.size == 1 and obs_dim > 1:
-        scale = jnp.full((obs_dim,), scale[0])
-    return jnp.diag(scale)
-
-
-def _check_state_independent_noise(
-    chol_R_at_x0: jax.Array, probe_dist_at_x1: dist.Distribution, obs_dim: int
-) -> None:
-    """Raise if the observation noise covariance varies with state.
-
-    cuthbert's EnKF API resolves ``chol_R`` once per step (before the ensemble
-    update), so it may depend on time/controls but NOT on the latent state.
-    Silently using a state-dependent scale would freeze it at the probe state
-    and misrepresent the noise. Detect this under concrete evaluation and raise.
-    Under JAX tracing, the comparison yields a tracer and we skip the check —
-    the constraint is documented in the filter docstring.
-    """
-    chol_R_at_x1 = _extract_gaussian_chol(probe_dist_at_x1, obs_dim)
-    try:
-        equal = bool(
-            jnp.asarray(chol_R_at_x0).shape == jnp.asarray(chol_R_at_x1).shape
-            and jnp.allclose(chol_R_at_x0, chol_R_at_x1)
-        )
-    except jax.errors.TracerBoolConversionError:
-        return
-    if not equal:
-        raise ValueError(
-            "cuthbert EnKF requires state-independent observation noise, but "
-            "the observation scale changes with the latent state (heteroscedastic "
-            "noise). The EnKF API resolves chol_R once per step before the "
-            "ensemble update, so a state-dependent scale cannot be honoured. "
-            "Either make the noise depend only on time/controls, or use a "
-            "particle filter (PFConfig)."
-        )
-
-
-def _probe_state_independent_observation_noise(
-    obs_model, *, state_dim: int, obs_dim: int
-) -> None:
-    """Probe custom observation callables for state-dependent noise."""
-    probe_u = jnp.zeros(())
-    probe_t = jnp.zeros(())
-    try:
-        probe_d0: Distribution | None = obs_model(
-            jnp.zeros((state_dim,)), probe_u, probe_t
-        )
-        probe_d1: Distribution | None = obs_model(
-            jnp.ones((state_dim,)), probe_u, probe_t
-        )
-    except Exception:
-        warnings.warn(
-            "Failed to probe observation model for state-independent noise check. Please ensure the observation model is state-independent."
-        )
-        return
-
-    if probe_d0 is not None and probe_d1 is not None:
-        chol0 = _extract_gaussian_chol(probe_d0, obs_dim)
-        _check_state_independent_noise(chol0, probe_d1, obs_dim)
-
-
-def _config_to_filter_kwargs(config: BaseFilterConfig) -> dict:
-    """Build filter_kwargs dict from config dataclass."""
-    kwargs = dict(config.extra_filter_kwargs)
-    if isinstance(config, PFConfig):
-        kwargs["n_filter_particles"] = config.n_particles
-        kwargs["ess_threshold"] = config.ess_threshold_ratio
-        kwargs["resampling_base_method"] = config.resampling_method.base_method
-        kwargs["resampling_differential_method"] = (
-            config.resampling_method.differential_method
-        )
-    elif isinstance(config, EnKFConfig):
-        kwargs["n_particles"] = config.n_particles
-        kwargs["inflation"] = (
-            config.inflation_delta if config.inflation_delta is not None else 0.0
-        )
-        if config.perturb_measurements is not None:
-            kwargs["perturbed_obs"] = config.perturb_measurements
-    return kwargs
-
-
-def _drop_cuthbert_dummy_step(states, *, obs_len: int):
-    """Drop cuthbert's leading dummy state from every time-indexed leaf."""
-    raw_len = obs_len + 1
-
-    def _drop_if_time_leaf(leaf):
-        shape = getattr(leaf, "shape", None)
-        ndim = getattr(leaf, "ndim", None)
-        if ndim is None and shape is not None:
-            ndim = len(shape)
-        if shape is not None and ndim is not None and ndim > 0 and shape[0] == raw_len:
-            return leaf[1:]
-        return leaf
-
-    return jax.tree.map(_drop_if_time_leaf, states)
-
-
-def compute_cuthbert_filter(
-    dynamics: DynamicalModel,
-    filter_config: BaseFilterConfig,
-    key: jax.Array | None = None,
-    *,
-    obs_times: jax.Array,
-    obs_values: jax.Array,
-    ctrl_times=None,
-    ctrl_values=None,
-):
-    """Pure-JAX cuthbert filter computation (no numpyro side-effects).
-
-    Returns:
-        tuple: (marginal_loglik, states) where states are aligned to obs_times.
-    """
-    filter_kwargs = _config_to_filter_kwargs(filter_config)
-
-    ys = obs_values
-    obs_len = int(ys.shape[0])
-    times = obs_times
-
-    if ctrl_values is None:
-        control_dim = dynamics.control_dim
-        ctrl_values = jnp.zeros((obs_len, control_dim), dtype=ys.dtype)
-    elif ctrl_values.shape[0] > obs_len:
-        inds = jnp.searchsorted(ctrl_times, times, side="left")
-        ctrl_values = ctrl_values[inds]
-
-    dt0 = times[1] - times[0]
-    time_prev = jnp.concatenate([times[:1] - dt0, times[:-1]], axis=0)
-    u_prev = jnp.concatenate([ctrl_values[:1], ctrl_values[:-1]], axis=0)
-
-    dummy_y = jnp.zeros_like(ys[:1])
-    dummy_u = jnp.zeros_like(ctrl_values[:1])
-    dummy_time = jnp.zeros_like(times[:1])
-
-    cuthbert_inputs = CuthbertInputs(
-        y=jnp.concatenate([dummy_y, ys], axis=0),
-        u=jnp.concatenate([dummy_u, ctrl_values], axis=0),
-        u_prev=jnp.concatenate([dummy_u, u_prev], axis=0),
-        time=jnp.concatenate([dummy_time, times], axis=0),
-        time_prev=jnp.concatenate([dummy_time, time_prev], axis=0),
-        is_first_step=jnp.arange(obs_len + 1) == 1,
-    )
-
-    if isinstance(filter_config, PFConfig):
-        if key is None:
-            raise ValueError(
-                "Particle filter requires a PRNG key: set 'crn_seed' in the filter config, "
-                "or run inside a NumPyro seeded context (e.g., with numpyro.handlers.seed)."
-            )
-        filter_obj = _cuthbert_filter_pf(dynamics, filter_kwargs)
-    elif isinstance(filter_config, EnKFConfig):
-        if key is None:
-            raise ValueError(
-                "Ensemble Kalman filter requires a PRNG key: set 'crn_seed' in the filter config, "
-                "or run inside a NumPyro seeded context (e.g., with numpyro.handlers.seed)."
-            )
-        filter_obj = _cuthbert_filter_enkf(dynamics, filter_kwargs)
-    elif isinstance(filter_config, KFConfig):
-        filter_obj = _cuthbert_filter_kalman(dynamics, filter_kwargs)
-    elif isinstance(filter_config, EKFConfig):
-        filter_obj = _cuthbert_filter_taylor_kf(dynamics, filter_kwargs)
-    else:
-        raise ValueError(
-            f"Unsupported cuthbert config: {type(filter_config).__name__}. "
-            "Expected KFConfig, EKFConfig, EnKFConfig, PFConfig."
-        )
-
-    parallel = isinstance(filter_config, KFConfig) and filter_config.associative
-    if parallel and not filter_obj.associative:
-        raise ValueError(
-            "Associative filtering was requested, but the constructed cuthbert "
-            f"filter is not associative: {type(filter_config).__name__}."
-        )
-
-    raw_states = cuthbert_filter(
-        filter_obj,
-        cuthbert_inputs,
-        parallel=cast(bool, parallel),
-        key=key,
-    )
-    marginal_loglik = raw_states.log_normalizing_constant[-1]
-    states = _drop_cuthbert_dummy_step(raw_states, obs_len=obs_len)
-    return marginal_loglik, states
-
-
-def run_discrete_filter(
-    name: str,
-    dynamics: DynamicalModel,
-    filter_config: BaseFilterConfig,
-    key: jax.Array | None = None,
-    *,
-    obs_times: jax.Array,
-    obs_values: jax.Array,
-    ctrl_times=None,
-    ctrl_values=None,
-    **kwargs,
-) -> list[dist.Distribution]:
-    """Run discrete-time filter via cuthbert (Kalman, Taylor KF, EnKF, PF).
-
-    Returns:
-        list[dist.Distribution]: Filtered state distributions at each obs time.
-    """
-    obs_len = int(obs_values.shape[0])
-    if obs_len == 0:
-        return []
-
-    marginal_loglik, states = compute_cuthbert_filter(
-        dynamics,
-        filter_config,
-        key,
-        obs_times=obs_times,
-        obs_values=obs_values,
-        ctrl_times=ctrl_times,
-        ctrl_values=ctrl_values,
-    )
-    record_kwargs = _config_to_record_kwargs(filter_config)
-
-    numpyro.factor(f"{name}_marginal_log_likelihood", marginal_loglik)
-    numpyro.deterministic(f"{name}_marginal_loglik", marginal_loglik)
-
-    if isinstance(filter_config, PFConfig):
-        _add_sites_pf(name, states, record_kwargs)
-        particles = states.particles
-        if particles.ndim == 2:
-            particles = particles[..., None]
-        return particles_to_delta_mixtures(particles, states.log_weights)
-    else:
-        _add_sites_gaussian_filter(name, states, record_kwargs)
-        mean = states.mean
-        chol_cov = states.chol_cov
-        cov = covariance_from_cholesky(chol_cov)
-        return [
-            dist.MultivariateNormal(mean[i], covariance_matrix=cov[i])
-            for i in range(mean.shape[0])
-        ]
-
-
-def _cuthbert_filter_pf(dynamics: DynamicalModel, filter_kwargs: dict | None = None):
-    if filter_kwargs is None:
-        filter_kwargs = {}
-
-    def init_sample(key, mi: CuthbertInputs):
-        return dynamics.initial_condition.sample(key)
-
-    def propagate_sample(key, x_prev, mi: CuthbertInputs):
-        def _noop(key, x_prev, mi):
-            return x_prev
-
-        def _evolve(key, x_prev, mi):
-            d = dynamics.state_evolution(x_prev, mi.u_prev, mi.time_prev, mi.time)  # type: ignore
-            return d.sample(key)  # type: ignore
-
-        return jax.lax.cond(mi.is_first_step, _noop, _evolve, key, x_prev, mi)
-
-    def log_potential(x_prev, x, mi: CuthbertInputs):
-        edist = dynamics.observation_model(x, mi.u, mi.time)
-        return jnp.asarray(edist.log_prob(mi.y)).sum()
-
-    ess_threshold = filter_kwargs.get("ess_threshold", 0.7)
-    base_method = filter_kwargs.get("resampling_base_method", "systematic")
-    if base_method == "systematic":
-        base_resampling_fn = systematic.resampling
-    elif base_method == "multinomial":
-        base_resampling_fn = multinomial.resampling
-    else:
-        raise ValueError(
-            f"Unsupported cuthbert PF base resampling method: {base_method!r}. "
-            "Expected one of: 'systematic', 'multinomial'."
-        )
-
-    differential_method = filter_kwargs.get(
-        "resampling_differential_method", "stop_gradient"
-    )
-    if differential_method == "stop_gradient":
-        base_resampling_fn = stop_gradient_decorator(base_resampling_fn)
-    elif differential_method == "straight_through":
-        pass
-    else:
-        raise ValueError(
-            "Unsupported cuthbert PF differential resampling method: "
-            f"{differential_method!r}. Expected one of: "
-            "'stop_gradient', 'straight_through'."
-        )
-
-    resampling_fn = adaptive.ess_decorator(base_resampling_fn, ess_threshold)
-
-    pf = particle_filter.build_filter(
-        init_sample=init_sample,  # type: ignore
-        propagate_sample=propagate_sample,  # type: ignore
-        log_potential=log_potential,  # type: ignore
-        n_filter_particles=int(filter_kwargs.get("n_filter_particles", 1_000)),
-        resampling_fn=resampling_fn,  # type: ignore
-    )
-    return pf
-
-
-def _cuthbert_filter_enkf(dynamics: DynamicalModel, filter_kwargs: dict | None = None):
-    if filter_kwargs is None:
-        filter_kwargs = {}
-
-    state_dim = dynamics.state_dim
-    obs_dim = dynamics.observation_dim
-
-    obs_model = dynamics.observation_model
-    if not isinstance(obs_model, (LinearGaussianObservation, GaussianObservation)):
-        _probe_state_independent_observation_noise(
-            obs_model, state_dim=state_dim, obs_dim=obs_dim
-        )
-
-    def init_sample(key, mi: CuthbertInputs):
-        return jnp.atleast_1d(jnp.asarray(dynamics.initial_condition.sample(key)))
-
-    def get_dynamics(mi: CuthbertInputs):
-        def dynamics_fn(x, key):
-            def _noop(key):
-                return x
-
-            def _evolve(key):
-                d = dynamics.state_evolution(x, mi.u_prev, mi.time_prev, mi.time)  # type: ignore
-                return jnp.atleast_1d(jnp.asarray(d.sample(key)))  # type: ignore
-
-            return jax.lax.cond(mi.is_first_step, _noop, _evolve, key)
-
-        return dynamics_fn
-
-    def get_observations(mi: CuthbertInputs):
-        obs_model = dynamics.observation_model
-        y = jnp.atleast_1d(jnp.asarray(mi.y))
-
-        if isinstance(obs_model, LinearGaussianObservation):
-            H = jnp.asarray(obs_model.H)
-            chol_R = jnp.linalg.cholesky(jnp.atleast_2d(jnp.asarray(obs_model.R)))
-            bias = (
-                jnp.zeros((obs_dim,), dtype=y.dtype)
-                if obs_model.bias is None
-                else jnp.atleast_1d(jnp.asarray(obs_model.bias))
-            )
-            D = None if obs_model.D is None else jnp.asarray(obs_model.D)
-
-            def observation_fn(x):
-                loc = H @ x + bias
-                if D is not None:
-                    loc = loc + D @ jnp.atleast_1d(jnp.asarray(mi.u))
-                return jnp.atleast_1d(jnp.asarray(loc))
-
-            return observation_fn, chol_R, y
-        elif isinstance(obs_model, GaussianObservation):
-            chol_R = jnp.linalg.cholesky(jnp.atleast_2d(jnp.asarray(obs_model.R)))
-
-            def observation_fn(x):
-                return jnp.atleast_1d(jnp.asarray(obs_model.h(x, mi.u, mi.time)))
-
-            return observation_fn, chol_R, y
-        else:
-            probe_x0 = jnp.zeros((state_dim,), dtype=y.dtype)
-            probe_x1 = jnp.ones((state_dim,), dtype=y.dtype)
-            probe_dist = obs_model(probe_x0, mi.u, mi.time)
-            chol_R = _extract_gaussian_chol(probe_dist, obs_dim)
-            _check_state_independent_noise(
-                chol_R, obs_model(probe_x1, mi.u, mi.time), obs_dim
-            )
-
-            def observation_fn(x):
-                edist = obs_model(x, mi.u, mi.time)
-                if not (
-                    isinstance(edist, (dist.MultivariateNormal, dist.Normal))
-                    or (
-                        isinstance(edist, dist.Independent)
-                        and isinstance(edist.base_dist, dist.Normal)
-                    )
-                ):
-                    raise TypeError(
-                        "cuthbert EnKF observation callable must keep returning "
-                        "Gaussian distributions; got "
-                        f"{type(edist).__name__}."
-                    )
-                return jnp.atleast_1d(jnp.asarray(edist.mean))
-
-            return observation_fn, chol_R, y
-
-    return ensemble_kalman_filter.build_filter(
-        init_sample=init_sample,  # type: ignore
-        get_dynamics=get_dynamics,  # type: ignore
-        get_observations=get_observations,  # type: ignore
-        n_particles=int(filter_kwargs.get("n_particles", 30)),
-        inflation=float(filter_kwargs.get("inflation", 0.0)),
-        perturbed_obs=bool(filter_kwargs.get("perturbed_obs", True)),
-    )
-
-
-def _cuthbert_filter_kalman(
-    dynamics: DynamicalModel, filter_kwargs: dict | None = None
-):
-    if filter_kwargs is None:
-        filter_kwargs = {}
-
-    if not (
-        isinstance(dynamics.state_evolution, LinearGaussianStateEvolution)
-        and isinstance(dynamics.observation_model, LinearGaussianObservation)
-        and isinstance(dynamics.initial_condition, dist.MultivariateNormal)
-    ):
-        raise TypeError(
-            "cuthbert Kalman filter expects a DynamicalModel with "
-            "LinearGaussianStateEvolution and LinearGaussianObservation, and "
-            "initial_condition as MultivariateNormal."
-        )
-
-    evo = dynamics.state_evolution
-    obs = dynamics.observation_model
-    ic = dynamics.initial_condition
-
-    state_dim = dynamics.state_dim
-    obs_dim = dynamics.observation_dim
-
-    m0 = jnp.reshape(
-        jnp.atleast_1d(squeeze_leading_singletons(ic.loc, 1)), (state_dim,)
-    )
-    chol_P0 = jnp.linalg.cholesky(squeeze_leading_singletons(ic.covariance_matrix, 2))
-
-    A = jnp.asarray(evo.A)
-    chol_Q = jnp.linalg.cholesky(jnp.asarray(evo.cov))
-
-    H = jnp.asarray(obs.H)
-    chol_R = jnp.linalg.cholesky(jnp.asarray(obs.R))
-
-    evo_bias = (
-        jnp.zeros((state_dim,), dtype=m0.dtype)
-        if evo.bias is None
-        else jnp.reshape(jnp.atleast_1d(jnp.asarray(evo.bias)), (state_dim,))
-    )
-    obs_bias = (
-        jnp.zeros((obs_dim,), dtype=m0.dtype)
-        if obs.bias is None
-        else jnp.reshape(jnp.atleast_1d(jnp.asarray(obs.bias)), (obs_dim,))
-    )
-
-    B = None if evo.B is None else jnp.asarray(evo.B)
-    D = None if obs.D is None else jnp.asarray(obs.D)
-
-    def get_init_params(mi: CuthbertInputs):
-        return m0, chol_P0
-
-    def get_dynamics_params(mi: CuthbertInputs):
-        def _noop(mi):
-            return (
-                jnp.eye(state_dim, dtype=m0.dtype),
-                jnp.zeros((state_dim,), dtype=m0.dtype),
-                jnp.zeros((state_dim, state_dim), dtype=m0.dtype),
-            )
-
-        def _evolve(mi):
-            c = evo_bias
-            if B is not None:
-                c = c + B @ jnp.atleast_1d(jnp.asarray(mi.u_prev))
-            return A, c, chol_Q
-
-        return jax.lax.cond(mi.is_first_step, _noop, _evolve, mi)
-
-    def get_observation_params(mi: CuthbertInputs):
-        d = obs_bias
-        if D is not None:
-            d = d + D @ jnp.atleast_1d(jnp.asarray(mi.u))
-        y = jnp.atleast_1d(jnp.asarray(mi.y))
-        return H, d, chol_R, y
-
-    return kalman.build_filter(
-        get_init_params,  # type: ignore
-        get_dynamics_params,  # type: ignore
-        get_observation_params,  # type: ignore
-    )
-
-
-def _cuthbert_filter_taylor_kf(
-    dynamics: DynamicalModel, filter_kwargs: dict | None = None
-):
-    if filter_kwargs is None:
-        filter_kwargs = {}
-
-    rtol = filter_kwargs.get("rtol", None)
-
-    def get_init_log_density(mi: CuthbertInputs):
-        dist0 = dynamics.initial_condition
-        state_dim = dynamics.state_dim
-
-        def init_log_density(x):
-            return jnp.asarray(dist0.log_prob(x)).sum()
-
-        x0_lin = jnp.reshape(jnp.atleast_1d(jnp.asarray(dist0.mean)), (state_dim,))
-        return init_log_density, x0_lin
-
-    def get_dynamics_log_density(
-        state: taylor.LinearizedKalmanFilterState, mi: CuthbertInputs
-    ):
-        def dynamics_log_density(x_prev, x):
-            normal_logp = jnp.asarray(
-                dynamics.state_evolution(
-                    x_prev, mi.u_prev, mi.time_prev, mi.time
-                ).log_prob(x)
-            ).sum()
-            # Identity dynamics with near-zero noise for the noop first step.
-            noop_logp = -1e10 * jnp.sum((x - x_prev) ** 2)
-            return jnp.where(mi.is_first_step, noop_logp, normal_logp)
-
-        x_prev_lin = jnp.atleast_1d(jnp.asarray(state.mean))
-
-        dist_at_lin = dynamics.state_evolution(  # type: ignore
-            x_prev_lin, mi.u_prev, mi.time_prev, mi.time
-        )
-        try:
-            x_lin = jnp.atleast_1d(jnp.asarray(dist_at_lin.mean))  # type: ignore
-        except (AttributeError, NotImplementedError) as exc:
-            raise ValueError(
-                "dist_at_lin.mean is not available. Linearized Kalman filter requires a mean-able distribution."
-            ) from exc
-
-        # On the first step, use identity linearization (x_lin = x_prev_lin).
-        x_lin = jnp.where(mi.is_first_step, x_prev_lin, x_lin)
-
-        return dynamics_log_density, x_prev_lin, x_lin
-
-    def get_observation_func(
-        state: taylor.LinearizedKalmanFilterState, mi: CuthbertInputs
-    ):
-        def log_potential(x):
-            edist = dynamics.observation_model(x, mi.u, mi.time)
-            return jnp.asarray(edist.log_prob(mi.y)).sum()
-
-        return log_potential, jnp.atleast_1d(jnp.asarray(state.mean))
-
-    kf = taylor.build_filter(
-        get_init_log_density,  # type: ignore
-        get_dynamics_log_density,  # type: ignore
-        get_observation_func,  # type: ignore
-        associative=False,
-        rtol=rtol,
-        ignore_nan_dims=True,
-    )
-    return kf
-
-
-def _add_sites_pf(
-    name: str, states: particle_filter.ParticleFilterState, record_kwargs: dict
-):
-    log_weights = states.log_weights
-    particles = states.particles
-    if particles.ndim == 2:
-        particles = particles[..., None]
-    max_elems = record_kwargs["record_max_elems"]
-    t_len, n_particles, state_dim = particles.shape
-
-    add_particles = _should_record_field(
-        record_kwargs["record_filtered_particles"], particles.shape, max_elems
-    )
-    add_log_weights = _should_record_field(
-        record_kwargs["record_filtered_log_weights"], log_weights.shape, max_elems
-    )
-    add_mean = _should_record_field(
-        record_kwargs["record_filtered_states_mean"], (t_len, state_dim), max_elems
-    )
-    add_filtered_states_cov = _should_record_field(
-        record_kwargs["record_filtered_states_cov"],
-        (t_len, state_dim, state_dim),
-        max_elems,
-    )
-    add_filtered_states_cov_diag = _should_record_field(
-        record_kwargs["record_filtered_states_cov_diag"], (t_len, state_dim), max_elems
-    )
-
-    need_filtered_means = (
-        add_mean or add_filtered_states_cov or add_filtered_states_cov_diag
-    )
-
-    if need_filtered_means:
-        w = jax.nn.softmax(log_weights, axis=1)[..., None]  # (T, n_particles, 1)
-        filtered_means = jnp.sum(particles * w, axis=1)  # (T, state_dim)
-
-    if add_filtered_states_cov or add_filtered_states_cov_diag:
-        second_mom = jnp.einsum(
-            "...tnj,...tnk,...tn->...tjk", particles, particles, w.squeeze(-1)
-        )
-        filtered_covariances = second_mom - jnp.einsum(
-            "...tj,...tk->...tjk", filtered_means, filtered_means
-        )
-
-    if add_particles:
-        numpyro.deterministic(f"{name}_filtered_particles", particles)
-    if add_log_weights:
-        numpyro.deterministic(f"{name}_filtered_log_weights", log_weights)
-    if add_mean:
-        numpyro.deterministic(f"{name}_filtered_states_mean", filtered_means)
-    if add_filtered_states_cov:
-        numpyro.deterministic(f"{name}_filtered_states_cov", filtered_covariances)
-    if add_filtered_states_cov_diag:
-        diag_cov = jnp.diagonal(filtered_covariances, axis1=1, axis2=2)
-        numpyro.deterministic(f"{name}_filtered_states_cov_diag", diag_cov)
-
-
-def _add_sites_gaussian_filter(
-    name: str,
-    states: taylor.LinearizedKalmanFilterState | ensemble_kalman_filter.EnKFState,
-    record_kwargs: dict,
-):
-    max_elems = record_kwargs["record_max_elems"]
-    mean = states.mean
-    chol_cov = states.chol_cov
-    t_len, state_dim, _ = chol_cov.shape
-
-    add_mean = _should_record_field(
-        record_kwargs["record_filtered_states_mean"], mean.shape, max_elems
-    )
-    add_chol_cov = _should_record_field(
-        record_kwargs["record_filtered_states_chol_cov"],
-        chol_cov.shape,
-        max_elems,
-    )
-    add_filtered_states_cov = _should_record_field(
-        record_kwargs["record_filtered_states_cov"],
-        (t_len, state_dim, state_dim),
-        max_elems,
-    )
-    add_filtered_states_cov_diag = _should_record_field(
-        record_kwargs["record_filtered_states_cov_diag"], (t_len, state_dim), max_elems
-    )
-
-    if add_mean:
-        numpyro.deterministic(f"{name}_filtered_states_mean", mean)
-    if add_chol_cov:
-        numpyro.deterministic(f"{name}_filtered_states_chol_cov", chol_cov)
-
-    if add_filtered_states_cov or add_filtered_states_cov_diag:
-        filtered_cov = covariance_from_cholesky(chol_cov)
 
-    if add_filtered_states_cov:
-        numpyro.deterministic(f"{name}_filtered_states_cov", filtered_cov)
-    if add_filtered_states_cov_diag:
-        diag_cov = jnp.diagonal(filtered_cov, axis1=1, axis2=2)
-        numpyro.deterministic(f"{name}_filtered_states_cov_diag", diag_cov)
+__all__ = [
+    "CuthbertInputs",
+    "compute_cuthbert_filter",
+    "run_discrete_filter",
+    "compute_cuthbert_smoother",
+    "run_discrete_smoother",
+]
diff --git a/dynestyx/inference/integrations/cuthbert/discrete_filter.py b/dynestyx/inference/integrations/cuthbert/discrete_filter.py
new file mode 100644
index 00000000..8f7f7cd1
--- /dev/null
+++ b/dynestyx/inference/integrations/cuthbert/discrete_filter.py
@@ -0,0 +1,694 @@
+import warnings
+from typing import NamedTuple, cast
+
+import jax
+import jax.numpy as jnp
+import numpyro
+import numpyro.distributions as dist
+from cuthbert import filter as cuthbert_filter
+from cuthbert.enkf import ensemble_kalman_filter
+from cuthbert.gaussian import kalman, taylor
+from cuthbert.smc import particle_filter
+from cuthbertlib.resampling import (
+    adaptive,
+    multinomial,
+    stop_gradient_decorator,
+    systematic,
+)
+from numpyro.distributions import Distribution
+
+from dynestyx.inference.distribution_utils import _cholesky_state_sequence_to_dists
+from dynestyx.inference.filter_configs import (
+    BaseFilterConfig,
+    EKFConfig,
+    EnKFConfig,
+    KFConfig,
+    PFConfig,
+    _config_to_record_kwargs,
+)
+from dynestyx.inference.integrations.utils import (
+    covariance_from_cholesky,
+    squeeze_leading_singletons,
+)
+from dynestyx.models import (
+    DynamicalModel,
+    GaussianObservation,
+    LinearGaussianObservation,
+    LinearGaussianStateEvolution,
+)
+from dynestyx.utils import _should_record_field
+
+
+class CuthbertInputs(NamedTuple):
+    """Model inputs pytree for cuthbert; leading time dim must be T+1."""
+
+    y: jax.Array  # (T+1, emission_dim)
+    u: jax.Array  # (T+1, control_dim) or (T+1, 0)
+    u_prev: jax.Array  # (T+1, control_dim) or (T+1, 0)
+    time: jax.Array  # (T+1,)
+    time_prev: jax.Array  # (T+1,)
+    is_first_step: jax.Array  # (T+1,) bool — True only at index 1
+
+
+def _extract_gaussian_chol(d: dist.Distribution, obs_dim: int) -> jax.Array:
+    """Extract a Cholesky factor of the covariance from a Gaussian distribution."""
+    if isinstance(d, dist.MultivariateNormal):
+        return jnp.asarray(d.scale_tril)
+    if isinstance(d, dist.Independent) and isinstance(d.base_dist, dist.Normal):
+        scale = jnp.atleast_1d(jnp.asarray(d.base_dist.scale))
+    elif isinstance(d, dist.Normal):
+        scale = jnp.atleast_1d(jnp.asarray(d.scale))
+    else:
+        raise TypeError(
+            "cuthbert EnKF requires Gaussian observation distributions. "
+            "Expected LinearGaussianObservation, GaussianObservation, or a "
+            "callable returning Normal, Independent(Normal), or "
+            f"MultivariateNormal; got {type(d).__name__}."
+        )
+    if scale.size == 1 and obs_dim > 1:
+        scale = jnp.full((obs_dim,), scale[0])
+    return jnp.diag(scale)
+
+
+def _check_state_independent_noise(
+    chol_R_at_x0: jax.Array, probe_dist_at_x1: dist.Distribution, obs_dim: int
+) -> None:
+    """Raise if the observation noise covariance varies with state."""
+    chol_R_at_x1 = _extract_gaussian_chol(probe_dist_at_x1, obs_dim)
+    try:
+        equal = bool(
+            jnp.asarray(chol_R_at_x0).shape == jnp.asarray(chol_R_at_x1).shape
+            and jnp.allclose(chol_R_at_x0, chol_R_at_x1)
+        )
+    except jax.errors.TracerBoolConversionError:
+        return
+    if not equal:
+        raise ValueError(
+            "cuthbert EnKF requires state-independent observation noise, but "
+            "the observation scale changes with the latent state (heteroscedastic "
+            "noise). The EnKF API resolves chol_R once per step before the "
+            "ensemble update, so a state-dependent scale cannot be honoured. "
+            "Either make the noise depend only on time/controls, or use a "
+            "particle filter (PFConfig)."
+        )
+
+
+def _probe_state_independent_observation_noise(
+    obs_model, *, state_dim: int, obs_dim: int
+) -> None:
+    """Probe custom observation callables for state-dependent noise."""
+    probe_u = jnp.zeros(())
+    probe_t = jnp.zeros(())
+    try:
+        probe_d0: Distribution | None = obs_model(
+            jnp.zeros((state_dim,)), probe_u, probe_t
+        )
+        probe_d1: Distribution | None = obs_model(
+            jnp.ones((state_dim,)), probe_u, probe_t
+        )
+    except Exception:
+        warnings.warn(
+            "Failed to probe observation model for state-independent noise check. "
+            "Please ensure the observation model is state-independent."
+        )
+        return
+
+    if probe_d0 is not None and probe_d1 is not None:
+        chol0 = _extract_gaussian_chol(probe_d0, obs_dim)
+        _check_state_independent_noise(chol0, probe_d1, obs_dim)
+
+
+def _config_to_filter_kwargs(config: BaseFilterConfig) -> dict:
+    """Build filter_kwargs dict from config dataclass."""
+    kwargs = dict(config.extra_filter_kwargs)
+    if isinstance(config, PFConfig):
+        kwargs["n_filter_particles"] = config.n_particles
+        kwargs["ess_threshold"] = config.ess_threshold_ratio
+        kwargs["resampling_base_method"] = config.resampling_method.base_method
+        kwargs["resampling_differential_method"] = (
+            config.resampling_method.differential_method
+        )
+    elif isinstance(config, EnKFConfig):
+        kwargs["n_particles"] = config.n_particles
+        kwargs["inflation"] = (
+            config.inflation_delta if config.inflation_delta is not None else 0.0
+        )
+        if config.perturb_measurements is not None:
+            kwargs["perturbed_obs"] = config.perturb_measurements
+    return kwargs
+
+
+def _drop_cuthbert_dummy_step(states, *, obs_len: int):
+    """Drop cuthbert's leading dummy state from every time-indexed leaf."""
+    raw_len = obs_len + 1
+
+    def _drop_if_time_leaf(leaf):
+        shape = getattr(leaf, "shape", None)
+        ndim = getattr(leaf, "ndim", None)
+        if ndim is None and shape is not None:
+            ndim = len(shape)
+        if shape is not None and ndim is not None and ndim > 0 and shape[0] == raw_len:
+            return leaf[1:]
+        return leaf
+
+    return jax.tree.map(_drop_if_time_leaf, states)
+
+
+def compute_cuthbert_filter(
+    dynamics: DynamicalModel,
+    filter_config: BaseFilterConfig,
+    key: jax.Array | None = None,
+    *,
+    obs_times: jax.Array,
+    obs_values: jax.Array,
+    ctrl_times=None,
+    ctrl_values=None,
+    align_to_observations: bool = True,
+):
+    """Pure-JAX cuthbert filter computation (no numpyro side-effects).
+
+    Returns:
+        tuple: (marginal_loglik, states). By default states are aligned to
+        obs_times; pass align_to_observations=False for raw cuthbert T+1 states.
+    """
+    filter_kwargs = _config_to_filter_kwargs(filter_config)
+
+    ys = obs_values
+    obs_len = int(ys.shape[0])
+    times = obs_times
+
+    if ctrl_values is None:
+        control_dim = dynamics.control_dim
+        ctrl_values = jnp.zeros((obs_len, control_dim), dtype=ys.dtype)
+    elif ctrl_values.shape[0] > obs_len:
+        inds = jnp.searchsorted(ctrl_times, times, side="left")
+        ctrl_values = ctrl_values[inds]
+
+    dt0 = times[1] - times[0]
+    time_prev = jnp.concatenate([times[:1] - dt0, times[:-1]], axis=0)
+    u_prev = jnp.concatenate([ctrl_values[:1], ctrl_values[:-1]], axis=0)
+
+    dummy_y = jnp.zeros_like(ys[:1])
+    dummy_u = jnp.zeros_like(ctrl_values[:1])
+    dummy_time = jnp.zeros_like(times[:1])
+
+    cuthbert_inputs = CuthbertInputs(
+        y=jnp.concatenate([dummy_y, ys], axis=0),
+        u=jnp.concatenate([dummy_u, ctrl_values], axis=0),
+        u_prev=jnp.concatenate([dummy_u, u_prev], axis=0),
+        time=jnp.concatenate([dummy_time, times], axis=0),
+        time_prev=jnp.concatenate([dummy_time, time_prev], axis=0),
+        is_first_step=jnp.arange(obs_len + 1) == 1,
+    )
+
+    if isinstance(filter_config, PFConfig):
+        if key is None:
+            raise ValueError(
+                "Particle filter requires a PRNG key: set 'crn_seed' in the filter config, "
+                "or run inside a NumPyro seeded context (e.g., with numpyro.handlers.seed)."
+            )
+        filter_obj = _cuthbert_filter_pf(dynamics, filter_kwargs)
+    elif isinstance(filter_config, EnKFConfig):
+        if key is None:
+            raise ValueError(
+                "Ensemble Kalman filter requires a PRNG key: set 'crn_seed' in the filter config, "
+                "or run inside a NumPyro seeded context (e.g., with numpyro.handlers.seed)."
+            )
+        filter_obj = _cuthbert_filter_enkf(dynamics, filter_kwargs)
+    elif isinstance(filter_config, KFConfig):
+        filter_obj = _cuthbert_filter_kalman(dynamics, filter_kwargs)
+    elif isinstance(filter_config, EKFConfig):
+        filter_obj = _cuthbert_filter_taylor_kf(dynamics, filter_kwargs)
+    else:
+        raise ValueError(
+            f"Unsupported cuthbert config: {type(filter_config).__name__}. "
+            "Expected KFConfig, EKFConfig, EnKFConfig, PFConfig."
+        )
+
+    parallel = isinstance(filter_config, KFConfig) and filter_config.associative
+    if parallel and not filter_obj.associative:
+        raise ValueError(
+            "Associative filtering was requested, but the constructed cuthbert "
+            f"filter is not associative: {type(filter_config).__name__}."
+        )
+
+    raw_states = cuthbert_filter(
+        filter_obj,
+        cuthbert_inputs,
+        parallel=cast(bool, parallel),
+        key=key,
+    )
+    marginal_loglik = raw_states.log_normalizing_constant[-1]
+    states = (
+        _drop_cuthbert_dummy_step(raw_states, obs_len=obs_len)
+        if align_to_observations
+        else raw_states
+    )
+    return marginal_loglik, states
+
+
+def run_discrete_filter(
+    name: str,
+    dynamics: DynamicalModel,
+    filter_config: BaseFilterConfig,
+    key: jax.Array | None = None,
+    *,
+    obs_times: jax.Array,
+    obs_values: jax.Array,
+    ctrl_times=None,
+    ctrl_values=None,
+    **kwargs,
+) -> list[dist.Distribution]:
+    """Run discrete-time filter via cuthbert (Kalman, Taylor KF, particle filter).
+
+    Returns:
+        list[dist.Distribution]: Filtered state distributions at each obs time.
+    """
+    obs_len = int(obs_values.shape[0])
+    if obs_len == 0:
+        return []
+
+    marginal_loglik, states = compute_cuthbert_filter(
+        dynamics,
+        filter_config,
+        key,
+        obs_times=obs_times,
+        obs_values=obs_values,
+        ctrl_times=ctrl_times,
+        ctrl_values=ctrl_values,
+    )
+    record_kwargs = _config_to_record_kwargs(filter_config)
+
+    numpyro.factor(f"{name}_marginal_log_likelihood", marginal_loglik)
+    numpyro.deterministic(f"{name}_marginal_loglik", marginal_loglik)
+
+    if isinstance(filter_config, PFConfig):
+        _add_sites_pf(name, states, record_kwargs)
+    else:
+        _add_sites_gaussian_filter(name, states, record_kwargs)
+    return _cholesky_state_sequence_to_dists(
+        states,
+        particle_mode=isinstance(filter_config, PFConfig),
+    )
+
+
+def _cuthbert_filter_pf(dynamics: DynamicalModel, filter_kwargs: dict | None = None):
+    if filter_kwargs is None:
+        filter_kwargs = {}
+
+    def init_sample(key, mi: CuthbertInputs):
+        return dynamics.initial_condition.sample(key)
+
+    def propagate_sample(key, x_prev, mi: CuthbertInputs):
+        def _noop(key, x_prev, mi):
+            return x_prev
+
+        def _evolve(key, x_prev, mi):
+            d = dynamics.state_evolution(x_prev, mi.u_prev, mi.time_prev, mi.time)  # type: ignore
+            return d.sample(key)  # type: ignore
+
+        return jax.lax.cond(mi.is_first_step, _noop, _evolve, key, x_prev, mi)
+
+    def log_potential(x_prev, x, mi: CuthbertInputs):
+        edist = dynamics.observation_model(x, mi.u, mi.time)
+        return jnp.asarray(edist.log_prob(mi.y)).sum()
+
+    ess_threshold = filter_kwargs.get("ess_threshold", 0.7)
+    base_method = filter_kwargs.get("resampling_base_method", "systematic")
+    if base_method == "systematic":
+        base_resampling_fn = systematic.resampling
+    elif base_method == "multinomial":
+        base_resampling_fn = multinomial.resampling
+    else:
+        raise ValueError(
+            f"Unsupported cuthbert PF base resampling method: {base_method!r}. "
+            "Expected one of: 'systematic', 'multinomial'."
+        )
+
+    differential_method = filter_kwargs.get(
+        "resampling_differential_method", "stop_gradient"
+    )
+    if differential_method == "stop_gradient":
+        base_resampling_fn = stop_gradient_decorator(base_resampling_fn)
+    elif differential_method == "straight_through":
+        pass
+    else:
+        raise ValueError(
+            "Unsupported cuthbert PF differential resampling method: "
+            f"{differential_method!r}. Expected one of: "
+            "'stop_gradient', 'straight_through'."
+        )
+
+    resampling_fn = adaptive.ess_decorator(base_resampling_fn, ess_threshold)
+
+    pf = particle_filter.build_filter(
+        init_sample=init_sample,  # type: ignore
+        propagate_sample=propagate_sample,  # type: ignore
+        log_potential=log_potential,  # type: ignore
+        n_filter_particles=int(filter_kwargs.get("n_filter_particles", 1_000)),
+        resampling_fn=resampling_fn,  # type: ignore
+    )
+    return pf
+
+
+def _cuthbert_filter_enkf(dynamics: DynamicalModel, filter_kwargs: dict | None = None):
+    if filter_kwargs is None:
+        filter_kwargs = {}
+
+    state_dim = dynamics.state_dim
+    obs_dim = dynamics.observation_dim
+
+    obs_model = dynamics.observation_model
+    if not isinstance(obs_model, (LinearGaussianObservation, GaussianObservation)):
+        _probe_state_independent_observation_noise(
+            obs_model, state_dim=state_dim, obs_dim=obs_dim
+        )
+
+    def init_sample(key, mi: CuthbertInputs):
+        return jnp.atleast_1d(jnp.asarray(dynamics.initial_condition.sample(key)))
+
+    def get_dynamics(mi: CuthbertInputs):
+        def dynamics_fn(x, key):
+            def _noop(key):
+                return x
+
+            def _evolve(key):
+                d = dynamics.state_evolution(x, mi.u_prev, mi.time_prev, mi.time)  # type: ignore
+                return jnp.atleast_1d(jnp.asarray(d.sample(key)))  # type: ignore
+
+            return jax.lax.cond(mi.is_first_step, _noop, _evolve, key)
+
+        return dynamics_fn
+
+    def get_observations(mi: CuthbertInputs):
+        obs_model = dynamics.observation_model
+        y = jnp.atleast_1d(jnp.asarray(mi.y))
+
+        if isinstance(obs_model, LinearGaussianObservation):
+            H = jnp.asarray(obs_model.H)
+            chol_R = jnp.linalg.cholesky(jnp.atleast_2d(jnp.asarray(obs_model.R)))
+            bias = (
+                jnp.zeros((obs_dim,), dtype=y.dtype)
+                if obs_model.bias is None
+                else jnp.atleast_1d(jnp.asarray(obs_model.bias))
+            )
+            D = None if obs_model.D is None else jnp.asarray(obs_model.D)
+
+            def observation_fn(x):
+                loc = H @ x + bias
+                if D is not None:
+                    loc = loc + D @ jnp.atleast_1d(jnp.asarray(mi.u))
+                return jnp.atleast_1d(jnp.asarray(loc))
+
+            return observation_fn, chol_R, y
+        elif isinstance(obs_model, GaussianObservation):
+            chol_R = jnp.linalg.cholesky(jnp.atleast_2d(jnp.asarray(obs_model.R)))
+
+            def observation_fn(x):
+                return jnp.atleast_1d(jnp.asarray(obs_model.h(x, mi.u, mi.time)))
+
+            return observation_fn, chol_R, y
+        else:
+            probe_x0 = jnp.zeros((state_dim,), dtype=y.dtype)
+            probe_x1 = jnp.ones((state_dim,), dtype=y.dtype)
+            probe_dist = obs_model(probe_x0, mi.u, mi.time)
+            chol_R = _extract_gaussian_chol(probe_dist, obs_dim)
+            _check_state_independent_noise(
+                chol_R, obs_model(probe_x1, mi.u, mi.time), obs_dim
+            )
+
+            def observation_fn(x):
+                edist = obs_model(x, mi.u, mi.time)
+                if not (
+                    isinstance(edist, (dist.MultivariateNormal, dist.Normal))
+                    or (
+                        isinstance(edist, dist.Independent)
+                        and isinstance(edist.base_dist, dist.Normal)
+                    )
+                ):
+                    raise TypeError(
+                        "cuthbert EnKF observation callable must keep returning "
+                        "Gaussian distributions; got "
+                        f"{type(edist).__name__}."
+                    )
+                return jnp.atleast_1d(jnp.asarray(edist.mean))
+
+            return observation_fn, chol_R, y
+
+    return ensemble_kalman_filter.build_filter(
+        init_sample=init_sample,  # type: ignore
+        get_dynamics=get_dynamics,  # type: ignore
+        get_observations=get_observations,  # type: ignore
+        n_particles=int(filter_kwargs.get("n_particles", 30)),
+        inflation=float(filter_kwargs.get("inflation", 0.0)),
+        perturbed_obs=bool(filter_kwargs.get("perturbed_obs", True)),
+    )
+
+
+def _cuthbert_filter_kalman(
+    dynamics: DynamicalModel, filter_kwargs: dict | None = None
+):
+    if filter_kwargs is None:
+        filter_kwargs = {}
+
+    if not (
+        isinstance(dynamics.state_evolution, LinearGaussianStateEvolution)
+        and isinstance(dynamics.observation_model, LinearGaussianObservation)
+        and isinstance(dynamics.initial_condition, dist.MultivariateNormal)
+    ):
+        raise TypeError(
+            "cuthbert Kalman filter expects a DynamicalModel with "
+            "LinearGaussianStateEvolution and LinearGaussianObservation, and "
+            "initial_condition as MultivariateNormal."
+        )
+
+    evo = dynamics.state_evolution
+    obs = dynamics.observation_model
+    ic = dynamics.initial_condition
+
+    state_dim = dynamics.state_dim
+    obs_dim = dynamics.observation_dim
+
+    m0 = jnp.reshape(
+        jnp.atleast_1d(squeeze_leading_singletons(ic.loc, 1)), (state_dim,)
+    )
+    chol_P0 = jnp.linalg.cholesky(squeeze_leading_singletons(ic.covariance_matrix, 2))
+
+    A = jnp.asarray(evo.A)
+    chol_Q = jnp.linalg.cholesky(jnp.asarray(evo.cov))
+
+    H = jnp.asarray(obs.H)
+    chol_R = jnp.linalg.cholesky(jnp.asarray(obs.R))
+
+    evo_bias = (
+        jnp.zeros((state_dim,), dtype=m0.dtype)
+        if evo.bias is None
+        else jnp.reshape(jnp.atleast_1d(jnp.asarray(evo.bias)), (state_dim,))
+    )
+    obs_bias = (
+        jnp.zeros((obs_dim,), dtype=m0.dtype)
+        if obs.bias is None
+        else jnp.reshape(jnp.atleast_1d(jnp.asarray(obs.bias)), (obs_dim,))
+    )
+
+    B = None if evo.B is None else jnp.asarray(evo.B)
+    D = None if obs.D is None else jnp.asarray(obs.D)
+
+    def get_init_params(mi: CuthbertInputs):
+        return m0, chol_P0
+
+    def get_dynamics_params(mi: CuthbertInputs):
+        def _noop(mi):
+            return (
+                jnp.eye(state_dim, dtype=m0.dtype),
+                jnp.zeros((state_dim,), dtype=m0.dtype),
+                jnp.zeros((state_dim, state_dim), dtype=m0.dtype),
+            )
+
+        def _evolve(mi):
+            c = evo_bias
+            if B is not None:
+                c = c + B @ jnp.atleast_1d(jnp.asarray(mi.u_prev))
+            return A, c, chol_Q
+
+        return jax.lax.cond(mi.is_first_step, _noop, _evolve, mi)
+
+    def get_observation_params(mi: CuthbertInputs):
+        d = obs_bias
+        if D is not None:
+            d = d + D @ jnp.atleast_1d(jnp.asarray(mi.u))
+        y = jnp.atleast_1d(jnp.asarray(mi.y))
+        return H, d, chol_R, y
+
+    return kalman.build_filter(
+        get_init_params,  # type: ignore
+        get_dynamics_params,  # type: ignore
+        get_observation_params,  # type: ignore
+    )
+
+
+def _cuthbert_filter_taylor_kf(
+    dynamics: DynamicalModel, filter_kwargs: dict | None = None
+):
+    if filter_kwargs is None:
+        filter_kwargs = {}
+
+    rtol = filter_kwargs.get("rtol", None)
+
+    def get_init_log_density(mi: CuthbertInputs):
+        dist0 = dynamics.initial_condition
+        state_dim = dynamics.state_dim
+
+        def init_log_density(x):
+            return jnp.asarray(dist0.log_prob(x)).sum()
+
+        x0_lin = jnp.reshape(jnp.atleast_1d(jnp.asarray(dist0.mean)), (state_dim,))
+        return init_log_density, x0_lin
+
+    def get_dynamics_log_density(
+        state: taylor.LinearizedKalmanFilterState, mi: CuthbertInputs
+    ):
+        def dynamics_log_density(x_prev, x):
+            normal_logp = jnp.asarray(
+                dynamics.state_evolution(
+                    x_prev, mi.u_prev, mi.time_prev, mi.time
+                ).log_prob(x)
+            ).sum()
+            # Identity dynamics with near-zero noise for the noop first step.
+            noop_logp = -1e10 * jnp.sum((x - x_prev) ** 2)
+            return jnp.where(mi.is_first_step, noop_logp, normal_logp)
+
+        x_prev_lin = jnp.atleast_1d(jnp.asarray(state.mean))
+
+        dist_at_lin = dynamics.state_evolution(  # type: ignore
+            x_prev_lin, mi.u_prev, mi.time_prev, mi.time
+        )
+        try:
+            x_lin = jnp.atleast_1d(jnp.asarray(dist_at_lin.mean))  # type: ignore
+        except Exception as exc:
+            raise ValueError(
+                "dist_at_lin.mean is not available. Linearized Kalman filter requires a mean-able distribution."
+            ) from exc
+
+        # On the first step, use identity linearization (x_lin = x_prev_lin).
+        x_lin = jnp.where(mi.is_first_step, x_prev_lin, x_lin)
+
+        return dynamics_log_density, x_prev_lin, x_lin
+
+    def get_observation_func(
+        state: taylor.LinearizedKalmanFilterState, mi: CuthbertInputs
+    ):
+        def log_potential(x):
+            edist = dynamics.observation_model(x, mi.u, mi.time)
+            return jnp.asarray(edist.log_prob(mi.y)).sum()
+
+        return log_potential, jnp.atleast_1d(jnp.asarray(state.mean))
+
+    kf = taylor.build_filter(
+        get_init_log_density,  # type: ignore
+        get_dynamics_log_density,  # type: ignore
+        get_observation_func,  # type: ignore
+        associative=False,
+        rtol=rtol,
+        ignore_nan_dims=True,
+    )
+    return kf
+
+
+def _add_sites_pf(
+    name: str, states: particle_filter.ParticleFilterState, record_kwargs: dict
+):
+    log_weights = states.log_weights
+    particles = states.particles
+    if particles.ndim == 2:
+        particles = particles[..., None]
+    max_elems = record_kwargs["record_max_elems"]
+    t_len, n_particles, state_dim = particles.shape
+
+    add_particles = _should_record_field(
+        record_kwargs["record_filtered_particles"], particles.shape, max_elems
+    )
+    add_log_weights = _should_record_field(
+        record_kwargs["record_filtered_log_weights"], log_weights.shape, max_elems
+    )
+    add_mean = _should_record_field(
+        record_kwargs["record_filtered_states_mean"], (t_len, state_dim), max_elems
+    )
+    add_filtered_states_cov = _should_record_field(
+        record_kwargs["record_filtered_states_cov"],
+        (t_len, state_dim, state_dim),
+        max_elems,
+    )
+    add_filtered_states_cov_diag = _should_record_field(
+        record_kwargs["record_filtered_states_cov_diag"], (t_len, state_dim), max_elems
+    )
+
+    need_filtered_means = (
+        add_mean or add_filtered_states_cov or add_filtered_states_cov_diag
+    )
+
+    if need_filtered_means:
+        w = jax.nn.softmax(log_weights, axis=1)[..., None]  # (T+1, n_particles, 1)
+        filtered_means = jnp.sum(particles * w, axis=1)  # (T+1, state_dim)
+
+    if add_filtered_states_cov or add_filtered_states_cov_diag:
+        second_mom = jnp.einsum(
+            "...tnj,...tnk,...tn->...tjk", particles, particles, w.squeeze(-1)
+        )
+        filtered_covariances = second_mom - jnp.einsum(
+            "...tj,...tk->...tjk", filtered_means, filtered_means
+        )
+
+    if add_particles:
+        numpyro.deterministic(f"{name}_filtered_particles", particles)
+    if add_log_weights:
+        numpyro.deterministic(f"{name}_filtered_log_weights", log_weights)
+    if add_mean:
+        numpyro.deterministic(f"{name}_filtered_states_mean", filtered_means)
+    if add_filtered_states_cov:
+        numpyro.deterministic(f"{name}_filtered_states_cov", filtered_covariances)
+    if add_filtered_states_cov_diag:
+        diag_cov = jnp.diagonal(filtered_covariances, axis1=1, axis2=2)
+        numpyro.deterministic(f"{name}_filtered_states_cov_diag", diag_cov)
+
+
+def _add_sites_gaussian_filter(
+    name: str,
+    states: taylor.LinearizedKalmanFilterState | ensemble_kalman_filter.EnKFState,
+    record_kwargs: dict,
+):
+    max_elems = record_kwargs["record_max_elems"]
+    mean = states.mean
+    chol_cov = states.chol_cov
+    t_len, state_dim, _ = chol_cov.shape
+
+    add_mean = _should_record_field(
+        record_kwargs["record_filtered_states_mean"], mean.shape, max_elems
+    )
+    add_chol_cov = _should_record_field(
+        record_kwargs["record_filtered_states_chol_cov"],
+        chol_cov.shape,
+        max_elems,
+    )
+    add_filtered_states_cov = _should_record_field(
+        record_kwargs["record_filtered_states_cov"],
+        (t_len, state_dim, state_dim),
+        max_elems,
+    )
+    add_filtered_states_cov_diag = _should_record_field(
+        record_kwargs["record_filtered_states_cov_diag"], (t_len, state_dim), max_elems
+    )
+
+    if add_mean:
+        numpyro.deterministic(f"{name}_filtered_states_mean", mean)
+    if add_chol_cov:
+        numpyro.deterministic(f"{name}_filtered_states_chol_cov", chol_cov)
+
+    if add_filtered_states_cov or add_filtered_states_cov_diag:
+        filtered_cov = covariance_from_cholesky(chol_cov)
+
+    if add_filtered_states_cov:
+        numpyro.deterministic(f"{name}_filtered_states_cov", filtered_cov)
+    if add_filtered_states_cov_diag:
+        diag_cov = jnp.diagonal(filtered_cov, axis1=1, axis2=2)
+        numpyro.deterministic(f"{name}_filtered_states_cov_diag", diag_cov)
diff --git a/dynestyx/inference/integrations/cuthbert/discrete_smoother.py b/dynestyx/inference/integrations/cuthbert/discrete_smoother.py
new file mode 100644
index 00000000..05fdcdb3
--- /dev/null
+++ b/dynestyx/inference/integrations/cuthbert/discrete_smoother.py
@@ -0,0 +1,384 @@
+"""Discrete-time smoothers via cuthbert: Kalman, Taylor-KF, and PF backward sampling."""
+
+from collections.abc import Callable
+from functools import partial
+from typing import cast
+
+import jax
+import jax.numpy as jnp
+import numpyro
+import numpyro.distributions as dist
+from cuthbert import smoother as cuthbert_smoother
+from cuthbert.gaussian import kalman, taylor
+from cuthbert.smc import backward_sampler
+from cuthbertlib.resampling import multinomial, stop_gradient_decorator, systematic
+from cuthbertlib.smc.smoothing import exact_sampling, mcmc, tracing
+
+from dynestyx.inference.distribution_utils import _cholesky_state_sequence_to_dists
+from dynestyx.inference.integrations.cuthbert.discrete_filter import (
+    CuthbertInputs,
+    _config_to_filter_kwargs,
+    _drop_cuthbert_dummy_step,
+    compute_cuthbert_filter,
+)
+from dynestyx.inference.integrations.utils import (
+    covariance_from_cholesky,
+    squeeze_leading_singletons,
+)
+from dynestyx.inference.smoother_configs import (
+    EKFSmootherConfig,
+    KFSmootherConfig,
+    PFSmootherConfig,
+    _config_to_smoother_record_kwargs,
+)
+from dynestyx.models import (
+    DynamicalModel,
+    LinearGaussianObservation,
+    LinearGaussianStateEvolution,
+)
+from dynestyx.utils import _should_record_field
+
+CuthbertSmootherConfig = KFSmootherConfig | EKFSmootherConfig | PFSmootherConfig
+
+
+def _kalman_get_dynamics_params(dynamics: DynamicalModel):
+    if not (
+        isinstance(dynamics.state_evolution, LinearGaussianStateEvolution)
+        and isinstance(dynamics.observation_model, LinearGaussianObservation)
+        and isinstance(dynamics.initial_condition, dist.MultivariateNormal)
+    ):
+        raise TypeError(
+            "cuthbert Kalman smoother expects a DynamicalModel with "
+            "LinearGaussianStateEvolution and LinearGaussianObservation, and "
+            "initial_condition as MultivariateNormal."
+        )
+
+    evo = dynamics.state_evolution
+    ic = dynamics.initial_condition
+    state_dim = dynamics.state_dim
+
+    m0 = jnp.reshape(
+        jnp.atleast_1d(squeeze_leading_singletons(ic.loc, 1)), (state_dim,)
+    )
+
+    A = jnp.asarray(evo.A)
+    chol_Q = jnp.linalg.cholesky(jnp.asarray(evo.cov))
+
+    evo_bias = (
+        jnp.zeros((state_dim,), dtype=m0.dtype)
+        if evo.bias is None
+        else jnp.reshape(jnp.atleast_1d(jnp.asarray(evo.bias)), (state_dim,))
+    )
+
+    B = None if evo.B is None else jnp.asarray(evo.B)
+
+    def get_dynamics_params(mi: CuthbertInputs):
+        def _noop(mi):
+            return (
+                jnp.eye(state_dim, dtype=m0.dtype),
+                jnp.zeros((state_dim,), dtype=m0.dtype),
+                jnp.zeros((state_dim, state_dim), dtype=m0.dtype),
+            )
+
+        def _evolve(mi):
+            c = evo_bias
+            if B is not None:
+                c = c + B @ jnp.atleast_1d(jnp.asarray(mi.u_prev))
+            return A, c, chol_Q
+
+        return jax.lax.cond(mi.is_first_step, _noop, _evolve, mi)
+
+    return get_dynamics_params
+
+
+def _taylor_get_dynamics_log_density(dynamics: DynamicalModel):
+    transition = cast(
+        Callable[
+            [jax.Array, jax.Array | None, jax.Array, jax.Array], dist.Distribution
+        ],
+        dynamics.state_evolution,
+    )
+
+    def get_dynamics_log_density(
+        state: taylor.LinearizedKalmanFilterState, mi: CuthbertInputs
+    ):
+        def dynamics_log_density(x_prev, x):
+            normal_logp = jnp.asarray(
+                transition(x_prev, mi.u_prev, mi.time_prev, mi.time).log_prob(x)
+            ).sum()
+            noop_logp = -1e10 * jnp.sum((x - x_prev) ** 2)
+            return jnp.where(mi.is_first_step, noop_logp, normal_logp)
+
+        x_prev_lin = jnp.atleast_1d(jnp.asarray(state.mean))
+        dist_at_lin = transition(x_prev_lin, mi.u_prev, mi.time_prev, mi.time)
+        try:
+            x_lin = jnp.atleast_1d(jnp.asarray(dist_at_lin.mean))
+        except Exception as exc:
+            raise ValueError(
+                "dist_at_lin.mean is not available. Linearized Kalman smoother requires a mean-able distribution."
+            ) from exc
+
+        x_lin = jnp.where(mi.is_first_step, x_prev_lin, x_lin)
+        return dynamics_log_density, x_prev_lin, x_lin
+
+    return get_dynamics_log_density
+
+
+def _pf_log_potential(dynamics: DynamicalModel):
+    def log_potential(x_prev, x, mi: CuthbertInputs):
+        edist = dynamics.observation_model(x, mi.u, mi.time)
+        return jnp.asarray(edist.log_prob(mi.y)).sum()
+
+    return log_potential
+
+
+def _pf_resampling_fn(filter_kwargs: dict):
+    base_method = filter_kwargs.get("resampling_base_method", "systematic")
+    if base_method == "systematic":
+        base_resampling_fn = systematic.resampling
+    elif base_method == "multinomial":
+        base_resampling_fn = multinomial.resampling
+    else:
+        raise ValueError(
+            f"Unsupported cuthbert PF base resampling method: {base_method!r}. "
+            "Expected one of: 'systematic', 'multinomial'."
+        )
+
+    differential_method = filter_kwargs.get(
+        "resampling_differential_method", "stop_gradient"
+    )
+    if differential_method == "stop_gradient":
+        base_resampling_fn = stop_gradient_decorator(base_resampling_fn)
+    elif differential_method == "straight_through":
+        pass
+    else:
+        raise ValueError(
+            "Unsupported cuthbert PF differential resampling method: "
+            f"{differential_method!r}. Expected one of: "
+            "'stop_gradient', 'straight_through'."
+        )
+
+    return base_resampling_fn
+
+
+def _pf_backward_sampling_fn(config: PFSmootherConfig):
+    method = config.pf_backward_sampling_method
+    if method == "tracing":
+        return tracing.simulate
+    if method == "exact":
+        return exact_sampling.simulate
+    if method == "mcmc":
+        return partial(mcmc.simulate, n_steps=config.pf_mcmc_n_steps)
+    raise ValueError(
+        "Unsupported PF smoother backward sampling method: "
+        f"{method!r}. Expected one of: 'tracing', 'exact', 'mcmc'."
+    )
+
+
+def compute_cuthbert_smoother(
+    dynamics: DynamicalModel,
+    smoother_config: CuthbertSmootherConfig,
+    key: jax.Array | None = None,
+    *,
+    obs_times: jax.Array,
+    obs_values: jax.Array,
+    ctrl_times=None,
+    ctrl_values=None,
+):
+    """Pure-JAX cuthbert smoother computation (no numpyro side-effects)."""
+    obs_len = int(obs_values.shape[0])
+    marginal_loglik, filtered_states = compute_cuthbert_filter(
+        dynamics,
+        smoother_config,
+        key,
+        obs_times=obs_times,
+        obs_values=obs_values,
+        ctrl_times=ctrl_times,
+        ctrl_values=ctrl_values,
+        align_to_observations=False,
+    )
+
+    filter_kwargs = _config_to_filter_kwargs(smoother_config)
+
+    if isinstance(smoother_config, KFSmootherConfig):
+        smoother_obj = kalman.build_smoother(
+            get_dynamics_params=_kalman_get_dynamics_params(dynamics)
+        )
+        smoothed_states = cuthbert_smoother(
+            smoother_obj, filtered_states, model_inputs=None, parallel=False, key=key
+        )
+    elif isinstance(smoother_config, EKFSmootherConfig):
+        smoother_obj = taylor.build_smoother(
+            _taylor_get_dynamics_log_density(dynamics),
+            rtol=filter_kwargs.get("rtol", None),
+            ignore_nan_dims=True,
+        )
+        smoothed_states = cuthbert_smoother(
+            smoother_obj, filtered_states, model_inputs=None, parallel=False, key=key
+        )
+    elif isinstance(smoother_config, PFSmootherConfig):
+        if key is None:
+            raise ValueError(
+                "Particle smoother requires a PRNG key: set 'crn_seed' in the filter config, "
+                "or run inside a NumPyro seeded context (e.g., with numpyro.handlers.seed)."
+            )
+        n_smoother_particles = (
+            smoother_config.pf_n_smoother_particles
+            if smoother_config.pf_n_smoother_particles is not None
+            else int(smoother_config.n_particles)
+        )
+        smoother_obj = backward_sampler.build_smoother(
+            log_potential=_pf_log_potential(dynamics),
+            backward_sampling_fn=_pf_backward_sampling_fn(smoother_config),
+            resampling_fn=_pf_resampling_fn(filter_kwargs),
+            n_smoother_particles=n_smoother_particles,
+        )
+        smoothed_states = cuthbert_smoother(
+            smoother_obj,
+            filtered_states,
+            model_inputs=None,
+            parallel=False,
+            key=key,
+        )
+    else:
+        raise ValueError(
+            f"Unsupported cuthbert smoother config: {type(smoother_config).__name__}. "
+            "Expected KFSmootherConfig, EKFSmootherConfig, PFSmootherConfig."
+        )
+
+    smoothed_states = _drop_cuthbert_dummy_step(smoothed_states, obs_len=obs_len)
+    return marginal_loglik, smoothed_states
+
+
+def _add_sites_pf(name: str, states, record_kwargs: dict):
+    log_weights = states.log_weights
+    particles = states.particles
+    if particles.ndim == 2:
+        particles = particles[..., None]
+    max_elems = record_kwargs["record_max_elems"]
+    t1, _, state_dim = particles.shape
+
+    add_particles = _should_record_field(
+        record_kwargs["record_smoothed_particles"], particles.shape, max_elems
+    )
+    add_log_weights = _should_record_field(
+        record_kwargs["record_smoothed_log_weights"], log_weights.shape, max_elems
+    )
+    add_mean = _should_record_field(
+        record_kwargs["record_smoothed_states_mean"], (t1, state_dim), max_elems
+    )
+    add_smoothed_states_cov = _should_record_field(
+        record_kwargs["record_smoothed_states_cov"],
+        (t1, state_dim, state_dim),
+        max_elems,
+    )
+    add_smoothed_states_cov_diag = _should_record_field(
+        record_kwargs["record_smoothed_states_cov_diag"], (t1, state_dim), max_elems
+    )
+
+    need_means = add_mean or add_smoothed_states_cov or add_smoothed_states_cov_diag
+    if need_means:
+        w = jax.nn.softmax(log_weights, axis=1)[..., None]
+        smoothed_means = jnp.sum(particles * w, axis=1)
+
+    if add_smoothed_states_cov or add_smoothed_states_cov_diag:
+        second_mom = jnp.einsum(
+            "...tnj,...tnk,...tn->...tjk", particles, particles, w.squeeze(-1)
+        )
+        smoothed_cov = second_mom - jnp.einsum(
+            "...tj,...tk->...tjk", smoothed_means, smoothed_means
+        )
+
+    if add_particles:
+        numpyro.deterministic(f"{name}_smoothed_particles", particles)
+    if add_log_weights:
+        numpyro.deterministic(f"{name}_smoothed_log_weights", log_weights)
+    if add_mean:
+        numpyro.deterministic(f"{name}_smoothed_states_mean", smoothed_means)
+    if add_smoothed_states_cov:
+        numpyro.deterministic(f"{name}_smoothed_states_cov", smoothed_cov)
+    if add_smoothed_states_cov_diag:
+        diag_cov = jnp.diagonal(smoothed_cov, axis1=1, axis2=2)
+        numpyro.deterministic(f"{name}_smoothed_states_cov_diag", diag_cov)
+
+
+def _add_sites_taylor_kf(name: str, states, record_kwargs: dict):
+    max_elems = record_kwargs["record_max_elems"]
+    mean = states.mean
+    chol_cov = states.chol_cov
+    t1, state_dim, _ = chol_cov.shape
+
+    add_mean = _should_record_field(
+        record_kwargs["record_smoothed_states_mean"], mean.shape, max_elems
+    )
+    add_chol_cov = _should_record_field(
+        record_kwargs["record_smoothed_states_chol_cov"],
+        chol_cov.shape,
+        max_elems,
+    )
+    add_smoothed_states_cov = _should_record_field(
+        record_kwargs["record_smoothed_states_cov"],
+        (t1, state_dim, state_dim),
+        max_elems,
+    )
+    add_smoothed_states_cov_diag = _should_record_field(
+        record_kwargs["record_smoothed_states_cov_diag"], (t1, state_dim), max_elems
+    )
+
+    if add_mean:
+        numpyro.deterministic(f"{name}_smoothed_states_mean", mean)
+    if add_chol_cov:
+        numpyro.deterministic(f"{name}_smoothed_states_chol_cov", chol_cov)
+
+    if add_smoothed_states_cov or add_smoothed_states_cov_diag:
+        smoothed_cov = covariance_from_cholesky(chol_cov)
+
+    if add_smoothed_states_cov:
+        numpyro.deterministic(f"{name}_smoothed_states_cov", smoothed_cov)
+    if add_smoothed_states_cov_diag:
+        diag_cov = jnp.diagonal(smoothed_cov, axis1=1, axis2=2)
+        numpyro.deterministic(f"{name}_smoothed_states_cov_diag", diag_cov)
+
+
+def run_discrete_smoother(
+    name: str,
+    dynamics: DynamicalModel,
+    smoother_config: CuthbertSmootherConfig,
+    key: jax.Array | None = None,
+    *,
+    obs_times: jax.Array,
+    obs_values: jax.Array,
+    ctrl_times=None,
+    ctrl_values=None,
+    **kwargs,
+) -> list[dist.Distribution]:
+    """Run discrete-time smoother via cuthbert."""
+    t1 = int(obs_values.shape[0])
+    if t1 == 0:
+        return []
+
+    marginal_loglik, states = compute_cuthbert_smoother(
+        dynamics,
+        smoother_config,
+        key,
+        obs_times=obs_times,
+        obs_values=obs_values,
+        ctrl_times=ctrl_times,
+        ctrl_values=ctrl_values,
+    )
+    record_kwargs = _config_to_smoother_record_kwargs(smoother_config)
+
+    numpyro.factor(f"{name}_marginal_log_likelihood", marginal_loglik)
+    numpyro.deterministic(f"{name}_marginal_loglik", marginal_loglik)
+
+    if isinstance(smoother_config, PFSmootherConfig):
+        _add_sites_pf(name, states, record_kwargs)
+    else:
+        _add_sites_taylor_kf(name, states, record_kwargs)
+    return _cholesky_state_sequence_to_dists(
+        states,
+        particle_mode=isinstance(smoother_config, PFSmootherConfig),
+    )
+
+
+__all__ = ["compute_cuthbert_smoother", "run_discrete_smoother"]
diff --git a/dynestyx/inference/plate_utils.py b/dynestyx/inference/plate_utils.py
new file mode 100644
index 00000000..1f16aa7b
--- /dev/null
+++ b/dynestyx/inference/plate_utils.py
@@ -0,0 +1,66 @@
+import jax
+import numpyro
+
+from dynestyx.utils import (
+    _array_has_plate_dims,
+    _dist_has_plate_batch_dims,
+    _leaf_is_plate_batched,
+    _path_field_names,
+)
+
+
+def _make_plate_in_axes(tree, plate_shapes: tuple[int, ...]):
+    """Build ``in_axes`` for leaves whose leading dims match active plates.
+
+    Unbatched distributions are treated as opaque shared leaves. Batched
+    distributions are traversed so mixed batched/shared parameters, such as a
+    batched ``MultivariateNormal.loc`` with shared covariance, can be mapped
+    correctly.
+    """
+
+    def _is_unbatched_distribution_leaf(node) -> bool:
+        return isinstance(
+            node, numpyro.distributions.Distribution
+        ) and not _dist_has_plate_batch_dims(node, plate_shapes)
+
+    def _axis(path, leaf):
+        if isinstance(leaf, numpyro.distributions.Distribution):
+            return None
+
+        if "initial_condition" in _path_field_names(path):
+            return (
+                0
+                if _array_has_plate_dims(leaf, plate_shapes, min_suffix_ndim=1)
+                else None
+            )
+
+        return 0 if _leaf_is_plate_batched(leaf, plate_shapes, path=path) else None
+
+    return jax.tree_util.tree_map_with_path(
+        _axis,
+        tree,
+        is_leaf=_is_unbatched_distribution_leaf,
+    )
+
+
+def _array_plate_axis(arr, plate_shapes: tuple[int, ...]):
+    """Return 0 if arr has leading dims matching plate_shapes, else None."""
+    return 0 if _array_has_plate_dims(arr, plate_shapes, min_suffix_ndim=1) else None
+
+
+def _get_time_axis(plate_shapes: tuple[int, ...]) -> int:
+    """Return the axis index corresponding to time after plate dimensions."""
+    return len(plate_shapes)
+
+
+def _time_len_from_array(arr: jax.Array, plate_shapes: tuple[int, ...]) -> int:
+    """Infer sequence length from an array with plate dims followed by time."""
+    return int(arr.shape[_get_time_axis(plate_shapes)])
+
+
+def _slice_time_axis(
+    arr: jax.Array, t: int, plate_shapes: tuple[int, ...]
+) -> jax.Array:
+    """Slice an array at time index t where time axis follows plate dims."""
+    time_axis = _get_time_axis(plate_shapes)
+    return arr[(slice(None),) * time_axis + (t, ...)]
diff --git a/dynestyx/inference/smoother_configs.py b/dynestyx/inference/smoother_configs.py
new file mode 100644
index 00000000..c3175ed5
--- /dev/null
+++ b/dynestyx/inference/smoother_configs.py
@@ -0,0 +1,386 @@
+"""Smoother configuration dataclasses. Shared by dispatchers and integration backends."""
+
+import abc
+import dataclasses
+from typing import Literal
+
+from dynestyx.inference.filter_configs import (
+    ContinuousTimeEKFConfig,
+    ContinuousTimeKFConfig,
+    EKFConfig,
+    KFConfig,
+    PFConfig,
+    UKFConfig,
+)
+
+PFBackwardSamplingMethod = Literal["tracing", "exact", "mcmc"]
+CDKFSmootherType = Literal["cd_smoother_1", "cd_smoother_2"]
+
+
+@dataclasses.dataclass
+class BaseSmootherConfig(abc.ABC):
+    r"""Shared base class for all smoother configs.
+
+    You do not instantiate this class directly; use one of the concrete
+    subclasses (e.g. `KFSmootherConfig`, `PFSmootherConfig`).
+
+    Concrete smoother configs inherit from their corresponding filter configs,
+    so backend selection, filter tuning, continuous-time solver options, and
+    filter recording fields follow the same conventions as
+    `dynestyx.inference.filter_configs`.
+
+    The `record_smoothed_*` fields let you save intermediate smoothing outputs
+    into the NumPyro trace as `numpyro.deterministic` sites, making them
+    accessible after inference (e.g. for plotting smoothed trajectories).
+    `None` defers to the backend's default for that quantity.
+
+    Attributes:
+        record_smoothed_states_mean (bool | None): Save the posterior smoothing
+            mean \(\mathbb{E}[x_t \mid y_{1:T}]\) at each time step.
+        record_smoothed_states_cov (bool | None): Save the full smoothing
+            covariance at each time step. Can be large; prefer
+            `record_smoothed_states_cov_diag` for high-dimensional states.
+        record_smoothed_states_cov_diag (bool | None): Save only the marginal
+            smoothing variances at each time step.
+        record_smoothed_particles (bool | None): Save smoothed particles for
+            particle smoothers.
+        record_smoothed_log_weights (bool | None): Save smoothed particle log
+            weights for particle smoothers.
+        record_smoothed_states_chol_cov (bool | None): Save the Cholesky factor
+            of the smoothing covariance when exposed by the backend.
+        record_max_elems (int): Hard cap on total scalar elements saved across
+            all `record_*` sites. Prevents accidentally filling device memory
+            for long sequences or large state spaces. Defaults to `100_000`.
+    """
+
+    record_smoothed_states_mean: bool | None = None
+    record_smoothed_states_cov: bool | None = None
+    record_smoothed_states_cov_diag: bool | None = None
+    record_smoothed_particles: bool | None = None
+    record_smoothed_log_weights: bool | None = None
+    record_smoothed_states_chol_cov: bool | None = None
+    record_max_elems: int = 100_000
+
+    def __post_init__(self):
+        if type(self) is BaseSmootherConfig:
+            raise TypeError(
+                "BaseSmootherConfig is abstract and cannot be instantiated."
+            )
+
+
+@dataclasses.dataclass
+class KFSmootherConfig(KFConfig, BaseSmootherConfig):
+    r"""Rauch-Tung-Striebel Kalman Smoother (RTS) for discrete-time models.
+
+    The exact Bayesian smoother for discrete-time linear-Gaussian state-space
+    models. Use this when your model was built with `LTI_discrete` or with
+    `LinearGaussianStateEvolution` + `LinearGaussianObservation`.
+
+    The smoother computes \(p(x_t \mid y_{1:T})\), while `KFConfig` computes
+    \(p(x_t \mid y_{1:t})\). Use the inherited `record_smoothed_*` fields to
+    save smoothed means, covariances, or covariance diagonals in the NumPyro
+    trace.
+
+    Attributes:
+        filter_source (FilterSource): Backend. Defaults to `"cd_dynamax"`.
+        associative (bool | None): Whether to enable cuthbert's associative
+            parallel-in-time scan. This is only supported when
+            `filter_source="cuthbert"`. Defaults to `None`, which selects
+            an associative scan if `filter_source="cuthbert"`, and a
+            sequential scan otherwise.
+
+    ??? note "Algorithm Reference"
+        After a forward Kalman filtering pass, the RTS smoother runs a backward
+        recursion over the filtered and one-step-ahead predicted Gaussian
+        states. For a linear transition matrix \(A_t\), the smoothing gain is
+
+        $$
+            G_t = P_{t|t} A_t^\top P_{t+1|t}^{-1}.
+        $$
+
+        The smoothed mean and covariance are then
+
+        $$
+            m_{t|T} = m_{t|t} + G_t (m_{t+1|T} - m_{t+1|t}),
+        $$
+
+        and
+
+        $$
+            P_{t|T} = P_{t|t}
+                + G_t (P_{t+1|T} - P_{t+1|t}) G_t^\top.
+        $$
+
+        References:
+
+        - For the original smoother, see: Rauch, H. E., Tung, F., & Striebel, C. T. (1965).
+            Maximum likelihood estimates of linear dynamic systems. AIAA Journal, 3(8), 1445-1450.
+        - For a modern textbook reference, see: Särkkä, S., & Svensson, L. (2023).
+            Bayesian Filtering and Smoothing (Vol. 17). Cambridge University Press.
+            [Available Online](https://users.aalto.fi/~ssarkka/pub/bfs_book_2023_online.pdf).
+    """
+
+
+@dataclasses.dataclass
+class EKFSmootherConfig(EKFConfig, BaseSmootherConfig):
+    r"""Extended Kalman Smoother (EKS) for discrete-time models.
+
+    The Gaussian smoother corresponding to `EKFConfig`. Use this for
+    differentiable nonlinear dynamics when local linearization is a reasonable
+    approximation to the filtering and smoothing distributions.
+
+    This is exact (but wasteful) for linear-Gaussian models. For those models,
+    prefer `KFSmootherConfig`.
+
+    Attributes:
+        filter_source (FilterSource): Backend. Defaults to `"cuthbert"`.
+
+    ??? note "Algorithm Reference"
+        The EKS applies the RTS backward recursion to the linear-Gaussian
+        approximations produced by an EKF filtering pass. The transition matrix
+        in the smoothing gain is the Jacobian of the transition function at the
+        filtered mean:
+
+        $$
+            G_t = P_{t|t} F_t^\top P_{t+1|t}^{-1}.
+        $$
+
+        Smoothed means and covariances are then computed with the same
+        backward recursion as `KFSmootherConfig`.
+
+        References:
+
+        - For a classical textbook treatment, see: Jazwinski, A. H. (1970).
+            Stochastic Processes and Filtering Theory. Academic Press.
+        - For a modern textbook reference, see: Särkkä, S., & Svensson, L. (2023).
+            Bayesian Filtering and Smoothing (Vol. 17). Cambridge University Press.
+            [Available Online](https://users.aalto.fi/~ssarkka/pub/bfs_book_2023_online.pdf).
+    """
+
+
+@dataclasses.dataclass
+class UKFSmootherConfig(UKFConfig, BaseSmootherConfig):
+    r"""Unscented Kalman Smoother (UKS) for discrete-time models.
+
+    A derivative-free Gaussian smoother that handles nonlinearities by
+    propagating deterministic sigma points rather than Jacobians. Use this when
+    sigma-point propagation is a better local approximation than EKF
+    linearization.
+
+    This smoother is currently supported with `filter_source="cd_dynamax"` only.
+    Cuthbert UKF smoothing is not implemented.
+
+    Attributes:
+        alpha (float): Spread of sigma points around the current mean.
+            Smaller → tighter cluster; larger → sigma points reach further.
+            Defaults to \(\sqrt{3}\).
+        beta (int): Encodes prior knowledge about the distribution shape.
+            `2` is optimal for Gaussians. Defaults to `2`.
+        kappa (int): Secondary scaling parameter. Defaults to `1`.
+        filter_source (FilterSource): Backend. Defaults to `"cd_dynamax"`.
+
+    ??? note "Algorithm Reference"
+        The UKS uses a forward UKF pass followed by an RTS-style backward pass.
+        Instead of using a Jacobian in the smoothing gain, it estimates the
+        lag-one cross-covariance \(P_{t,t+1|t}\) by propagating sigma points:
+
+        $$
+            G_t = P_{t,t+1|t} P_{t+1|t}^{-1}.
+        $$
+
+        The smoothed mean and covariance then follow the same correction form
+        as the Kalman and extended Kalman smoothers.
+
+        References:
+
+        - For the original unscented Kalman filter paper, see: Julier, S. J., & Uhlmann, J. K. (1997).
+            New extension of the Kalman filter to nonlinear systems. SPIE Proceedings, 3068.
+        - For the unscented RTS smoother, see: Särkkä, S. (2008).
+            Unscented Rauch-Tung-Striebel smoother. IEEE Transactions on Automatic Control, 53(3), 845-849.
+        - For a modern textbook reference, see: Särkkä, S., & Svensson, L. (2023).
+            Bayesian Filtering and Smoothing (Vol. 17). Cambridge University Press.
+            [Available Online](https://users.aalto.fi/~ssarkka/pub/bfs_book_2023_online.pdf).
+    """
+
+
+@dataclasses.dataclass
+class PFSmootherConfig(PFConfig, BaseSmootherConfig):
+    r"""Particle smoother for discrete-time models.
+
+    Use this for nonlinear or non-Gaussian discrete-time models. Particle
+    smoothing is more flexible than Gaussian smoothing, but accuracy scales
+    with the number of particles and backward-sampled smoother trajectories.
+
+    This smoother is currently supported with `filter_source="cuthbert"` only.
+
+    Attributes:
+        n_particles (int): Number of particles in the forward particle filter.
+            More particles give a lower-variance log-likelihood and smoothing
+            approximation at linear compute cost. Defaults to `1_000`.
+        pf_backward_sampling_method (PFBackwardSamplingMethod): Backward
+            simulation method. `"tracing"` is the default, `"exact"` performs
+            exact backward sampling where available, and `"mcmc"` uses an MCMC
+            backward kernel.
+        pf_mcmc_n_steps (int): Number of MCMC steps used when
+            `pf_backward_sampling_method="mcmc"`.
+        pf_n_smoother_particles (int | None): Number of backward-sampled
+            smoother particles. `None` inherits `n_particles` from `PFConfig`.
+        filter_source (FilterSource): Backend. Defaults to `"cuthbert"`, which
+            is currently the only available implementation.
+
+    ??? note "Algorithm Reference"
+        Particle smoothers augment a forward particle filter with a backward
+        pass that reconstructs trajectories from the particle approximation to
+        the filtering distributions.
+
+        In backward simulation, a sampled state \(x_{t+1}^{(j)}\) chooses an
+        ancestor at time \(t\) with probabilities proportional to
+
+        $$
+            w_t^{(i)} p(x_{t+1}^{(j)} \mid x_t^{(i)}).
+        $$
+
+        The `"tracing"` method follows stored particle ancestors. The `"exact"`
+        and `"mcmc"` methods use backward kernels that can better approximate
+        the full smoothing distribution at higher compute cost.
+
+        References:
+
+        - For backward simulation particle smoothing, see: Godsill, S. J., Doucet, A., & West, M. (2004).
+            Monte Carlo smoothing for nonlinear time series. Journal of the American Statistical Association, 99(465), 156-168.
+        - For a general SMC/particle filtering reference, see: Doucet, A., De Freitas, N., & Gordon, N. (2001).
+            An Introduction to Sequential Monte Carlo Methods. In Sequential Monte Carlo Methods in Practice (pp. 3-14).
+            New York, NY: Springer New York.
+        - For a modern textbook reference, see: Särkkä, S., & Svensson, L. (2023).
+            Bayesian Filtering and Smoothing (Vol. 17). Cambridge University Press.
+            [Available Online](https://users.aalto.fi/~ssarkka/pub/bfs_book_2023_online.pdf).
+    """
+
+    pf_backward_sampling_method: PFBackwardSamplingMethod = "tracing"
+    pf_mcmc_n_steps: int = 10
+    pf_n_smoother_particles: int | None = None
+
+
+@dataclasses.dataclass
+class ContinuousTimeKFSmootherConfig(ContinuousTimeKFConfig, BaseSmootherConfig):
+    r"""Continuous-discrete Kalman Smoother (CD-KS).
+
+    The exact Bayesian smoother for continuous-time linear-Gaussian models with
+    discrete observations. Use this when your model was built with
+    `LTI_continuous`.
+
+    This smoother is currently implemented through the `cd_dynamax` backend.
+    Inherited differential-equation solver options control the continuous-time
+    filtering pass, and inherited `record_smoothed_*` fields control trace
+    recording.
+
+    Attributes:
+        cdlgssm_smoother_type (CDKFSmootherType): CD-Dynamax smoother variant.
+            `"cd_smoother_1"` is the default; `"cd_smoother_2"` exposes the
+            backend's alternate continuous-discrete RTS implementation.
+        filter_source (FilterSource): Backend. Defaults to `"cd_dynamax"`.
+
+    ??? note "Algorithm Reference"
+        Continuous-discrete Kalman smoothing runs a continuous-time Kalman
+        filtering pass between observations, applies discrete Kalman updates at
+        observation times, and then integrates the corresponding backward
+        smoothing equations through the filtered trajectory.
+
+        The result is the continuous-discrete analogue of the RTS smoother:
+        each state is conditioned on all observations \(y_{1:T}\), not just the
+        observations available up to that time.
+
+        References:
+
+        - For continuous-discrete smoothing equations, see: Särkkä, S. (2006).
+            Recursive Bayesian Inference on Stochastic Differential Equations.
+        - For a modern textbook reference, see Chapter 10.6 of:
+            Särkkä, S., & Solin, A. (2019). Applied Stochastic Differential Equations.
+            Cambridge University Press. [Available Online](https://users.aalto.fi/~asolin/sde-book/sde-book.pdf).
+        - For general Gaussian smoothing background, see: Särkkä, S., & Svensson, L. (2023).
+            Bayesian Filtering and Smoothing (Vol. 17). Cambridge University Press.
+            [Available Online](https://users.aalto.fi/~ssarkka/pub/bfs_book_2023_online.pdf).
+    """
+
+    cdlgssm_smoother_type: CDKFSmootherType = "cd_smoother_1"
+
+
+@dataclasses.dataclass
+class ContinuousTimeEKFSmootherConfig(ContinuousTimeEKFConfig, BaseSmootherConfig):
+    r"""Continuous-discrete Extended Kalman Smoother (CD-EKS).
+
+    Gaussian smoother for mildly nonlinear continuous-time dynamics with
+    discrete observations. Requires differentiable dynamics; JAX autodiff is
+    used to construct the local linearizations.
+
+    This smoother is currently implemented through the `cd_dynamax` backend.
+    See `ContinuousTimeEKFConfig` for linearisation and solver options.
+
+    Attributes:
+        filter_source (FilterSource): Backend. Defaults to `"cd_dynamax"`.
+
+    ??? note "Algorithm Reference"
+        The CD-EKS combines a continuous-discrete EKF filtering pass with a
+        backward smoothing pass through the locally linear Gaussian
+        approximations. It is the continuous-discrete analogue of
+        `EKFSmootherConfig`.
+
+        The inherited `filter_state_order` and `filter_emission_order` options
+        control the Taylor approximation used during the continuous-time
+        filtering and smoothing computation.
+
+        References:
+
+        - For continuous-discrete smoothing equations, see: Särkkä, S. (2006).
+            Recursive Bayesian Inference on Stochastic Differential Equations.
+        - For a modern textbook reference, see Chapter 10.9 of:
+            Särkkä, S., & Solin, A. (2019). Applied Stochastic Differential Equations.
+            Cambridge University Press. [Available Online](https://users.aalto.fi/~asolin/sde-book/sde-book.pdf).
+        - For general Gaussian smoothing background, see: Särkkä, S., & Svensson, L. (2023).
+            Bayesian Filtering and Smoothing (Vol. 17). Cambridge University Press.
+            [Available Online](https://users.aalto.fi/~ssarkka/pub/bfs_book_2023_online.pdf).
+    """
+
+
+DiscreteTimeSmootherConfigs: tuple[type, ...] = (
+    KFSmootherConfig,
+    EKFSmootherConfig,
+    UKFSmootherConfig,
+    PFSmootherConfig,
+)
+
+ContinuousTimeSmootherConfigs: tuple[type, ...] = (
+    ContinuousTimeKFSmootherConfig,
+    ContinuousTimeEKFSmootherConfig,
+)
+
+
+def _config_to_smoother_record_kwargs(config: BaseSmootherConfig) -> dict:
+    """Build smoother record kwargs dict from config."""
+    return {
+        "record_smoothed_states_mean": config.record_smoothed_states_mean,
+        "record_smoothed_states_cov": config.record_smoothed_states_cov,
+        "record_smoothed_states_cov_diag": config.record_smoothed_states_cov_diag,
+        "record_smoothed_particles": config.record_smoothed_particles,
+        "record_smoothed_log_weights": config.record_smoothed_log_weights,
+        "record_smoothed_states_chol_cov": config.record_smoothed_states_chol_cov,
+        "record_max_elems": config.record_max_elems,
+    }
+
+
+__all__ = [
+    "BaseSmootherConfig",
+    "CDKFSmootherType",
+    "ContinuousTimeEKFSmootherConfig",
+    "ContinuousTimeKFSmootherConfig",
+    "ContinuousTimeSmootherConfigs",
+    "DiscreteTimeSmootherConfigs",
+    "EKFSmootherConfig",
+    "KFSmootherConfig",
+    "PFBackwardSamplingMethod",
+    "PFSmootherConfig",
+    "UKFSmootherConfig",
+]
+
+
+SmootherConfig = BaseSmootherConfig
diff --git a/dynestyx/inference/smoothers.py b/dynestyx/inference/smoothers.py
new file mode 100644
index 00000000..2b15f99a
--- /dev/null
+++ b/dynestyx/inference/smoothers.py
@@ -0,0 +1,530 @@
+import dataclasses
+import math
+from typing import cast
+
+import equinox as eqx
+import jax
+import jax.numpy as jnp
+import numpy as np
+import numpyro
+from effectful.ops.semantics import fwd
+from effectful.ops.syntax import ObjectInterpretation, implements
+
+from dynestyx.handlers import HandlesSelf, _sample_intp
+from dynestyx.inference.checkers import _validate_batched_plate_alignment
+from dynestyx.inference.distribution_utils import (
+    _cholesky_state_sequence_to_dists,
+    _posterior_sequence_to_dists,
+)
+from dynestyx.inference.integrations.cd_dynamax.continuous_smoother import (
+    compute_continuous_smoother,
+    run_continuous_smoother,
+)
+from dynestyx.inference.integrations.cd_dynamax.discrete_smoother import (
+    compute_cd_dynamax_discrete_smoother,
+)
+from dynestyx.inference.integrations.cd_dynamax.discrete_smoother import (
+    run_discrete_smoother as run_cd_dynamax_discrete_smoother,
+)
+from dynestyx.inference.integrations.cuthbert.discrete_smoother import (
+    compute_cuthbert_smoother,
+)
+from dynestyx.inference.integrations.cuthbert.discrete_smoother import (
+    run_discrete_smoother as run_cuthbert_discrete_smoother,
+)
+from dynestyx.inference.plate_utils import _array_plate_axis, _make_plate_in_axes
+from dynestyx.inference.smoother_configs import (
+    BaseSmootherConfig,
+    ContinuousTimeEKFSmootherConfig,
+    ContinuousTimeKFSmootherConfig,
+    ContinuousTimeSmootherConfigs,
+    DiscreteTimeSmootherConfigs,
+    EKFSmootherConfig,
+    KFSmootherConfig,
+    PFSmootherConfig,
+    UKFSmootherConfig,
+)
+from dynestyx.models import DynamicalModel
+from dynestyx.types import FunctionOfTime
+from dynestyx.utils import _ensure_continuous_bm_dim
+
+DiscreteSmootherConfig = (
+    KFSmootherConfig | EKFSmootherConfig | UKFSmootherConfig | PFSmootherConfig
+)
+ContinuousSmootherConfig = (
+    ContinuousTimeKFSmootherConfig | ContinuousTimeEKFSmootherConfig
+)
+SmootherAnyConfig = DiscreteSmootherConfig | ContinuousSmootherConfig
+
+
+def _default_smoother_config(dynamics: DynamicalModel) -> SmootherAnyConfig:
+    """Return an appropriate default smoother config when none is specified."""
+    if dynamics.continuous_time:
+        return ContinuousTimeEKFSmootherConfig()
+    return EKFSmootherConfig(filter_source="cuthbert")
+
+
+def _valid_smoother_config_names(*, continuous_time: bool) -> list[str]:
+    if continuous_time:
+        return [c.__name__ for c in ContinuousTimeSmootherConfigs]
+    return [c.__name__ for c in DiscreteTimeSmootherConfigs]
+
+
+def _validate_future_only_predict_times(
+    predict_times: jax.Array | None,
+    obs_times: jax.Array | None,
+) -> jax.Array | None:
+    """Validate the current smoother prediction contract."""
+    if predict_times is None or obs_times is None:
+        return predict_times
+    obs_end = obs_times[..., -1:]
+    _ = eqx.error_if(
+        predict_times,
+        jnp.any(predict_times < obs_end),
+        "Smoother prediction only supports predict_times >= max(obs_times); in-window smoothing predictions are not implemented yet. Please use `Filter` for in-window predictions for now.",
+    )
+    return predict_times
+
+
+def _final_obs_times_for_rollout(obs_times: jax.Array) -> jax.Array:
+    """Return the final observation time while keeping simulator segmentation host-safe."""
+    try:
+        obs_times_host = np.asarray(jax.device_get(obs_times))
+        return jnp.asarray(obs_times_host[..., -1:], dtype=obs_times.dtype)
+    except Exception:
+        return obs_times[..., -1:]
+
+
+class BaseSmootherLogFactorAdder(ObjectInterpretation, HandlesSelf):
+    """Base class for smoother handlers."""
+
+    @implements(_sample_intp)
+    def _sample_ds(
+        self,
+        name: str,
+        dynamics: DynamicalModel,
+        *,
+        plate_shapes=(),
+        obs_times=None,
+        obs_values=None,
+        ctrl_times=None,
+        ctrl_values=None,
+        predict_times=None,
+        **kwargs,
+    ) -> FunctionOfTime:
+        smoothed_dists = None
+        if not (obs_times is None or obs_values is None):
+            smoothed_dists = self._add_log_factors(
+                name,
+                dynamics,
+                plate_shapes=plate_shapes,
+                obs_times=obs_times,
+                obs_values=obs_values,
+                ctrl_times=ctrl_times,
+                ctrl_values=ctrl_values,
+                **kwargs,
+            )
+
+        predict_times = _validate_future_only_predict_times(predict_times, obs_times)
+        filtered_times = None
+        filtered_dists = None
+        posterior_rollout_final_only = False
+        smoothed_times = obs_times
+        if predict_times is not None and smoothed_dists:
+            filtered_times = _final_obs_times_for_rollout(obs_times)
+            filtered_dists = [smoothed_dists[-1]]
+            posterior_rollout_final_only = True
+            smoothed_times = None
+            smoothed_dists = None
+
+        return fwd(
+            name,
+            dynamics,
+            plate_shapes=plate_shapes,
+            obs_times=None,
+            obs_values=None,
+            ctrl_times=ctrl_times,
+            ctrl_values=ctrl_values,
+            predict_times=predict_times,
+            filtered_times=filtered_times,
+            filtered_dists=filtered_dists,
+            smoothed_times=smoothed_times,
+            smoothed_dists=smoothed_dists,
+            _posterior_rollout_final_only=posterior_rollout_final_only,
+            **kwargs,
+        )
+
+    def _add_log_factors(
+        self,
+        name: str,
+        dynamics: DynamicalModel,
+        *,
+        plate_shapes=(),
+        obs_times=None,
+        obs_values=None,
+        ctrl_times=None,
+        ctrl_values=None,
+        **kwargs,
+    ) -> list[numpyro.distributions.Distribution] | None:
+        raise NotImplementedError()
+
+
+@dataclasses.dataclass
+class Smoother(BaseSmootherLogFactorAdder):
+    r"""Performs Bayesian smoothing to compute the smoothing distribution p(x_t | y_{1:T})."""
+
+    smoother_config: SmootherAnyConfig | None = None
+
+    def _add_log_factors(
+        self,
+        name: str,
+        dynamics: DynamicalModel,
+        *,
+        plate_shapes=(),
+        obs_times: jax.Array | None = None,
+        obs_values: jax.Array | None = None,
+        ctrl_times=None,
+        ctrl_values=None,
+        **kwargs,
+    ) -> list[numpyro.distributions.Distribution] | None:
+        if obs_times is None or obs_values is None:
+            raise ValueError("obs_times and obs_values are required for smoothing.")
+
+        dynamics = _ensure_continuous_bm_dim(dynamics)
+
+        config = (
+            self.smoother_config
+            if self.smoother_config is not None
+            else _default_smoother_config(dynamics)
+        )
+        if not isinstance(config, BaseSmootherConfig):
+            valid = _valid_smoother_config_names(
+                continuous_time=dynamics.continuous_time
+            )
+            raise ValueError(
+                f"Invalid smoother config: {type(config).__name__}. "
+                "Expected a smoother config class from dynestyx.inference.smoother_configs. "
+                f"Valid types: {valid}"
+            )
+
+        typed_config = config
+        key = (
+            numpyro.prng_key()
+            if typed_config.crn_seed is None
+            else typed_config.crn_seed
+        )
+
+        if plate_shapes:
+            return self._add_log_factors_batched(
+                name,
+                dynamics,
+                typed_config,
+                key=key,
+                plate_shapes=plate_shapes,
+                obs_times=obs_times,
+                obs_values=obs_values,
+                ctrl_times=ctrl_times,
+                ctrl_values=ctrl_values,
+            )
+
+        if dynamics.continuous_time:
+            if not isinstance(typed_config, ContinuousTimeSmootherConfigs):
+                valid = _valid_smoother_config_names(continuous_time=True)
+                raise ValueError(
+                    f"Invalid smoother config: {type(typed_config).__name__}. "
+                    f"Valid continuous-time config types: {valid}"
+                )
+            continuous_config = cast(ContinuousSmootherConfig, typed_config)
+            return _smooth_continuous_time(
+                name,
+                dynamics,
+                continuous_config,
+                key=key,
+                obs_times=obs_times,
+                obs_values=obs_values,
+                ctrl_times=ctrl_times,
+                ctrl_values=ctrl_values,
+                **kwargs,
+            )
+
+        if not isinstance(typed_config, DiscreteTimeSmootherConfigs):
+            valid = _valid_smoother_config_names(continuous_time=False)
+            raise ValueError(
+                f"Invalid smoother config: {type(typed_config).__name__}. "
+                f"Valid discrete-time config types: {valid}"
+            )
+        discrete_config = cast(DiscreteSmootherConfig, typed_config)
+
+        return _smooth_discrete_time(
+            name,
+            dynamics,
+            discrete_config,
+            key=key,
+            obs_times=obs_times,
+            obs_values=obs_values,
+            ctrl_times=ctrl_times,
+            ctrl_values=ctrl_values,
+            **kwargs,
+        )
+
+    def _add_log_factors_batched(
+        self,
+        name: str,
+        dynamics: DynamicalModel,
+        config: SmootherAnyConfig,
+        *,
+        key: jax.Array | None,
+        plate_shapes: tuple[int, ...],
+        obs_times: jax.Array,
+        obs_values: jax.Array,
+        ctrl_times=None,
+        ctrl_values=None,
+    ) -> list[numpyro.distributions.Distribution]:
+        """Compute batched marginal log-likelihoods via vmap for plate contexts."""
+        output_kind: str
+        if dynamics.continuous_time:
+            if not isinstance(config, ContinuousTimeSmootherConfigs):
+                valid = _valid_smoother_config_names(continuous_time=True)
+                raise ValueError(
+                    f"Invalid smoother config: {type(config).__name__}. "
+                    f"Valid config types: {valid}"
+                )
+            continuous_config = cast(ContinuousSmootherConfig, config)
+            output_kind = "continuous"
+
+            def compute_output(dyn, ot, ov, ct, cv, k):
+                return compute_continuous_smoother(
+                    dyn,
+                    continuous_config,
+                    k,
+                    obs_times=ot,
+                    obs_values=ov,
+                    ctrl_times=ct,
+                    ctrl_values=cv,
+                )
+
+        elif isinstance(config, DiscreteTimeSmootherConfigs):
+            discrete_config = cast(DiscreteSmootherConfig, config)
+            if discrete_config.filter_source == "cuthbert":
+                output_kind = "cuthbert"
+
+                def compute_output(dyn, ot, ov, ct, cv, k):
+                    return compute_cuthbert_smoother(
+                        dyn,
+                        discrete_config,
+                        k,
+                        obs_times=ot,
+                        obs_values=ov,
+                        ctrl_times=ct,
+                        ctrl_values=cv,
+                    )
+
+            elif discrete_config.filter_source == "cd_dynamax":
+                output_kind = "cd_dynamax_discrete"
+
+                def compute_output(dyn, ot, ov, ct, cv, k):
+                    return compute_cd_dynamax_discrete_smoother(
+                        dyn,
+                        discrete_config,
+                        obs_times=ot,
+                        obs_values=ov,
+                        ctrl_times=ct,
+                        ctrl_values=cv,
+                    )
+
+            else:
+                raise ValueError(
+                    f"Unknown filter source: {discrete_config.filter_source}"
+                )
+        else:
+            raise ValueError(
+                f"Unsupported smoother config for plate: {type(config).__name__}"
+            )
+
+        if key is not None:
+            if not jnp.issubdtype(key.dtype, jax.dtypes.prng_key):
+                key = jax.random.wrap_key_data(key)
+            total = math.prod(plate_shapes)
+            split_keys = jax.random.split(key, total)
+            keys = split_keys.reshape(*plate_shapes, *split_keys.shape[1:])
+        else:
+            keys = None
+
+        dyn_axes = _make_plate_in_axes(dynamics, plate_shapes)
+        ot_axis = _array_plate_axis(obs_times, plate_shapes)
+        ov_axis = _array_plate_axis(obs_values, plate_shapes)
+        ct_axis = _array_plate_axis(ctrl_times, plate_shapes)
+        cv_axis = _array_plate_axis(ctrl_values, plate_shapes)
+        _validate_batched_plate_alignment(
+            dynamics,
+            plate_shapes,
+            obs_times=obs_times,
+            obs_values=obs_values,
+            ctrl_times=ctrl_times,
+            ctrl_values=ctrl_values,
+        )
+        k_axis = 0 if keys is not None else None
+
+        vmapped = compute_output
+        for _ in plate_shapes:
+            vmapped = jax.vmap(
+                vmapped,
+                in_axes=(dyn_axes, ot_axis, ov_axis, ct_axis, cv_axis, k_axis),
+            )
+
+        outputs = vmapped(
+            dynamics,
+            obs_times,
+            obs_values,
+            ctrl_times,
+            ctrl_values,
+            keys,
+        )
+
+        if output_kind in {"continuous", "cd_dynamax_discrete"}:
+            marginal_logliks = outputs.marginal_loglik
+        elif output_kind == "cuthbert":
+            marginal_logliks, states = outputs
+        else:
+            raise ValueError(f"Unsupported batched output kind: {output_kind}")
+
+        numpyro.factor(f"{name}_marginal_log_likelihood", marginal_logliks)
+        numpyro.deterministic(f"{name}_marginal_loglik", marginal_logliks)
+
+        if output_kind == "continuous":
+            return _posterior_sequence_to_dists(
+                outputs,
+                means_attr="smoothed_means",
+                covariances_attr="smoothed_covariances",
+                plate_shapes=plate_shapes,
+                particle_mode=False,
+                missing_message=(
+                    "Smoothed means/covariances were unavailable for a Gaussian rollout path."
+                ),
+            )
+        if output_kind == "cd_dynamax_discrete":
+            return _posterior_sequence_to_dists(
+                outputs,
+                means_attr="smoothed_means",
+                covariances_attr="smoothed_covariances",
+                plate_shapes=plate_shapes,
+                particle_mode=False,
+                missing_message=(
+                    "Smoothed means/covariances were unavailable for a Gaussian rollout path."
+                ),
+            )
+        if output_kind == "cuthbert":
+            return _cholesky_state_sequence_to_dists(
+                states,
+                particle_mode=isinstance(config, PFSmootherConfig),
+                plate_shapes=plate_shapes,
+            )
+
+        raise ValueError(f"Unsupported batched output kind: {output_kind}")
+
+
+def _smooth_discrete_time(
+    name: str,
+    dynamics: DynamicalModel,
+    smoother_config: DiscreteSmootherConfig,
+    key: jax.Array | None = None,
+    *,
+    obs_times: jax.Array,
+    obs_values: jax.Array,
+    ctrl_times=None,
+    ctrl_values=None,
+    **kwargs,
+) -> list[numpyro.distributions.Distribution]:
+    """Discrete-time marginal likelihood via cuthbert or cd-dynamax smoothers."""
+
+    if isinstance(smoother_config, UKFSmootherConfig) and (
+        smoother_config.filter_source == "cuthbert"
+    ):
+        raise ValueError(
+            "UKF smoothing is not available in cuthbert. "
+            "Use UKFSmootherConfig(filter_source='cd_dynamax') or a cuthbert-supported smoother "
+            "(KFSmootherConfig, EKFSmootherConfig, PFSmootherConfig)."
+        )
+
+    if isinstance(smoother_config, PFSmootherConfig) and (
+        smoother_config.filter_source != "cuthbert"
+    ):
+        raise ValueError(
+            "PFSmootherConfig is only supported with filter_source='cuthbert'."
+        )
+
+    if smoother_config.filter_source == "cd_dynamax":
+        return run_cd_dynamax_discrete_smoother(
+            name,
+            dynamics,
+            smoother_config,
+            obs_times=obs_times,
+            obs_values=obs_values,
+            ctrl_times=ctrl_times,
+            ctrl_values=ctrl_values,
+            **kwargs,
+        )
+
+    if smoother_config.filter_source == "cuthbert":
+        if isinstance(smoother_config, UKFSmootherConfig):
+            raise ValueError(
+                "UKF smoothing is not available in cuthbert. "
+                "Use UKFSmootherConfig(filter_source='cd_dynamax') or a cuthbert-supported smoother "
+                "(KFSmootherConfig, EKFSmootherConfig, PFSmootherConfig)."
+            )
+        return run_cuthbert_discrete_smoother(
+            name,
+            dynamics,
+            smoother_config,
+            key=key,
+            obs_times=obs_times,
+            obs_values=obs_values,
+            ctrl_times=ctrl_times,
+            ctrl_values=ctrl_values,
+            **kwargs,
+        )
+
+    raise ValueError(f"Unknown filter source: {smoother_config.filter_source}")
+
+
+def _smooth_continuous_time(
+    name: str,
+    dynamics: DynamicalModel,
+    smoother_config: ContinuousSmootherConfig,
+    key: jax.Array | None = None,
+    *,
+    obs_times: jax.Array,
+    obs_values: jax.Array,
+    ctrl_times=None,
+    ctrl_values=None,
+    **kwargs,
+) -> list[numpyro.distributions.Distribution]:
+    """Continuous-time marginal likelihood via CD-Dynamax smoothers."""
+    if smoother_config.filter_source != "cd_dynamax":
+        raise ValueError(
+            f"{type(smoother_config).__name__} supports only filter_source='cd_dynamax'."
+        )
+
+    return run_continuous_smoother(
+        name,
+        dynamics,
+        smoother_config,
+        key=key,
+        obs_times=obs_times,
+        obs_values=obs_values,
+        ctrl_times=ctrl_times,
+        ctrl_values=ctrl_values,
+        **kwargs,
+    )
+
+
+__all__ = [
+    "ContinuousTimeEKFSmootherConfig",
+    "ContinuousTimeKFSmootherConfig",
+    "EKFSmootherConfig",
+    "KFSmootherConfig",
+    "PFSmootherConfig",
+    "Smoother",
+    "BaseSmootherConfig",
+    "UKFSmootherConfig",
+]
diff --git a/dynestyx/simulators.py b/dynestyx/simulators.py
index cb5fac1b..6869349a 100644
--- a/dynestyx/simulators.py
+++ b/dynestyx/simulators.py
@@ -254,38 +254,47 @@ def _run_single_member_simulation(
         predict_times=None,
         filtered_times=None,
         filtered_dists=None,
+        smoothed_times=None,
+        smoothed_dists=None,
+        _posterior_rollout_final_only: bool = False,
         **kwargs,
     ) -> dict[str, Array] | None:
         """Run simulator logic for one unbatched member and return trajectories."""
         dynamics = _ensure_continuous_bm_dim(dynamics)
 
+        use_smoothed_rollout = smoothed_times is not None or smoothed_dists is not None
+        if use_smoothed_rollout and (
+            filtered_times is not None or filtered_dists is not None
+        ):
+            raise ValueError(
+                "Smoothed rollout metadata was provided alongside filtered rollout "
+                "metadata. When smoothed_times or smoothed_dists is provided, "
+                "filtered_times and filtered_dists must be None."
+            )
+        rollout_times = smoothed_times if use_smoothed_rollout else filtered_times
+        rollout_dists = smoothed_dists if use_smoothed_rollout else filtered_dists
+        rollout_label = "smoothed" if use_smoothed_rollout else "filtered"
+
         if (
-            filtered_times is not None
-            and filtered_dists is None
+            rollout_times is not None
+            and rollout_dists is None
             and predict_times is not None
         ):
             raise ValueError(
-                "Rollout requested with filtered_times but missing filtered_dists. "
-                "Plate-aware rollout requires filtered distributions from the filter."
+                f"Rollout requested with {rollout_label}_times but missing {rollout_label}_dists. "
+                "Plate-aware rollout requires posterior distributions from Filter/Smoother."
             )
 
         # Need times to simulate: predict_times or obs_times
-        # For filter rollout, need predict_times
+        # For posterior rollout, need predict_times
         if predict_times is None:
-            if obs_times is None or filtered_times is not None:
+            if obs_times is None or rollout_times is not None:
                 return None
 
-        filter_rollout = filtered_times is not None and filtered_dists is not None
+        posterior_rollout = rollout_times is not None and rollout_dists is not None
 
-        if filter_rollout:
-            _validate_site_sorting(filtered_times, name="filtered_times")
-            n_pred = len(predict_times)
-
-            # Build segment ids on host once.
-            # seg_id == -1 means "before first filtered time" (use model prior).
-            pt_host = np.asarray(jax.device_get(predict_times))
-            ft_host = np.asarray(jax.device_get(filtered_times))
-            seg_ids_host = np.searchsorted(ft_host, pt_host, side="right") - 1
+        if posterior_rollout:
+            _validate_site_sorting(rollout_times, name=f"{rollout_label}_times")
 
             def _ctrl_for_segment(sub_times):
                 if ctrl_times is None or ctrl_values is None:
@@ -297,22 +306,50 @@ def _dynamics_for_segment(seg_id: int):
                 if seg_id < 0:
                     return dynamics, f"{name}_0"
 
-                filtered_time = filtered_times[seg_id]
-                filtered_dist = filtered_dists[seg_id]
-                dynamics_with_filtered_time = eqx.tree_at(
+                posterior_time = rollout_times[seg_id]
+                posterior_dist = rollout_dists[seg_id]
+                dynamics_with_posterior_time = eqx.tree_at(
                     lambda m: m.t0,
                     dynamics,
-                    filtered_time,
+                    posterior_time,
                     is_leaf=lambda x: x is None,
                 )
                 dynamics_seg = eqx.tree_at(
                     lambda m: m.initial_condition,
-                    dynamics_with_filtered_time,
-                    filtered_dist,
+                    dynamics_with_posterior_time,
+                    posterior_dist,
                     is_leaf=lambda x: x is None,
                 )
                 return dynamics_seg, f"{name}_{seg_id + 1}"
 
+            if _posterior_rollout_final_only:
+                dynamics_seg, seg_name = _dynamics_for_segment(0)
+                ctrl_t_seg, ctrl_v_seg = _ctrl_for_segment(predict_times)
+                seg_result = self._simulate(
+                    seg_name,
+                    dynamics_seg,
+                    obs_times=None,
+                    obs_values=None,
+                    ctrl_times=ctrl_t_seg,
+                    ctrl_values=ctrl_v_seg,
+                    predict_times=predict_times,
+                )
+                results = {
+                    "predicted_states": seg_result["states"],
+                    "predicted_observations": seg_result["observations"],
+                }
+                n_sim_out = results["predicted_states"].shape[0]
+                results["predicted_times"] = _tile_times(predict_times, n_sim_out)
+                return results
+
+            n_pred = len(predict_times)
+
+            # Build segment ids on host once.
+            # seg_id == -1 means "before first posterior time" (use model prior).
+            pt_host = np.asarray(jax.device_get(predict_times))
+            ft_host = np.asarray(jax.device_get(rollout_times))
+            seg_ids_host = np.searchsorted(ft_host, pt_host, side="right") - 1
+
             seg_results = []
             seg_masks = []
             # Simulate one segment per present anchor (skip empty segments).
@@ -386,6 +423,9 @@ def _run_plated_simulation(
         predict_times=None,
         filtered_times=None,
         filtered_dists=None,
+        smoothed_times=None,
+        smoothed_dists=None,
+        _posterior_rollout_final_only: bool = False,
         **kwargs,
     ) -> dict[str, Array] | None:
         """Run simulator over all plate members and stack outputs.
@@ -403,8 +443,9 @@ def _run_plated_simulation(
                 ctrl_values,
                 predict_times,
                 filtered_times,
+                smoothed_times,
             ),
-            dists=filtered_dists,
+            dists=smoothed_dists if smoothed_dists is not None else filtered_dists,
         ):
             raise ValueError(
                 "Plate simulator received plate_shapes but no plate-batched dynamics/data "
@@ -454,6 +495,9 @@ def _run_plated_simulation(
             member_filtered_times = _slice_array_for_plate_member(
                 filtered_times, plate_shapes, plate_idx
             )
+            member_smoothed_times = _slice_array_for_plate_member(
+                smoothed_times, plate_shapes, plate_idx
+            )
 
             # Same distribution slicing logic as above, but for prediction.
             member_filtered_dists = None
@@ -462,6 +506,12 @@ def _run_plated_simulation(
                     _slice_dist_for_plate_member(d, plate_shapes, plate_idx)
                     for d in filtered_dists
                 ]
+            member_smoothed_dists = None
+            if smoothed_dists is not None:
+                member_smoothed_dists = [
+                    _slice_dist_for_plate_member(d, plate_shapes, plate_idx)
+                    for d in smoothed_dists
+                ]
 
             # To perform inference, we need to suspend the active numpyro.plate frames
             # This is because the simulator has unguarded numpyro.sample statements inside,
@@ -477,6 +527,9 @@ def _run_plated_simulation(
                     predict_times=member_predict_times,
                     filtered_times=member_filtered_times,
                     filtered_dists=member_filtered_dists,
+                    smoothed_times=member_smoothed_times,
+                    smoothed_dists=member_smoothed_dists,
+                    _posterior_rollout_final_only=_posterior_rollout_final_only,
                     **kwargs,
                 )
 
@@ -514,8 +567,13 @@ def _sample_ds(
         predict_times=None,
         filtered_times=None,
         filtered_dists=None,
+        smoothed_times=None,
+        smoothed_dists=None,
         **kwargs,
     ) -> FunctionOfTime:
+        posterior_rollout_final_only = kwargs.pop(
+            "_posterior_rollout_final_only", False
+        )
         if plate_shapes:
             results = self._run_plated_simulation(
                 name,
@@ -528,6 +586,9 @@ def _sample_ds(
                 predict_times=predict_times,
                 filtered_times=filtered_times,
                 filtered_dists=filtered_dists,
+                smoothed_times=smoothed_times,
+                smoothed_dists=smoothed_dists,
+                _posterior_rollout_final_only=posterior_rollout_final_only,
                 **kwargs,
             )
         else:
@@ -541,6 +602,9 @@ def _sample_ds(
                 predict_times=predict_times,
                 filtered_times=filtered_times,
                 filtered_dists=filtered_dists,
+                smoothed_times=smoothed_times,
+                smoothed_dists=smoothed_dists,
+                _posterior_rollout_final_only=posterior_rollout_final_only,
                 **kwargs,
             )
 
diff --git a/mkdocs.yml b/mkdocs.yml
index 68b30d89..16efd33f 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -28,6 +28,8 @@ nav:
       - Continuous-time dynamics (ODEs): tutorials/gentle_intro/06b_odes.ipynb
       - Hidden Markov Models: tutorials/gentle_intro/07_hmm.ipynb
       - Hierarchical inference: tutorials/gentle_intro/08_hierarchical_inference.ipynb
+      - Discrete-time smoothing: tutorials/gentle_intro/09_discrete_smoothing.ipynb
+      - Continuous-time smoothing: tutorials/gentle_intro/10_continuous_smoothing.ipynb
   - Examples and Deep Dives:
     - tutorials.md
     - Examples:
@@ -64,6 +66,8 @@ nav:
       - Inference:
         - Filters: api_reference/public/inference/filters.md
         - FilterConfigs: api_reference/public/inference/filter_configs.md
+        - Smoothers: api_reference/public/inference/smoothers.md
+        - SmootherOptions: api_reference/public/inference/smoother_configs.md
         - MCMC: api_reference/public/inference/mcmc.md
         - MCMCConfigs: api_reference/public/inference/mcmc_configs.md
       - Discretizers:
@@ -90,6 +94,9 @@ nav:
         - Filters: api_reference/developer/inference/filters.md
         - Checkers: api_reference/developer/inference/checkers.md
         - FilterConfigs: api_reference/developer/inference/filter_configs.md
+        - Smoothers: api_reference/developer/inference/smoothers.md
+        - SmootherOptions: api_reference/developer/inference/smoother_configs.md
+        - Smoothing Handoff: smoothing_handoff.md
         - MCMC: api_reference/developer/inference/mcmc.md
         - MCMCConfigs: api_reference/developer/inference/mcmc_configs.md
         - BlackJAX Integration: api_reference/developer/inference/blackjax_integration.md
diff --git a/tests/test_distribution_utils.py b/tests/test_distribution_utils.py
new file mode 100644
index 00000000..4ce5a967
--- /dev/null
+++ b/tests/test_distribution_utils.py
@@ -0,0 +1,109 @@
+from types import SimpleNamespace
+
+import jax
+import jax.numpy as jnp
+
+from dynestyx.inference.distribution_utils import (
+    _categorical_log_probs_to_dists,
+    _cholesky_state_sequence_to_dists,
+    _gaussian_sequence_to_dists,
+    _particle_sequence_to_dists,
+    _posterior_sequence_to_dists,
+)
+
+
+def test_gaussian_sequence_to_dists_unbatched():
+    means = jnp.array([[0.0, 1.0], [2.0, 3.0]])
+    covs = jnp.broadcast_to(jnp.eye(2), (2, 2, 2))
+
+    dists = _gaussian_sequence_to_dists(means, covs)
+
+    assert len(dists) == 2
+    assert dists[0].batch_shape == ()
+    assert dists[0].event_shape == (2,)
+    assert jnp.allclose(dists[1].loc, jnp.array([2.0, 3.0]))
+
+
+def test_gaussian_sequence_to_dists_plate_batched():
+    means = jnp.arange(12.0).reshape(2, 3, 2)
+    covs = jnp.broadcast_to(jnp.eye(2), (2, 3, 2, 2))
+
+    dists = _gaussian_sequence_to_dists(means, covs, plate_shapes=(2,))
+
+    assert len(dists) == 3
+    assert dists[0].batch_shape == (2,)
+    assert dists[0].event_shape == (2,)
+    assert dists[0].loc.shape == (2, 2)
+
+
+def test_particle_sequence_to_dists_normalizes_and_plate_batches():
+    particles = jnp.arange(48.0).reshape(2, 3, 4, 2)
+    log_weights = jnp.zeros((2, 3, 4))
+
+    dists = _particle_sequence_to_dists(
+        particles,
+        log_weights,
+        plate_shapes=(2,),
+    )
+
+    assert len(dists) == 3
+    assert dists[0].batch_shape == (2,)
+    assert dists[0].event_shape == (2,)
+    assert dists[0].particles.shape == (2, 4, 2)
+    assert jnp.allclose(jnp.exp(dists[0].log_weights).sum(axis=-1), jnp.ones(2))
+
+
+def test_cholesky_state_sequence_to_dists_gaussian():
+    states = SimpleNamespace(
+        mean=jnp.array([[0.0, 1.0], [2.0, 3.0]]),
+        chol_cov=jnp.broadcast_to(2.0 * jnp.eye(2), (2, 2, 2)),
+    )
+
+    dists = _cholesky_state_sequence_to_dists(states, particle_mode=False)
+
+    assert len(dists) == 2
+    assert jnp.allclose(dists[0].covariance_matrix, 4.0 * jnp.eye(2))
+
+
+def test_categorical_log_probs_to_dists_plate_batched():
+    logits = jnp.arange(24.0).reshape(2, 3, 4)
+    log_probs = jax.nn.log_softmax(logits, axis=-1)
+
+    dists = _categorical_log_probs_to_dists(log_probs, plate_shapes=(2,))
+
+    assert len(dists) == 3
+    assert dists[0].batch_shape == (2,)
+    assert dists[0].probs.shape == (2, 4)
+    assert jnp.allclose(dists[0].probs.sum(axis=-1), jnp.ones(2))
+
+
+def test_posterior_sequence_to_dists_uses_attrs_and_missing_empty():
+    posterior = SimpleNamespace(
+        filtered_means=jnp.array([[0.0], [1.0]]),
+        filtered_covariances=jnp.ones((2, 1, 1)),
+    )
+
+    dists = _posterior_sequence_to_dists(
+        posterior,
+        means_attr="filtered_means",
+        covariances_attr="filtered_covariances",
+        particle_mode=False,
+    )
+
+    assert len(dists) == 2
+    assert dists[1].loc.shape == (1,)
+
+    missing_posterior = SimpleNamespace(
+        filtered_means=None,
+        filtered_covariances=None,
+    )
+    assert (
+        _posterior_sequence_to_dists(
+            missing_posterior,
+            means_attr="filtered_means",
+            covariances_attr="filtered_covariances",
+            particle_mode=False,
+            missing="empty",
+        )
+        == []
+    )
diff --git a/tests/test_filters.py b/tests/test_filters.py
index 45f2ca96..e52987db 100644
--- a/tests/test_filters.py
+++ b/tests/test_filters.py
@@ -177,6 +177,24 @@ def test_compute_cuthbert_filter_returns_observation_aligned_states(filter_confi
         assert states.chol_cov.shape[0] == len(obs_times)
 
 
+def test_compute_cuthbert_filter_can_return_raw_cuthbert_states():
+    obs_times, obs_values = _make_discrete_lti_data()
+    dynamics = _make_discrete_lti_dynamics()
+
+    _, states = compute_cuthbert_filter(
+        dynamics,
+        KFConfig(filter_source="cuthbert"),
+        obs_times=obs_times,
+        obs_values=obs_values,
+        align_to_observations=False,
+    )
+
+    assert states.log_normalizing_constant.shape[0] == len(obs_times) + 1
+    assert states.model_inputs.y.shape[0] == len(obs_times) + 1
+    assert states.mean.shape[0] == len(obs_times) + 1
+    assert states.chol_cov.shape[0] == len(obs_times) + 1
+
+
 @pytest.mark.parametrize(
     "filter_config",
     [
diff --git a/tests/test_science/test_lti_continuous_smoothing.py b/tests/test_science/test_lti_continuous_smoothing.py
new file mode 100644
index 00000000..a0b5eac6
--- /dev/null
+++ b/tests/test_science/test_lti_continuous_smoothing.py
@@ -0,0 +1,153 @@
+"""Science tests for LTI_continuous smoothing with continuous-time KF."""
+
+import os
+
+import arviz as az
+import jax.numpy as jnp
+import jax.random as jr
+import pytest
+from numpyro.infer import MCMC, NUTS, Predictive
+
+from dynestyx.inference.smoother_configs import ContinuousTimeKFSmootherConfig
+from dynestyx.inference.smoothers import Smoother
+from dynestyx.simulators import Simulator
+from tests.models import continuous_time_lti_simplified_model
+from tests.test_utils import get_output_dir
+
+SAVE_FIG = True
+
+
+def _profiled_arange(
+    *, fast_stop: float, fast_step: float, science_stop: float, science_step: float
+):
+    fast_tests = os.environ.get("DYNESTYX_FAST_TESTS", "1").lower() not in {
+        "0",
+        "false",
+        "no",
+    }
+    stop = fast_stop if fast_tests else science_stop
+    step = fast_step if fast_tests else science_step
+    return jnp.arange(start=0.0, stop=stop, step=step)
+
+
+@pytest.mark.parametrize("num_samples", [250])
+def test_continuous_lti_smoother_mcmc_science(num_samples: int):
+    """Infer rho with Smoother and validate smoothed trajectories."""
+    rng_key = jr.PRNGKey(0)
+    data_init_key, mcmc_key, post_key = jr.split(rng_key, 3)
+
+    true_rho = 2.0
+    obs_times = _profiled_arange(
+        fast_stop=3.0,
+        fast_step=0.2,
+        science_stop=10.0,
+        science_step=0.05,
+    )
+
+    predictive = Predictive(
+        continuous_time_lti_simplified_model,
+        params={"rho": jnp.array(true_rho)},
+        num_samples=1,
+        exclude_deterministic=False,
+    )
+
+    with Simulator():
+        synthetic = predictive(data_init_key, predict_times=obs_times)
+
+    obs_values = synthetic["f_observations"].squeeze(0).squeeze(0)
+    true_states = synthetic["f_states"].squeeze(0).squeeze(0)
+    plot_times = synthetic["f_times"].squeeze(0).squeeze(0)
+
+    output_dir = get_output_dir("test_lti_continuous_smoothing_simplified")
+
+    if SAVE_FIG and output_dir is not None:
+        import matplotlib.pyplot as plt
+
+        plt.plot(plot_times, true_states[:, 0], label="true x[0]")
+        plt.plot(plot_times, true_states[:, 1], label="true x[1]")
+        plt.plot(plot_times, obs_values[:, 0], "--", label="observations")
+        plt.legend()
+        plt.savefig(output_dir / "data_generation.png", dpi=150, bbox_inches="tight")
+        plt.close()
+
+    config = ContinuousTimeKFSmootherConfig(
+        record_smoothed_states_mean=True,
+        record_smoothed_states_cov_diag=True,
+    )
+
+    def data_conditioned_model():
+        with Smoother(smoother_config=config):
+            return continuous_time_lti_simplified_model(
+                obs_times=obs_times,
+                obs_values=obs_values,
+            )
+
+    mcmc = MCMC(
+        NUTS(data_conditioned_model),
+        num_samples=num_samples,
+        num_warmup=num_samples,
+    )
+    mcmc.run(mcmc_key)
+
+    posterior_samples = mcmc.get_samples()
+    assert "rho" in posterior_samples
+
+    posterior_rho = posterior_samples["rho"]
+    assert len(posterior_rho) == num_samples
+    assert not jnp.isnan(posterior_rho).any()
+    assert not jnp.isinf(posterior_rho).any()
+
+    if SAVE_FIG and output_dir is not None:
+        import matplotlib.pyplot as plt
+
+        az.plot_posterior(posterior_rho, hdi_prob=0.95, ref_val=true_rho)
+        plt.savefig(output_dir / "posterior_rho.png", dpi=150, bbox_inches="tight")
+        plt.close()
+
+    post_pred = Predictive(
+        data_conditioned_model,
+        params={"rho": posterior_rho.mean()},
+        num_samples=1,
+        exclude_deterministic=False,
+    )
+    post_out = post_pred(post_key)
+
+    assert "f_smoothed_states_mean" in post_out
+    assert "f_smoothed_states_cov_diag" in post_out
+
+    smoothed_mean = post_out["f_smoothed_states_mean"].squeeze(0)
+    smoothed_cov_diag = post_out["f_smoothed_states_cov_diag"].squeeze(0)
+    assert smoothed_mean.shape == true_states.shape
+    assert smoothed_cov_diag.shape == true_states.shape
+    assert jnp.all(jnp.isfinite(smoothed_mean))
+    assert jnp.all(jnp.isfinite(smoothed_cov_diag))
+
+    rmse_x1 = jnp.sqrt(jnp.mean((smoothed_mean[:, 1] - true_states[:, 1]) ** 2))
+
+    if SAVE_FIG and output_dir is not None:
+        import matplotlib.pyplot as plt
+
+        sigma_x1 = jnp.sqrt(jnp.maximum(smoothed_cov_diag[:, 1], 1e-9))
+        plt.plot(plot_times, true_states[:, 1], label="true x[1]")
+        plt.plot(plot_times, smoothed_mean[:, 1], label="smoothed x[1]")
+        plt.plot(plot_times, obs_values[:, 0], "--", label="observations")
+        plt.fill_between(
+            plot_times,
+            smoothed_mean[:, 1] - 2.0 * sigma_x1,
+            smoothed_mean[:, 1] + 2.0 * sigma_x1,
+            alpha=0.2,
+            label="smoothed +-2 sigma",
+        )
+        plt.legend()
+        plt.savefig(output_dir / "smoothed_state_x1.png", dpi=150, bbox_inches="tight")
+        plt.close()
+
+    assert jnp.abs(posterior_rho.mean() - true_rho) < 2.0
+    assert float(rmse_x1) < 2.0
+
+    hdi_data = az.hdi(posterior_rho, hdi_prob=0.95)
+    hdi_min = hdi_data["x"].sel(hdi="lower").item()
+    hdi_max = hdi_data["x"].sel(hdi="higher").item()
+    assert hdi_min <= true_rho <= hdi_max, (
+        f"True rho {true_rho} not in HDI {hdi_min}, {hdi_max}"
+    )
diff --git a/tests/test_science/test_lti_discrete_smoothing.py b/tests/test_science/test_lti_discrete_smoothing.py
new file mode 100644
index 00000000..3f8fd58e
--- /dev/null
+++ b/tests/test_science/test_lti_discrete_smoothing.py
@@ -0,0 +1,176 @@
+"""Science tests for LTI_discrete smoothing with KF and PF backends."""
+
+import os
+
+import arviz as az
+import jax.numpy as jnp
+import jax.random as jr
+import pytest
+from numpyro.infer import MCMC, NUTS, BarkerMH, Predictive
+
+from dynestyx.inference.smoother_configs import KFSmootherConfig, PFSmootherConfig
+from dynestyx.inference.smoothers import Smoother
+from dynestyx.simulators import DiscreteTimeSimulator
+from tests.models import discrete_time_lti_simplified_model
+from tests.test_utils import get_output_dir
+
+SAVE_FIG = True
+
+
+def _profiled_arange(
+    *, fast_stop: float, fast_step: float, science_stop: float, science_step: float
+):
+    fast_tests = os.environ.get("DYNESTYX_FAST_TESTS", "1").lower() not in {
+        "0",
+        "false",
+        "no",
+    }
+    stop = fast_stop if fast_tests else science_stop
+    step = fast_step if fast_tests else science_step
+    return jnp.arange(start=0.0, stop=stop, step=step)
+
+
+@pytest.mark.parametrize("smoother_type", ["kf", "pf"])
+@pytest.mark.parametrize("num_samples", [250])
+def test_discrete_lti_smoother_mcmc_science(smoother_type: str, num_samples: int):
+    """Infer alpha with Smoother and validate smoothed trajectories."""
+    rng_key = jr.PRNGKey(0)
+    data_init_key, mcmc_key, post_key = jr.split(rng_key, 3)
+
+    true_alpha = 0.4
+    obs_times = _profiled_arange(
+        fast_stop=30.0,
+        fast_step=1.0,
+        science_stop=200.0,
+        science_step=1.0,
+    )
+
+    predictive = Predictive(
+        discrete_time_lti_simplified_model,
+        params={"alpha": jnp.array(true_alpha)},
+        num_samples=1,
+        exclude_deterministic=False,
+    )
+
+    with DiscreteTimeSimulator():
+        synthetic = predictive(data_init_key, predict_times=obs_times)
+
+    obs_values = synthetic["f_observations"].squeeze(0).squeeze(0)
+    true_states = synthetic["f_states"].squeeze(0).squeeze(0)
+    plot_times = synthetic["f_times"].squeeze(0).squeeze(0)
+
+    output_dir_name = f"test_lti_discrete_smoothing_simplified_smoother_{smoother_type}"
+    output_dir = get_output_dir(output_dir_name)
+
+    if SAVE_FIG and output_dir is not None:
+        import matplotlib.pyplot as plt
+
+        plt.plot(plot_times, true_states[:, 0], label="true x[0]")
+        plt.plot(plot_times, true_states[:, 1], label="true x[1]")
+        plt.plot(
+            plot_times,
+            obs_values[:, 0],
+            label="observations",
+            linestyle="--",
+        )
+        plt.legend()
+        plt.savefig(output_dir / "data_generation.png", dpi=150, bbox_inches="tight")
+        plt.close()
+
+    config: KFSmootherConfig | PFSmootherConfig
+    kernel_cls: type[NUTS] | type[BarkerMH]
+    if smoother_type == "kf":
+        config = KFSmootherConfig(
+            filter_source="cd_dynamax",
+            record_smoothed_states_mean=True,
+            record_smoothed_states_cov_diag=True,
+        )
+        kernel_cls = NUTS
+    else:
+        config = PFSmootherConfig(
+            filter_source="cuthbert",
+            n_particles=256,
+            record_smoothed_states_mean=True,
+            record_smoothed_states_cov_diag=True,
+        )
+        kernel_cls = BarkerMH
+
+    def data_conditioned_model():
+        with Smoother(smoother_config=config):
+            return discrete_time_lti_simplified_model(
+                obs_times=obs_times,
+                obs_values=obs_values,
+            )
+
+    mcmc = MCMC(
+        kernel_cls(data_conditioned_model),
+        num_samples=num_samples,
+        num_warmup=num_samples,
+    )
+    mcmc.run(mcmc_key)
+
+    posterior_samples = mcmc.get_samples()
+    assert "alpha" in posterior_samples
+
+    posterior_alpha = posterior_samples["alpha"]
+    assert len(posterior_alpha) == num_samples
+    assert not jnp.isnan(posterior_alpha).any()
+    assert not jnp.isinf(posterior_alpha).any()
+
+    if SAVE_FIG and output_dir is not None:
+        import matplotlib.pyplot as plt
+
+        az.plot_posterior(posterior_alpha, hdi_prob=0.95, ref_val=true_alpha)
+        plt.savefig(output_dir / "posterior_alpha.png", dpi=150, bbox_inches="tight")
+        plt.close()
+
+    post_pred = Predictive(
+        data_conditioned_model,
+        params={"alpha": posterior_alpha.mean()},
+        num_samples=1,
+        exclude_deterministic=False,
+    )
+    post_out = post_pred(post_key)
+
+    assert "f_smoothed_states_mean" in post_out
+    assert "f_smoothed_states_cov_diag" in post_out
+
+    smoothed_mean = post_out["f_smoothed_states_mean"].squeeze(0)
+    smoothed_cov_diag = post_out["f_smoothed_states_cov_diag"].squeeze(0)
+    assert smoothed_mean.shape == true_states.shape
+    assert smoothed_cov_diag.shape == true_states.shape
+    assert jnp.all(jnp.isfinite(smoothed_mean))
+    assert jnp.all(jnp.isfinite(smoothed_cov_diag))
+
+    rmse_x0 = jnp.sqrt(jnp.mean((smoothed_mean[:, 0] - true_states[:, 0]) ** 2))
+
+    if SAVE_FIG and output_dir is not None:
+        import matplotlib.pyplot as plt
+
+        sigma_x0 = jnp.sqrt(jnp.maximum(smoothed_cov_diag[:, 0], 1e-9))
+        plt.plot(plot_times, true_states[:, 0], label="true x[0]")
+        plt.plot(plot_times, smoothed_mean[:, 0], label="smoothed x[0]")
+        plt.plot(plot_times, obs_values[:, 0], "--", label="observations")
+        plt.fill_between(
+            plot_times,
+            smoothed_mean[:, 0] - 2.0 * sigma_x0,
+            smoothed_mean[:, 0] + 2.0 * sigma_x0,
+            alpha=0.2,
+            label="smoothed +-2 sigma",
+        )
+        plt.legend()
+        plt.savefig(output_dir / "smoothed_state_x0.png", dpi=150, bbox_inches="tight")
+        plt.close()
+
+    alpha_tol = 0.15 if smoother_type == "kf" else 0.25
+    rmse_tol = 0.8 if smoother_type == "kf" else 1.2
+
+    assert jnp.abs(posterior_alpha.mean() - true_alpha) < alpha_tol
+    assert float(rmse_x0) < rmse_tol
+
+    hdi_data = az.hdi(posterior_alpha, hdi_prob=0.95)
+    hdi_min = hdi_data["x"].sel(hdi="lower").item()
+    hdi_max = hdi_data["x"].sel(hdi="higher").item()
+    assert hdi_min <= true_alpha <= hdi_max, (
+        f"True alpha {true_alpha} not in HDI {hdi_min}, {hdi_max}"
+    )
diff --git a/tests/test_smoothers.py b/tests/test_smoothers.py
new file mode 100644
index 00000000..ca849056
--- /dev/null
+++ b/tests/test_smoothers.py
@@ -0,0 +1,435 @@
+import equinox as eqx
+import jax.numpy as jnp
+import jax.random as jr
+import numpyro.distributions as dist
+import pytest
+from numpyro.handlers import seed, trace
+from numpyro.infer import Predictive
+
+import dynestyx as dsx
+from dynestyx import DiscreteTimeSimulator, ODESimulator, Simulator, Smoother
+from dynestyx.inference.filter_configs import (
+    ContinuousTimeEnKFConfig,
+    EnKFConfig,
+    HMMConfig,
+)
+from dynestyx.inference.integrations.cuthbert.discrete_smoother import (
+    _pf_backward_sampling_fn,
+    compute_cuthbert_smoother,
+)
+from dynestyx.inference.smoother_configs import (
+    ContinuousTimeEKFSmootherConfig,
+    ContinuousTimeKFSmootherConfig,
+    EKFSmootherConfig,
+    KFSmootherConfig,
+    PFBackwardSamplingMethod,
+    PFSmootherConfig,
+)
+from tests.models import (
+    continuous_time_lti_simplified_model,
+    discrete_time_lti_simplified_model,
+    jumpy_controls_model_ode,
+)
+
+
+def _gen_obs_discrete():
+    rng = jr.PRNGKey(42)
+    obs_times = jnp.arange(0.0, 6.0, 1.0)
+    predict_times = jnp.arange(obs_times[-1], 9.0, 1.0)
+    with DiscreteTimeSimulator(n_simulations=1):
+        pred = Predictive(
+            discrete_time_lti_simplified_model,
+            params={"alpha": jnp.array(0.35)},
+            num_samples=1,
+            exclude_deterministic=False,
+        )
+        sim = pred(rng, predict_times=obs_times)
+    obs_values = sim["f_observations"][0, 0]
+    return obs_times, obs_values, predict_times
+
+
+def _gen_obs_ode():
+    rng = jr.PRNGKey(42)
+    obs_times = jnp.linspace(0.0, 0.5, 11)
+    predict_times = jnp.linspace(obs_times[-1], 1.0, 11)
+    ctrl_times = jnp.linspace(0.0, 1.0, 21)
+    ctrl_values = jnp.ones((len(ctrl_times),)) * 100
+    for i in range(1, len(ctrl_values), 2):
+        ctrl_values = ctrl_values.at[i].set(-ctrl_values[i])
+    with ODESimulator(n_simulations=1):
+        pred = Predictive(
+            jumpy_controls_model_ode,
+            params={},
+            num_samples=1,
+            exclude_deterministic=False,
+        )
+        sim = pred(
+            rng,
+            predict_times=obs_times,
+            ctrl_times=obs_times,
+            ctrl_values=ctrl_values[: len(obs_times)],
+        )
+    obs_values = sim["f_observations"][0, 0]
+    return obs_times, obs_values, predict_times, ctrl_times, ctrl_values
+
+
+def _make_discrete_lti_dynamics(alpha=0.35):
+    state_dim = 2
+    return dsx.LTI_discrete(
+        A=jnp.array([[alpha, 0.1], [0.1, 0.8]]),
+        Q=0.1 * jnp.eye(state_dim),
+        H=jnp.array([[1.0, 0.0]]),
+        R=jnp.array([[0.5**2]]),
+        B=jnp.array([[0.1], [0.0]]),
+        D=jnp.array([[0.01]]),
+    )
+
+
+@pytest.mark.parametrize(
+    "smoother_config",
+    [
+        KFSmootherConfig(filter_source="cuthbert"),
+        EKFSmootherConfig(filter_source="cuthbert"),
+        PFSmootherConfig(n_particles=16, filter_source="cuthbert"),
+    ],
+)
+def test_compute_cuthbert_smoother_returns_observation_aligned_states(
+    smoother_config,
+):
+    obs_times, obs_values, _ = _gen_obs_discrete()
+    dynamics = _make_discrete_lti_dynamics()
+
+    marginal_loglik, states = compute_cuthbert_smoother(
+        dynamics,
+        smoother_config,
+        key=jr.PRNGKey(2),
+        obs_times=obs_times,
+        obs_values=obs_values,
+    )
+
+    assert jnp.ndim(marginal_loglik) == 0
+    if isinstance(smoother_config, PFSmootherConfig):
+        assert states.particles.shape[0] == len(obs_times)
+        assert states.log_weights.shape[0] == len(obs_times)
+    else:
+        assert states.mean.shape[0] == len(obs_times)
+        assert states.chol_cov.shape[0] == len(obs_times)
+
+
+def test_predictive_smoother_discretetimesimulator_shapes():
+    obs_times, obs_values, predict_times = _gen_obs_discrete()
+
+    predictive = Predictive(
+        discrete_time_lti_simplified_model,
+        params={"alpha": jnp.array(0.35)},
+        num_samples=2,
+        exclude_deterministic=False,
+    )
+    with DiscreteTimeSimulator(n_simulations=2):
+        with Smoother(
+            smoother_config=KFSmootherConfig(
+                filter_source="cd_dynamax",
+                record_smoothed_states_mean=True,
+                record_smoothed_states_cov_diag=True,
+            )
+        ):
+            samples = predictive(
+                jr.PRNGKey(0),
+                obs_times=obs_times,
+                obs_values=obs_values,
+                predict_times=predict_times,
+            )
+
+    assert samples["f_predicted_states"].shape == (2, 2, len(predict_times), 2)
+    assert samples["f_predicted_times"].shape == (2, 2, len(predict_times))
+    assert samples["f_smoothed_states_mean"].shape == (2, len(obs_times), 2)
+    assert samples["f_smoothed_states_cov_diag"].shape == (2, len(obs_times), 2)
+    x0_keys = [k for k in samples if k.endswith("_x_0")]
+    assert "f_1_x_0" in x0_keys
+    assert "f_6_x_0" not in x0_keys
+
+
+def test_predictive_smoother_odesimulator_shapes():
+    obs_times, obs_values, predict_times, ctrl_times, ctrl_values = _gen_obs_ode()
+
+    predictive = Predictive(
+        jumpy_controls_model_ode,
+        params={},
+        num_samples=2,
+        exclude_deterministic=False,
+    )
+    with ODESimulator(n_simulations=2):
+        with Smoother(
+            smoother_config=ContinuousTimeEKFSmootherConfig(
+                record_smoothed_states_mean=True
+            )
+        ):
+            samples = predictive(
+                jr.PRNGKey(0),
+                obs_times=obs_times,
+                obs_values=obs_values,
+                predict_times=predict_times,
+                ctrl_times=ctrl_times,
+                ctrl_values=ctrl_values,
+            )
+
+    assert samples["f_predicted_states"].shape == (2, 2, len(predict_times), 1)
+    assert samples["f_predicted_times"].shape == (2, 2, len(predict_times))
+    assert samples["f_smoothed_states_mean"].shape == (2, len(obs_times), 1)
+
+
+@pytest.mark.parametrize(
+    "bad_predict_times",
+    [
+        jnp.arange(0.0, 6.0, 1.0),
+        jnp.arange(4.0, 8.0, 1.0),
+    ],
+)
+def test_predictive_smoother_discrete_rejects_in_window_predict_times(
+    bad_predict_times,
+):
+    obs_times, obs_values, _ = _gen_obs_discrete()
+
+    predictive = Predictive(
+        discrete_time_lti_simplified_model,
+        params={"alpha": jnp.array(0.35)},
+        num_samples=1,
+        exclude_deterministic=False,
+    )
+    with pytest.raises(
+        (ValueError, eqx.EquinoxRuntimeError),
+        match="Smoother prediction only supports predict_times >= max\\(obs_times\\)",
+    ):
+        with DiscreteTimeSimulator(n_simulations=1):
+            with Smoother(smoother_config=KFSmootherConfig(filter_source="cd_dynamax")):
+                predictive(
+                    jr.PRNGKey(0),
+                    obs_times=obs_times,
+                    obs_values=obs_values,
+                    predict_times=bad_predict_times,
+                )
+
+
+@pytest.mark.parametrize(
+    "bad_predict_times",
+    [
+        jnp.linspace(0.0, 0.5, 11),
+        jnp.linspace(0.45, 1.0, 12),
+    ],
+)
+def test_predictive_smoother_continuous_rejects_in_window_predict_times(
+    bad_predict_times,
+):
+    obs_times, obs_values, _, ctrl_times, ctrl_values = _gen_obs_ode()
+
+    predictive = Predictive(
+        jumpy_controls_model_ode,
+        params={},
+        num_samples=1,
+        exclude_deterministic=False,
+    )
+    total_times = jnp.union1d(obs_times, bad_predict_times)
+    bad_ctrl_values = jnp.ones((len(total_times),)) * 100
+    for i in range(1, len(bad_ctrl_values), 2):
+        bad_ctrl_values = bad_ctrl_values.at[i].set(-bad_ctrl_values[i])
+
+    with pytest.raises(
+        (ValueError, eqx.EquinoxRuntimeError),
+        match="Smoother prediction only supports predict_times >= max\\(obs_times\\)",
+    ):
+        with ODESimulator(n_simulations=1):
+            with Smoother(smoother_config=ContinuousTimeEKFSmootherConfig()):
+                predictive(
+                    jr.PRNGKey(0),
+                    obs_times=obs_times,
+                    obs_values=obs_values,
+                    predict_times=bad_predict_times,
+                    ctrl_times=total_times,
+                    ctrl_values=bad_ctrl_values,
+                )
+
+
+@pytest.mark.parametrize("backward_method", ["tracing", "exact"])
+def test_predictive_smoother_cuthbert_pf_backward_methods_and_override(
+    backward_method: PFBackwardSamplingMethod,
+):
+    obs_times, obs_values, predict_times = _gen_obs_discrete()
+
+    predictive = Predictive(
+        discrete_time_lti_simplified_model,
+        params={"alpha": jnp.array(0.35)},
+        num_samples=1,
+        exclude_deterministic=False,
+    )
+    with DiscreteTimeSimulator(n_simulations=1):
+        with Smoother(
+            smoother_config=PFSmootherConfig(
+                filter_source="cuthbert",
+                n_particles=32,
+                pf_backward_sampling_method=backward_method,
+                pf_mcmc_n_steps=3,
+                pf_n_smoother_particles=16,
+                record_smoothed_particles=True,
+                record_smoothed_log_weights=True,
+                record_smoothed_states_mean=True,
+            )
+        ):
+            samples = predictive(
+                jr.PRNGKey(0),
+                obs_times=obs_times,
+                obs_values=obs_values,
+                predict_times=predict_times,
+            )
+
+    assert samples["f_smoothed_particles"].shape[0] == 1
+    assert samples["f_smoothed_particles"].shape[1] == len(obs_times)
+    assert samples["f_smoothed_particles"].shape[2] == 16
+    assert samples["f_smoothed_log_weights"].shape[0] == 1
+    assert samples["f_smoothed_log_weights"].shape[1] == len(obs_times)
+    assert samples["f_smoothed_log_weights"].shape[2] == 16
+    assert jnp.all(jnp.isfinite(samples["f_smoothed_log_weights"]))
+
+
+def test_pf_smoother_mcmc_config_exposes_step_count():
+    config = PFSmootherConfig(
+        pf_backward_sampling_method="mcmc",
+        pf_mcmc_n_steps=7,
+    )
+    fn = _pf_backward_sampling_fn(config)
+    assert fn.keywords["n_steps"] == 7
+
+
+@pytest.mark.parametrize(
+    "invalid_config",
+    [
+        EnKFConfig(filter_source="cd_dynamax"),  # type: ignore[arg-type]
+        ContinuousTimeEnKFConfig(n_particles=8),
+        HMMConfig(),
+    ],
+)
+def test_smoother_rejects_filter_configs_as_invalid_input(invalid_config):
+    obs_times, obs_values, _ = _gen_obs_discrete()
+
+    with pytest.raises(
+        ValueError,
+        match="Expected a smoother config class from dynestyx.inference.smoother_configs",
+    ):
+        with seed(rng_seed=jr.PRNGKey(0)):
+            with Smoother(smoother_config=invalid_config):
+                discrete_time_lti_simplified_model(
+                    obs_times=obs_times,
+                    obs_values=obs_values,
+                )
+
+
+def test_continuous_time_kf_smoother_type_option_smoke():
+    rng = jr.PRNGKey(7)
+    obs_times = jnp.arange(0.0, 1.0, 0.2)
+
+    with Simulator(n_simulations=1):
+        pred = Predictive(
+            continuous_time_lti_simplified_model,
+            params={"rho": jnp.array(2.0)},
+            num_samples=1,
+            exclude_deterministic=False,
+        )
+        sim = pred(rng, predict_times=obs_times)
+
+    obs_values = sim["f_observations"][0, 0]
+
+    with seed(rng_seed=jr.PRNGKey(8)):
+        with Smoother(
+            smoother_config=ContinuousTimeKFSmootherConfig(
+                cdlgssm_smoother_type="cd_smoother_2",
+                record_smoothed_states_mean=True,
+            )
+        ):
+            out = continuous_time_lti_simplified_model(
+                obs_times=obs_times,
+                obs_values=obs_values,
+            )
+
+    assert out is None
+
+
+def test_smoother_default_configs_discrete_and_continuous_smoke():
+    obs_times_d, obs_values_d, _ = _gen_obs_discrete()
+    obs_times_c, obs_values_c, _, _, _ = _gen_obs_ode()
+
+    with seed(rng_seed=jr.PRNGKey(9)):
+        with Smoother():
+            discrete_time_lti_simplified_model(
+                obs_times=obs_times_d,
+                obs_values=obs_values_d,
+            )
+
+    with seed(rng_seed=jr.PRNGKey(10)):
+        with Smoother():
+            jumpy_controls_model_ode(
+                obs_times=obs_times_c,
+                obs_values=obs_values_c,
+            )
+
+
+def _plate_discrete_model(obs_times=None, obs_values=None, predict_times=None, m=2):
+    dynamics = dsx.LTI_discrete(
+        A=jnp.array([[0.9, 0.0], [0.0, 0.8]]),
+        Q=0.1 * jnp.eye(2),
+        H=jnp.array([[1.0, 0.0]]),
+        R=jnp.array([[0.25]]),
+    )
+    with dsx.plate("trajectories", m):
+        dsx.sample(
+            "f",
+            dynamics,
+            obs_times=obs_times,
+            obs_values=obs_values,
+            predict_times=predict_times,
+        )
+
+
+def test_smoother_plate_batched_loglik_shape():
+    obs_times = jnp.arange(0.0, 5.0, 1.0)
+    m = 3
+    obs_values = jnp.zeros((m, len(obs_times), 1))
+
+    with trace() as tr, seed(rng_seed=jr.PRNGKey(0)):
+        with Smoother(smoother_config=KFSmootherConfig(filter_source="cd_dynamax")):
+            _plate_discrete_model(
+                obs_times=obs_times,
+                obs_values=obs_values,
+                predict_times=jnp.arange(obs_times[-1], obs_times[-1] + 2.0, 1.0),
+                m=m,
+            )
+
+    assert tr["f_marginal_loglik"]["value"].shape == (m,)
+
+
+def _explicit_rollout_metadata_model(predict_times=None):
+    dynamics = dsx.LTI_discrete(
+        A=jnp.array([[1.0, 0.0], [0.0, 1.0]]),
+        Q=0.01 * jnp.eye(2),
+        H=jnp.array([[1.0, 0.0]]),
+        R=jnp.array([[0.1]]),
+    )
+    filtered_dists = [
+        dist.MultivariateNormal(jnp.zeros(2), covariance_matrix=jnp.eye(2))
+    ]
+    dsx.sample(
+        "f",
+        dynamics,
+        predict_times=predict_times,
+        filtered_times=jnp.array([0.0]),
+        filtered_dists=filtered_dists,
+        smoothed_times=jnp.array([0.0]),
+        smoothed_dists=None,
+    )
+
+
+def test_simulator_rejects_smoothed_and_filtered_rollout_metadata_together():
+    with pytest.raises(ValueError, match="filtered_times and filtered_dists"):
+        with seed(rng_seed=jr.PRNGKey(0)):
+            with DiscreteTimeSimulator(n_simulations=1):
+                _explicit_rollout_metadata_model(
+                    predict_times=jnp.arange(0.0, 3.0, 1.0)
+                )