diff --git a/autofit/non_linear/checkpoint.py b/autofit/non_linear/checkpoint.py
new file mode 100644
index 000000000..6e5d2fed8
--- /dev/null
+++ b/autofit/non_linear/checkpoint.py
@@ -0,0 +1,378 @@
+"""
+Where a non-linear search keeps its internal state on disk, and for how long.
+
+A search has two kinds of internal state (decision D5 of the search-extensibility
+epic, ``docs/design/checkpointing.md``):
+
+- ``result_internal``, the **archive**: the backend's final state, written when a
+  fit completes and read back by ``Result.search_internal`` and by
+  ``samples_via_internal_from`` on a completed output folder (the emcee HDF file,
+  the NUTS and SMC dicts, the dill of every other search);
+- ``resume_state``: the file an *interrupted* run resumes from (dynesty's
+  ``savestate.save``, nautilus's ``checkpoint.hdf5``, NSS's ``nss_checkpoint.pkl``,
+  MultiStart's dill). Only searches that genuinely resume have one.
+
+Each search chooses its strategies with two class attributes,
+``NonLinearSearch.checkpointer`` (the archive, ``DillCheckpointer()`` by default) and
+``NonLinearSearch.resume_state`` (``None`` by default). Three strategies cover every
+search:
+
+- ``DillCheckpointer``: ``search_internal.dill`` (the default);
+- ``PickleCheckpointer``: ``search_internal.pickle``, plain ``pickle`` of NumPy data
+  (BlackJAX NUTS and SMC);
+- ``NativeFileCheckpointer(filename, loader)``: a file the backend writes itself and
+  only autofit reads (emcee's HDF backend, dynesty's savestate, nautilus's hdf5
+  checkpoint, NSS's pickle).
+
+Every strategy offers ``save``, ``load``, ``exists`` and ``finalize`` and declares
+``retain_after_completion``: whether its file survives the end of a fit when the
+``output.search_internal`` config is off. Strategies are stateless: they take the
+search's ``paths`` on every call, so one class-level instance serves every copy of a
+search (``copy_with_paths``) and is never serialised with it.
+
+Only ``DirectoryPaths`` stores anything. ``NullPaths`` (no output) and
+``DatabasePaths`` (results live in the database) make ``save`` a no-op and ``load``
+return ``None``, as their ``save_search_internal`` / ``load_search_internal`` always
+have.
+"""
+import logging
+import os
+import pickle
+from pathlib import Path
+from typing import Any, Callable, Optional
+
+logger = logging.getLogger(__name__)
+
+DILL_FILENAME = "search_internal.dill"
+PICKLE_FILENAME = "search_internal.pickle"
+EMCEE_FILENAME = "search_internal.hdf"
+
+
+def _directory(paths) -> Optional[Path]:
+    """
+    The ``search_internal`` folder of ``paths``, or ``None`` when ``paths`` stores no
+    internal state (``NullPaths``, ``DatabasePaths``).
+    """
+    if paths is None or not getattr(paths, "stores_search_internal", True):
+        return None
+    try:
+        directory = paths.search_internal_path
+    except TypeError:
+        return None
+    if directory is None:
+        return None
+    return Path(directory)
+
+
+def _atomic_dump(path: Path, obj, dump: Callable):
+    from autofit.tools.util import open_atomic
+
+    with open_atomic(path, "wb") as f:
+        dump(obj, f)
+
+
+def load_emcee_backend(path: Path):
+    """
+    Open an emcee ``HDFBackend`` file (the emcee archive and resume state).
+    """
+    import emcee
+
+    return emcee.backends.HDFBackend(filename=str(path))
+
+
+def _load_dill(path: Path):
+    import dill
+
+    with open(path, "rb") as f:
+        return dill.load(f)
+
+
+def _load_pickle(path: Path):
+    with open(path, "rb") as f:
+        return pickle.load(f)
+
+
+def load_legacy_search_internal(directory) -> Any:
+    """
+    Load whatever internal state a ``search_internal`` folder holds, detecting its
+    format from the files present.
+
+    This is the format-aware detection ``DirectoryPaths.load_search_internal`` used
+    to do inline (it probed for emcee's ``search_internal.hdf`` first), kept for
+    callers that do not know which search wrote the folder and as the fallback for
+    a folder written before its search chose a strategy. In order:
+
+    - ``search_internal.hdf``, opened as an emcee ``HDFBackend`` (when emcee is
+      installed);
+    - ``search_internal.pickle`` (BlackJAX NUTS / SMC);
+    - ``search_internal.dill`` (every other search).
+
+    Raises
+    ------
+    FileNotFoundError
+        If the folder holds none of them.
+    """
+    directory = Path(directory)
+
+    hdf = directory / EMCEE_FILENAME
+    if hdf.is_file():
+        try:
+            return load_emcee_backend(hdf)
+        except ImportError:
+            pass
+
+    pickled = directory / PICKLE_FILENAME
+    if pickled.is_file():
+        return _load_pickle(pickled)
+
+    return _load_dill(directory / DILL_FILENAME)
+
+
+class Checkpointer:
+    """
+    A strategy for one internal-state file of a search (see the module docstring).
+
+    Parameters
+    ----------
+    filename
+        The file's name inside the search's ``search_internal`` folder.
+    retain_after_completion
+        Whether the file is kept when a fit completes even though the
+        ``output.search_internal`` config is off. For an archive this replaces the
+        old practice of a search forcing that config on in its ``__init__``; for a
+        resume state it says the file is also the archive.
+    """
+
+    kind = "abstract"
+
+    def __init__(self, filename: str, retain_after_completion: bool = False):
+        self.filename = filename
+        self.retain_after_completion = retain_after_completion
+
+    def path(self, paths) -> Optional[Path]:
+        """
+        The file's full path, or ``None`` when ``paths`` stores no internal state.
+        """
+        directory = _directory(paths)
+        if directory is None:
+            return None
+        return directory / self.filename
+
+    def exists(self, paths) -> bool:
+        path = self.path(paths)
+        return path is not None and path.is_file()
+
+    def save(self, paths, obj):
+        """
+        Write ``obj`` atomically (a sibling temporary file renamed into place), so an
+        interrupted write leaves the previous file whole. A no-op when ``paths``
+        stores nothing.
+        """
+        raise NotImplementedError
+
+    def _load(self, path: Path):
+        raise NotImplementedError
+
+    def load(self, paths):
+        """
+        Read the file back.
+
+        Returns ``None`` when ``paths`` stores nothing. When this strategy's own file
+        is absent the folder is probed by ``load_legacy_search_internal``, so an
+        output written before the search chose this strategy still loads.
+
+        Raises
+        ------
+        FileNotFoundError
+            If the folder holds no internal state at all.
+        """
+        path = self.path(paths)
+        if path is None:
+            return None
+        if path.is_file():
+            return self._load(path)
+        return load_legacy_search_internal(path.parent)
+
+    def finalize(self, paths, obj=None):
+        """
+        Write the final state when a fit completes. For an archive strategy that is
+        ``save(obj)``; a strategy whose file the backend writes itself has nothing
+        to add.
+        """
+        if obj is not None:
+            self.save(paths, obj)
+
+    def discard(self, paths):
+        """
+        Delete the file (a resume state, once the fit it would resume has completed).
+        """
+        path = self.path(paths)
+        if path is None:
+            return
+        try:
+            os.remove(path)
+        except FileNotFoundError:
+            pass
+
+    def __repr__(self):
+        return (
+            f"{type(self).__name__}({self.filename!r}, "
+            f"retain_after_completion={self.retain_after_completion})"
+        )
+
+    def __eq__(self, other):
+        return (
+            type(self) is type(other)
+            and self.filename == other.filename
+            and self.retain_after_completion == other.retain_after_completion
+        )
+
+    def __hash__(self):
+        return hash((type(self), self.filename, self.retain_after_completion))
+
+
+class DillCheckpointer(Checkpointer):
+    """
+    ``search_internal.dill``: the backend object serialised with ``dill`` (the
+    default for every search).
+
+    Parameters
+    ----------
+    strip_attributes
+        Attributes set to ``None`` for the duration of the dump and restored
+        afterwards, for members that cannot be serialised (nautilus's
+        multiprocessing pools ``pool_l`` / ``pool_s``).
+    """
+
+    kind = "dill"
+
+    def __init__(
+        self,
+        filename: str = DILL_FILENAME,
+        retain_after_completion: bool = False,
+        strip_attributes=(),
+    ):
+        super().__init__(
+            filename=filename, retain_after_completion=retain_after_completion
+        )
+        self.strip_attributes = tuple(strip_attributes)
+
+    def save(self, paths, obj):
+        path = self.path(paths)
+        if path is None:
+            return
+
+        import dill
+
+        stripped = {}
+        for name in self.strip_attributes:
+            if hasattr(obj, name):
+                stripped[name] = getattr(obj, name)
+                setattr(obj, name, None)
+        try:
+            _atomic_dump(path, obj, dill.dump)
+        finally:
+            for name, value in stripped.items():
+                setattr(obj, name, value)
+
+    def _load(self, path: Path):
+        return _load_dill(path)
+
+
+class PickleCheckpointer(Checkpointer):
+    """
+    ``search_internal.pickle``: plain ``pickle`` of already-NumPy data (BlackJAX NUTS
+    and SMC, whose dicts ``dill`` does not need and which then load without it).
+    """
+
+    kind = "pickle"
+
+    def __init__(
+        self, filename: str = PICKLE_FILENAME, retain_after_completion: bool = True
+    ):
+        super().__init__(
+            filename=filename, retain_after_completion=retain_after_completion
+        )
+
+    def save(self, paths, obj):
+        path = self.path(paths)
+        if path is None:
+            return
+        _atomic_dump(path, obj, pickle.dump)
+
+    def _load(self, path: Path):
+        return _load_pickle(path)
+
+
+class NativeFileCheckpointer(Checkpointer):
+    """
+    A file in the backend's own format, which the backend writes itself (emcee's HDF
+    backend, dynesty's ``savestate.save``, nautilus's ``checkpoint.hdf5``, NSS's
+    ``nss_checkpoint.pkl``): autofit only locates, loads and (for a resume state)
+    deletes it.
+
+    Parameters
+    ----------
+    filename
+        The file's name inside ``search_internal``.
+    loader
+        ``loader(path)`` opens the file (for example ``load_emcee_backend``).
+    retain_after_completion
+        See ``Checkpointer``.
+    """
+
+    kind = "native"
+
+    def __init__(
+        self,
+        filename: str,
+        loader: Callable[[Path], Any],
+        retain_after_completion: bool = False,
+    ):
+        super().__init__(
+            filename=filename, retain_after_completion=retain_after_completion
+        )
+        self.loader = loader
+
+    def save(self, paths, obj):
+        """
+        A no-op: the backend writes this file itself, during the run.
+        """
+
+    def finalize(self, paths, obj=None):
+        """
+        A no-op: the backend has already written the final state.
+        """
+
+    def load(self, paths):
+        """
+        Open the file with ``loader``. A native file is the backend's own format, so
+        there is no format probing; ``FileNotFoundError`` if it is absent, except
+        that an absent archive falls back to the legacy probe like every strategy.
+        """
+        path = self.path(paths)
+        if path is None:
+            return None
+        if path.is_file():
+            return self.loader(path)
+        if self.retain_after_completion:
+            return load_legacy_search_internal(path.parent)
+        raise FileNotFoundError(f"No {self.filename} exists at {path.parent}")
+
+    def __eq__(self, other):
+        return super().__eq__(other) and self.loader is other.loader
+
+    def __hash__(self):
+        return super().__hash__()
+
+
+def checkpointer_kind(checkpointer: Optional[Checkpointer]) -> Optional[str]:
+    """
+    The manifest spelling of a strategy: ``"dill"``, ``"pickle"``,
+    ``"native:<filename>"``, or ``None``.
+    """
+    if checkpointer is None:
+        return None
+    if checkpointer.kind == "native":
+        return f"native:{checkpointer.filename}"
+    return checkpointer.kind
diff --git a/autofit/non_linear/paths/abstract.py b/autofit/non_linear/paths/abstract.py
index 6fb6e2713..df08ca88b 100644
--- a/autofit/non_linear/paths/abstract.py
+++ b/autofit/non_linear/paths/abstract.py
@@ -104,6 +104,13 @@ def _replace_zip_member(zip_path, arcname: str, file_path):
 
 
 class AbstractPaths(ABC):
+    stores_search_internal = True
+    """
+    Whether the search's internal state (``autofit.non_linear.checkpoint``) is
+    stored on disk under ``search_internal/``. ``NullPaths`` (no output) and
+    ``DatabasePaths`` (results live in the database) store none.
+    """
+
     def __init__(
         self,
         name: Optional[str] = None,
diff --git a/autofit/non_linear/paths/database.py b/autofit/non_linear/paths/database.py
index 6f7d03092..bbe548d8e 100644
--- a/autofit/non_linear/paths/database.py
+++ b/autofit/non_linear/paths/database.py
@@ -15,6 +15,8 @@
 
 
 class DatabasePaths(AbstractPaths):
+    stores_search_internal = False
+
     def __init__(
         self,
         session,
diff --git a/autofit/non_linear/paths/directory.py b/autofit/non_linear/paths/directory.py
index 78110cb37..a1a8bb792 100644
--- a/autofit/non_linear/paths/directory.py
+++ b/autofit/non_linear/paths/directory.py
@@ -20,6 +20,7 @@
 from autofit.non_linear.test_mode import skip_visualization
 
 from .abstract import AbstractPaths, _test_mode_segment
+from ..checkpoint import load_legacy_search_internal
 
 from ..samples import load_from_table
 from autofit.non_linear.samples.pdf import SamplesPDF
@@ -224,31 +225,33 @@ def save_search_internal(self, obj):
 
     def load_search_internal(self):
         """
-        Load the internal representation of a non-linear search from a pickle or dill file.
+        Load the internal representation of a non-linear search (its archive).
 
-        The results in this representation are required to use a search's in-built tools for visualization,
-        analysing samples and other tasks.
+        Loading is injected: the search that owns these paths (``self.search``)
+        loads through its ``checkpointer`` (emcee's HDF backend, the NUTS / SMC
+        pickle, or the dill of every other search), so ``paths`` knows no backend
+        format. A folder opened without its search falls back to format-aware
+        detection (``autofit.non_linear.checkpoint.load_legacy_search_internal``:
+        ``search_internal.hdf``, then ``.pickle``, then ``.dill``), which is also what
+        a checkpointer falls back to when its own file is absent, so outputs written
+        before A3 still load.
 
         Returns
         -------
         The results of the non-linear search in its internal representation.
-        """
-
-        # This is a nasty hack to load emcee backends. It will be removed once the source code is more stable.
-
-        try:
-            import emcee
 
-            backend_filename = self.search_internal_path / "search_internal.hdf"
-            if backend_filename.is_file():
-                return emcee.backends.HDFBackend(filename=str(backend_filename))
-        except ImportError:
-            pass
+        Raises
+        ------
+        FileNotFoundError
+            If no internal state is stored (for example a summary-only output, or
+            ``output.search_internal: false``).
+        """
+        checkpointer = getattr(getattr(self, "search", None), "checkpointer", None)
 
-        filename = self.search_internal_path / "search_internal.dill"
+        if checkpointer is not None:
+            return checkpointer.load(self)
 
-        with open_(filename, "rb") as f:
-            return dill.load(f)
+        return load_legacy_search_internal(self.search_internal_path)
 
     def remove_search_internal(self):
         """
diff --git a/autofit/non_linear/paths/null.py b/autofit/non_linear/paths/null.py
index 7b7f76bc1..e3686eba5 100644
--- a/autofit/non_linear/paths/null.py
+++ b/autofit/non_linear/paths/null.py
@@ -9,6 +9,8 @@ class NullPaths(AbstractPaths):
     Null version of paths object for avoiding writing of files to disk
     """
 
+    stores_search_internal = False
+
     @property
     def samples(self):
         return None
diff --git a/autofit/non_linear/samples/adapter.py b/autofit/non_linear/samples/adapter.py
new file mode 100644
index 000000000..28f0eb1ea
--- /dev/null
+++ b/autofit/non_linear/samples/adapter.py
@@ -0,0 +1,358 @@
+"""
+The one conversion from a backend's raw samples to a ``Samples`` object.
+
+Every search turns its backend's internal state into the same five things: the
+parameter vectors, a log likelihood or a log posterior per vector, a weight per
+vector, and the backend-specific ``samples_info`` entries. A search expresses that
+as a ``RawSamples`` (``NonLinearSearch.raw_samples_from``) and ``samples_from_raw``
+does everything else, once, for every search:
+
+- the log prior of every vector, through one helper (``log_prior_list_from``);
+- the log likelihood from a log posterior (``log_likelihood = log_posterior -
+  log_prior``) when the backend reports a posterior;
+- the length checks that keep parameters, log values and weights in correspondence
+  (PyAutoFit#1628);
+- recognition of the backends' invalid-model sentinels (``-1e99``, ``-1e30``,
+  ``-inf``) through one predicate (``is_invalid``);
+- the injection of ``samples_info["time"]`` and, through the ``Samples`` class,
+  ``samples_info["class_path"]``.
+
+Representations are preserved (decision D13 of the search-extensibility epic).
+Parameter vectors, log likelihoods and weights reach ``Sample.from_lists`` exactly
+as the backend produced them, in the backend's order: equally weighted draws keep
+weight 1, weighted samples keep the weights their search computed (normalised or
+not), nothing is renormalised and no sentinel value is rewritten. The ordering of
+the vectors is the search's to specify (for example ``ChainPosterior`` flattens
+draw-major for emcee and zeus and chain-major for BlackJAX NUTS), and diagnostics
+belong to the search's ``info`` entries, which are passed through untouched.
+"""
+import logging
+import math
+from dataclasses import dataclass, field
+from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Type
+
+import numpy as np
+
+from autofit import exc
+
+logger = logging.getLogger(__name__)
+
+INVALID_SENTINELS = (-1.0e99, -1.0e30, -float("inf"))
+"""
+The values backends use for a model whose likelihood is invalid: ``-1e99``
+(dynesty and the post-fit ``Fitness`` resample value), ``-1e30`` (NSS sampling) and
+``-inf`` (emcee, zeus, BlackJAX, the MLE searches).
+"""
+
+INVALID_THRESHOLD = -1.0e30
+"""
+Every log likelihood at or below this value is one of ``INVALID_SENTINELS`` (or a
+value derived from one, such as ``-1e99 - log_prior``), never a likelihood a real
+model evaluates to.
+"""
+
+
+def is_invalid(value) -> bool:
+    """
+    Whether a log likelihood or log posterior is a backend's invalid-model sentinel.
+
+    This is the single recognition rule for all three sentinels; the value itself is
+    never rewritten, so stored samples keep the representation their backend gave
+    them. NaN (the MultiStart diagnostic lanes, which carry no likelihood) is not a
+    sentinel.
+    """
+    try:
+        value = float(value)
+    except (TypeError, ValueError):
+        return False
+    if math.isnan(value):
+        return False
+    return value <= INVALID_THRESHOLD
+
+
+def invalid_count(values: Sequence) -> int:
+    """
+    The number of entries of ``values`` that are invalid-model sentinels.
+    """
+    return sum(1 for value in values if is_invalid(value))
+
+
+def log_prior_list_from(model, parameter_lists: Sequence[Sequence[float]]) -> List:
+    """
+    The log prior of every parameter vector: the one log-prior helper of sample
+    conversion (``model.log_prior_list_from``, the sum of each vector's per-prior log
+    densities).
+    """
+    return model.log_prior_list_from(parameter_lists=parameter_lists)
+
+
+def _as_list(values):
+    """
+    ``values`` as a list, without changing the type of its elements unless it is a
+    NumPy array (whose ``tolist`` gives plain Python scalars, as every search did
+    before the adapter).
+    """
+    if values is None:
+        return None
+    if isinstance(values, np.ndarray):
+        return values.tolist()
+    return list(values)
+
+
+@dataclass(frozen=True)
+class RawSamples:
+    """
+    What a backend produced, before any conversion.
+
+    Parameters
+    ----------
+    parameters
+        The parameter vectors, ``(N, D)``, in physical coordinates and in the
+        search's sample order.
+    log_likelihood
+        The log likelihood of every vector, ``(N,)``. Give either this or
+        ``log_posterior``.
+    log_posterior
+        The log posterior of every vector, ``(N,)``; the log likelihood is then
+        ``log_posterior - log_prior``.
+    weights
+        The weight of every vector, ``(N,)``, exactly as stored. ``None`` means
+        equally weighted draws (weight 1 each). Never renormalised here.
+    info
+        The search-specific ``samples_info`` entries (diagnostics, settings,
+        counters). ``time`` is injected by ``samples_from_raw`` when absent.
+    log_prior
+        The log prior of every vector when the backend already has it; ``None``
+        (the usual case) computes it with ``log_prior_list_from``.
+    samples_kwargs
+        Extra keyword arguments of the ``Samples`` class (for example
+        ``SamplesMCMC``'s ``auto_correlations``).
+    label
+        The backend's name, used in error messages.
+    """
+
+    parameters: Any
+    log_likelihood: Optional[Any] = None
+    log_posterior: Optional[Any] = None
+    weights: Optional[Any] = None
+    info: Dict[str, Any] = field(default_factory=dict)
+    log_prior: Optional[Any] = None
+    samples_kwargs: Dict[str, Any] = field(default_factory=dict)
+    label: str = "search"
+
+    def __post_init__(self):
+        if (self.log_likelihood is None) == (self.log_posterior is None):
+            raise exc.SamplesException(
+                f"The raw samples of {self.label} must give exactly one of "
+                "log_likelihood and log_posterior."
+            )
+
+
+def _check_length(label: str, n_parameters: int, values, name: str):
+    """
+    Raise when a per-sample column does not have one entry per parameter vector.
+    """
+    if values is not None and len(values) != n_parameters:
+        raise exc.SamplesException(
+            f"The number of {label} parameter samples does not match the number of "
+            f"{name} values returned by the sampler: "
+            f"{n_parameters} parameter samples versus "
+            f"{len(values)} {name} values. "
+            f"The parameters and {name}s are therefore not in correspondence "
+            "and the samples cannot be built."
+        )
+
+
+def samples_from_raw(
+    model,
+    raw: RawSamples,
+    samples_cls: Type,
+    time: Optional[float] = None,
+    **samples_kwargs,
+):
+    """
+    Build the ``Samples`` object of ``raw`` (see the module docstring for what is
+    done here, once, for every search).
+
+    Parameters
+    ----------
+    model
+        The model the parameter vectors belong to.
+    raw
+        The backend's raw samples.
+    samples_cls
+        The ``Samples`` class to build (``Samples``, ``SamplesMCMC``,
+        ``SamplesNest``, ``SamplesSMC``, ``NSSamples``, ...). Its constructor stamps
+        ``samples_info["class_path"]``, which the aggregator and
+        ``DirectoryPaths.samples`` use to rebuild it.
+    time
+        The search's run time, injected as ``samples_info["time"]`` unless the
+        search's own ``info`` already gives one.
+    samples_kwargs
+        Extra keyword arguments of ``samples_cls``, merged over
+        ``raw.samples_kwargs``.
+    """
+    from autofit.non_linear.samples.sample import Sample
+
+    parameter_lists = _as_list(raw.parameters)
+    n = len(parameter_lists)
+
+    if raw.log_posterior is not None:
+        log_posterior_list = _as_list(raw.log_posterior)
+        _check_length(raw.label, n, log_posterior_list, "log posterior")
+    else:
+        log_posterior_list = None
+
+    log_likelihood_list = _as_list(raw.log_likelihood)
+    _check_length(raw.label, n, log_likelihood_list, "log likelihood")
+
+    if raw.log_prior is not None:
+        log_prior_list = _as_list(raw.log_prior)
+        _check_length(raw.label, n, log_prior_list, "log prior")
+    else:
+        log_prior_list = log_prior_list_from(
+            model=model, parameter_lists=parameter_lists
+        )
+
+    if log_likelihood_list is None:
+        log_likelihood_list = [
+            log_posterior - log_prior
+            for log_posterior, log_prior in zip(log_posterior_list, log_prior_list)
+        ]
+
+    if raw.weights is None:
+        weight_list = n * [1.0]
+    else:
+        weight_list = _as_list(raw.weights)
+        _check_length(raw.label, n, weight_list, "weight")
+
+    invalid = invalid_count(log_likelihood_list)
+    if invalid:
+        logger.debug(
+            f"{invalid} of the {n} {raw.label} samples carry an invalid-model "
+            "sentinel log likelihood; they are kept as stored."
+        )
+
+    sample_list = Sample.from_lists(
+        model=model,
+        parameter_lists=parameter_lists,
+        log_likelihood_list=log_likelihood_list,
+        log_prior_list=log_prior_list,
+        weight_list=weight_list,
+    )
+
+    info = dict(raw.info)
+    if "time" not in info:
+        info["time"] = time
+
+    return samples_cls(
+        model=model,
+        sample_list=sample_list,
+        samples_info=info,
+        **{**raw.samples_kwargs, **samples_kwargs},
+    )
+
+
+class ChainPosterior:
+    """
+    The equally weighted draws of an ensemble or multi-chain MCMC backend, thinned and
+    with burn-in removed: the one implementation shared by Emcee, Zeus and
+    BlackJAX NUTS.
+
+    ``get_chain(discard, thin)`` and ``get_log_prob(discard, thin)`` return the flat
+    ``(N, D)`` draws and ``(N,)`` log values for a burn-in and thinning, so emcee's and
+    zeus's own ``get_chain(discard=..., thin=..., flat=True)`` slicing is used
+    unchanged. ``from_arrays`` builds the getters for a backend that only stores
+    arrays.
+    """
+
+    def __init__(
+        self,
+        get_chain: Callable[[int, int], np.ndarray],
+        get_log_prob: Callable[[int, int], np.ndarray],
+        label: str = "MCMC",
+    ):
+        self.get_chain = get_chain
+        self.get_log_prob = get_log_prob
+        self.label = label
+
+    @classmethod
+    def from_sampler(cls, sampler, label: str = "MCMC") -> "ChainPosterior":
+        """
+        For an emcee or zeus sampler (or backend), via their ``get_chain`` and
+        ``get_log_prob`` with ``flat=True``.
+        """
+        return cls(
+            get_chain=lambda discard, thin: sampler.get_chain(
+                discard=discard, thin=thin, flat=True
+            ),
+            get_log_prob=lambda discard, thin: sampler.get_log_prob(
+                discard=discard, thin=thin, flat=True
+            ),
+            label=label,
+        )
+
+    @classmethod
+    def from_arrays(
+        cls,
+        chain: np.ndarray,
+        log_prob: np.ndarray,
+        chain_major: bool = False,
+        label: str = "MCMC",
+    ) -> "ChainPosterior":
+        """
+        For draws stored as arrays of shape ``(n_steps, n_chains, n_dim)`` and
+        ``(n_steps, n_chains)``.
+
+        Burn-in and thinning slice the step axis as emcee does
+        (``[discard + thin - 1 :: thin]``, the whole chain for ``discard=0,
+        thin=1``). ``chain_major=True`` flattens chain by chain (all of chain 0's
+        draws in order, then chain 1's, ...); otherwise draw by draw, as emcee's
+        ``flat=True`` does.
+        """
+        chain = np.asarray(chain)
+        log_prob = np.asarray(log_prob)
+
+        def _flat(values, discard, thin):
+            values = values[discard + thin - 1 :: thin]
+            if chain_major:
+                values = np.moveaxis(values, 0, 1)
+            return values.reshape((-1,) + values.shape[2:])
+
+        return cls(
+            get_chain=lambda discard, thin: _flat(chain, discard, thin),
+            get_log_prob=lambda discard, thin: _flat(log_prob, discard, thin),
+            label=label,
+        )
+
+    def thin(self, discard: int, thin: int) -> Tuple[np.ndarray, np.ndarray]:
+        """
+        The draws and log values after removing ``discard`` burn-in steps and keeping
+        every ``thin``-th step.
+
+        When that leaves no draws (an unconverged chain whose auto-correlation time
+        is comparable to its length), the whole chain is used instead, with a log
+        message, so the samples can still be built and inspected. The log values are
+        always requested with the same ``discard`` and ``thin`` as the draws, so the
+        two stay in correspondence (PyAutoFit#1628).
+        """
+        draws = self.get_chain(discard, thin)
+
+        if len(draws) == 0:
+            logger.info(
+                f"""
+                After thinning the {self.label} samples in order to remove burn-in, no samples were left.
+
+                To create a samples object containing samples, so that the code can continue and results
+                can be inspected, the full list of samples before removing burn-in has been used. This may
+                indicate that the sampler has not converged and therefore your results may not be reliable.
+
+                To fix this, run {self.label} with more steps to ensure convergence is achieved or change the
+                auto correlation settings to be less aggressive in thinning samples.
+                """
+            )
+            discard = 0
+            thin = 1
+            draws = self.get_chain(discard, thin)
+
+        return draws, self.get_log_prob(discard, thin)
diff --git a/autofit/non_linear/search/abstract_search.py b/autofit/non_linear/search/abstract_search.py
index b0d7715ac..1bcebb7dc 100644
--- a/autofit/non_linear/search/abstract_search.py
+++ b/autofit/non_linear/search/abstract_search.py
@@ -50,6 +50,7 @@
 from autofit.non_linear.paths.sub_directory_paths import SubDirectoryPaths
 from autofit.non_linear.samples.samples import Samples
 from autofit.non_linear.samples.summary import SamplesSummary
+from autofit.non_linear.checkpoint import Checkpointer, DillCheckpointer
 from autofit.non_linear.timer import Timer
 from autofit.non_linear.analysis import Analysis
 from autofit.non_linear.paths.null import NullPaths
@@ -1194,7 +1195,7 @@ def post_fit_output(self, search_internal):
         search_internal
             The internal search.
         """
-        if not conf.instance["output"]["search_internal"]:
+        if not self.retains_search_internal:
             self.logger.info("Removing search internal folder.")
             self.paths.remove_search_internal()
         elif search_internal is not None:
@@ -1439,9 +1440,6 @@ def _build_fake_samples(model, parameter_vector, log_likelihood):
     # result through the initializer, so it has no starting point to plot.
     _plots_start_point = True
 
-    # The `Samples` class `samples_from_raw` builds for a `run(ctx)` search.
-    samples_cls = Samples
-
     @abstractmethod
     def _fit(self, model: AbstractPriorModel, analysis: Analysis):
         """
@@ -1493,18 +1491,13 @@ def run(self, ctx) -> Any:
 
     run._is_bridge_default = True
 
-    def raw_samples_from(self, model: AbstractPriorModel, internal):
-        """
-        Map a ``run(ctx)`` search's internal state onto a ``RawSamples``
-        (``autofit.non_linear.search.fit_context``).
-        """
-        raise NotImplementedError
-
-    def info_from(self, internal) -> Optional[dict]:
+    def info_from(self, internal) -> dict:
         """
-        The ``samples_info`` entries of a ``run(ctx)`` search's internal state.
+        The ``samples_info`` entries of a ``run(ctx)`` search's internal state, the
+        ``info`` its ``raw_samples_from`` puts on the ``RawSamples``
+        (``autofit.non_linear.samples.adapter``).
         """
-        return None
+        return {}
 
     def fitness_overrides(self, analysis: Analysis) -> dict:
         """
@@ -1589,11 +1582,55 @@ def apply_test_mode(self):
         """
         pass
 
-    def output_search_internal(self, search_internal):
-        self.paths.save_search_internal(
-            obj=search_internal,
+    # --- internal state: archive and resume state (search-extensibility A3, D5) ----
+    #
+    # See ``autofit.non_linear.checkpoint`` and ``docs/design/checkpointing.md``.
+
+    checkpointer: Checkpointer = DillCheckpointer()
+    """
+    The archive strategy: where the backend's final state is stored when a fit
+    completes, for ``Result.search_internal`` and ``samples_via_internal_from``.
+    """
+
+    resume_state: Optional[Checkpointer] = None
+    """
+    The file an interrupted run resumes from, for searches that genuinely resume
+    (``None`` otherwise). Deleted when the fit completes unless it is the archive's
+    own file or declares ``retain_after_completion``.
+    """
+
+    @property
+    def retains_search_internal(self) -> bool:
+        """
+        Whether ``search_internal/`` survives the end of a fit: always when the
+        archive declares ``retain_after_completion`` (its results cannot be rebuilt
+        without it), otherwise when the ``output.search_internal`` config is on.
+        Constructing or running a search never changes that config.
+        """
+        return bool(
+            self.checkpointer.retain_after_completion
+            or conf.instance["output"]["search_internal"]
         )
 
+    def output_search_internal(self, search_internal):
+        """
+        Archive ``search_internal`` through the search's ``checkpointer`` and, once a
+        fit has completed, discard its resume state (there is nothing left to
+        resume).
+
+        ``_fit`` of the searches that archive mid-run (BlackJAX NUTS, SMC) also
+        calls this after every chunk; their resume state is ``None``.
+        """
+        self.checkpointer.finalize(self.paths, search_internal)
+
+        resume_state = self.resume_state
+        if (
+            resume_state is not None
+            and not resume_state.retain_after_completion
+            and resume_state.filename != self.checkpointer.filename
+        ):
+            resume_state.discard(self.paths)
+
     def _steps_until_full_update(self, iterations_remaining: int) -> int:
         """
         How many iterations to run before the next ``perform_update``
@@ -1857,6 +1894,19 @@ def start_points(
 
         return unit_parameter_lists, parameter_lists, figure_of_merit_list
 
+    # --- samples conversion (search-extensibility phase A3) -------------------------
+    #
+    # A search maps its backend's internal state onto a ``RawSamples`` in
+    # ``raw_samples_from``; ``samples_via_internal_from`` below is implemented once,
+    # here, on top of it (``autofit.non_linear.samples.adapter``).
+
+    samples_cls = Samples
+    """
+    The ``Samples`` class ``samples_via_internal_from`` builds (``SamplesMCMC`` and
+    ``SamplesNest`` on the family bases, ``SamplesSMC`` / ``NSSamples`` on those
+    searches).
+    """
+
     def samples_from(self, model: AbstractPriorModel, search_internal=None) -> Samples:
         """
         Loads the samples of a non-linear search from its output files.
@@ -1888,25 +1938,54 @@ def samples_from(self, model: AbstractPriorModel, search_internal=None) -> Sampl
 
     def samples_via_internal_from(
         self, model: AbstractPriorModel, search_internal=None
-    ):
-        """
-        The samples of the internal state. A ``run(ctx)`` search gets them from its
-        ``raw_samples_from``; any other search overrides this.
+    ) -> Samples:
         """
-        if type(self).raw_samples_from is NonLinearSearch.raw_samples_from:
-            raise NotImplementedError
+        The samples of the search, converted from its internal state.
 
-        from autofit.non_linear.search.fit_context import samples_from_raw
+        ``search_internal`` is the backend's in-memory state when given; otherwise
+        it is loaded from the output folder (``load_search_internal``). The search's
+        ``raw_samples_from`` maps it onto a ``RawSamples`` and ``samples_from_raw``
+        builds the ``samples_cls`` object (log priors, the log likelihood from a log
+        posterior, length checks, ``time`` / ``class_path``), the same way for every
+        search.
+
+        Raises
+        ------
+        NotImplementedError
+            When the search does not implement ``raw_samples_from``.
+        FileNotFoundError
+            When no internal state is stored.
+        """
+        from autofit.non_linear.samples.adapter import samples_from_raw
 
         if search_internal is None:
-            search_internal = self.paths.load_search_internal()
+            search_internal = self.load_search_internal()
+
+        raw = self.raw_samples_from(model, search_internal)
 
         return samples_from_raw(
             model=model,
-            raw=self.raw_samples_from(model, search_internal),
+            raw=raw,
             samples_cls=self.samples_cls,
+            time=self.timer.time if self.timer else None,
         )
 
+    def raw_samples_from(self, model: AbstractPriorModel, search_internal):
+        """
+        Map the backend's internal state onto a
+        ``autofit.non_linear.samples.adapter.RawSamples``: its parameter vectors,
+        log likelihoods (or log posteriors), weights and ``samples_info`` entries,
+        exactly as the backend stores them. Every concrete search implements it.
+        """
+        raise NotImplementedError
+
+    def load_search_internal(self):
+        """
+        The search's internal state as stored in its output folder, or ``None`` when
+        its paths store nothing (``NullPaths``, ``DatabasePaths``).
+        """
+        return self.paths.load_search_internal()
+
     def _pools(self) -> PoolFactory:
         """
         The current fit's `PoolFactory` (the JAX fork rule applied to its analysis), or,
@@ -1915,6 +1994,7 @@ def _pools(self) -> PoolFactory:
         pools = self.__dict__.get("_fit_pools")
         return pools if pools is not None else PoolFactory(self)
 
+    @check_cores
     def make_pool(self):
         """Make the pool instance used to parallelize a `NonLinearSearch` alongside a set of unique ids for every
         process in the pool. If the specified number of cores is 1, a pool instance is not made and None is returned.
diff --git a/autofit/non_linear/search/capabilities.py b/autofit/non_linear/search/capabilities.py
index a9b891950..283835461 100644
--- a/autofit/non_linear/search/capabilities.py
+++ b/autofit/non_linear/search/capabilities.py
@@ -29,6 +29,9 @@
 ``test_mode_budget``      The reduced budget ``PYAUTO_TEST_MODE=1`` applies.
 ``objective_target``      What the backend's objective returns and in which coordinates.
 ``invalid_value``         The sentinel the objective returns for an invalid model.
+``checkpointer``          Where the search archives its internal state when a fit
+                          completes (``autofit.non_linear.checkpoint``): ``dill``,
+                          ``pickle`` or ``native:<filename>``.
 ========================  ==============================================================
 
 ``invalid_value`` records what the backend actually sees and is not yet normalised. For
@@ -172,6 +175,7 @@ def to_dict(self) -> Dict[str, str]:
     "test_mode_budget",
     "objective_target",
     "invalid_value",
+    "checkpointer",
 )
 """
 The names of the static capability attributes, in manifest order.
@@ -196,6 +200,10 @@ def capability_value(name: str, value: Any) -> Any:
         return value.to_dict() if value is not None else None
     if name == "invalid_value":
         return invalid_value_to_str(value)
+    if name == "checkpointer":
+        from autofit.non_linear.checkpoint import checkpointer_kind
+
+        return checkpointer_kind(value)
     if name == "citation_keys":
         return list(value)
     if name == "test_mode_budget":
diff --git a/autofit/non_linear/search/fit_context.py b/autofit/non_linear/search/fit_context.py
index 7d7677918..9f2c29dd6 100644
--- a/autofit/non_linear/search/fit_context.py
+++ b/autofit/non_linear/search/fit_context.py
@@ -7,79 +7,24 @@
 a search that overrides ``_fit`` keeps working unchanged. ``Drawer`` and ``Nautilus`` are
 the first two searches on the bridge.
 
-Members filled by later phases are present with their A2 value:
+Members filled by later phases:
 
-- ``ctx.resume`` is ``None`` and ``ctx.checkpointer`` is ``None`` (phase A3 fills both;
-  until then a backend resumes from its own native checkpoint, as ``Nautilus`` does);
+- ``ctx.checkpointer`` is the search's archive strategy (``NonLinearSearch.checkpointer``)
+  and ``ctx.resume`` its resume state (``NonLinearSearch.resume_state``) when the
+  interrupted run's file exists, else ``None`` (phase A3, ``autofit.non_linear.checkpoint``);
+  ``ctx.resume`` is the ``Checkpointer`` handle, not the loaded state, because some
+  backends reopen their own native file (``Nautilus``);
 - ``ctx.rng`` is a ``numpy.random.SeedSequence`` from the search's ``seed`` attribute
   when it has one (phase A4 adds one search-level seed and feeds the initializer).
 
-``RawSamples`` and ``samples_from_raw`` are a provisional, minimal stand-in for phase
-A3's samples adapter (``autofit/non_linear/samples/adapter.py``), so a ``run(ctx)`` search
-has the right shape now; A3's adapter replaces them.
+``RawSamples`` and ``samples_from_raw`` are phase A3's samples adapter
+(``autofit/non_linear/samples/adapter.py``), re-exported here for ``run(ctx)`` searches.
 """
-from typing import Any, Iterator, List, NamedTuple, Optional
+from typing import Any, Iterator
 
 import numpy as np
 
-
-class RawSamples(NamedTuple):
-    """
-    A backend's samples, before conversion to ``Samples``: parameter vectors, their log
-    likelihoods **or** log posteriors, their weights and the ``samples_info`` entries.
-
-    Provisional: phase A3's ``samples/adapter.py`` owns the final type.
-    """
-
-    parameters: List[List[float]]
-    log_likelihood: Optional[List[float]] = None
-    log_posterior: Optional[List[float]] = None
-    weights: Optional[List[float]] = None
-    info: Optional[dict] = None
-
-
-def samples_from_raw(model, raw: RawSamples, samples_cls):
-    """
-    Convert ``raw`` into a ``samples_cls`` instance: the log prior of every vector, the log
-    likelihood from the log posterior when only that was given, and unit weights when none
-    were. Weights are never renormalised.
-
-    Provisional: phase A3's adapter replaces it.
-    """
-    from autofit.non_linear.samples.sample import Sample
-
-    parameter_lists = raw.parameters
-
-    log_prior_list = [
-        sum(model.log_prior_list_from_vector(vector=vector))
-        for vector in parameter_lists
-    ]
-
-    if raw.log_likelihood is not None:
-        log_likelihood_list = raw.log_likelihood
-    else:
-        log_likelihood_list = [
-            log_posterior - log_prior
-            for log_posterior, log_prior in zip(raw.log_posterior, log_prior_list)
-        ]
-
-    weight_list = (
-        raw.weights if raw.weights is not None else len(log_likelihood_list) * [1.0]
-    )
-
-    sample_list = Sample.from_lists(
-        model=model,
-        parameter_lists=parameter_lists,
-        log_likelihood_list=log_likelihood_list,
-        log_prior_list=log_prior_list,
-        weight_list=weight_list,
-    )
-
-    return samples_cls(
-        model=model,
-        sample_list=sample_list,
-        samples_info=raw.info,
-    )
+from autofit.non_linear.samples.adapter import RawSamples, samples_from_raw
 
 
 class UpdateSchedule:
@@ -136,8 +81,13 @@ def __init__(
         self.test_mode_level = test_mode_level
         self.pool = pool
         self.rng = np.random.SeedSequence(getattr(search, "seed", None))
-        self.resume = None
-        self.checkpointer = None
+        self.checkpointer = search.checkpointer
+        resume_state = search.resume_state
+        self.resume = (
+            resume_state
+            if resume_state is not None and resume_state.exists(search.paths)
+            else None
+        )
         self.schedule = UpdateSchedule(search)
 
     def objective(self, kind: str):
diff --git a/autofit/non_linear/search/mcmc/abstract_mcmc.py b/autofit/non_linear/search/mcmc/abstract_mcmc.py
index 77ff4489b..49de061ec 100644
--- a/autofit/non_linear/search/mcmc/abstract_mcmc.py
+++ b/autofit/non_linear/search/mcmc/abstract_mcmc.py
@@ -7,12 +7,15 @@
 from autofit.non_linear.initializer import Initializer, InitializerBall
 from autofit.non_linear.search.mcmc.auto_correlations import AutoCorrelationsSettings
 from autofit.non_linear.plot import corner_cornerpy
+from autofit.non_linear.samples.mcmc import SamplesMCMC
 
 if TYPE_CHECKING:
     from autofit.database.sqlalchemy_ import sa
 
 class AbstractMCMC(NonLinearSearch):
 
+    samples_cls = SamplesMCMC
+
     def __init__(
             self,
             name: Optional[str] = None,
diff --git a/autofit/non_linear/search/mcmc/blackjax/nuts/search.py b/autofit/non_linear/search/mcmc/blackjax/nuts/search.py
index a0876de53..af1d43837 100644
--- a/autofit/non_linear/search/mcmc/blackjax/nuts/search.py
+++ b/autofit/non_linear/search/mcmc/blackjax/nuts/search.py
@@ -2,15 +2,13 @@
 
 import logging
 import os
-import pickle
 from pathlib import Path
 from typing import Optional, TYPE_CHECKING
 
 import numpy as np
 
-from autonerves import conf
-
 from autofit.mapper.prior_model.abstract import AbstractPriorModel
+from autofit.non_linear.checkpoint import PICKLE_FILENAME, PickleCheckpointer
 from autofit.non_linear.fitness import Fitness
 from autofit.non_linear.initializer import Initializer
 from autofit.non_linear.search.mcmc.abstract_mcmc import AbstractMCMC
@@ -25,8 +23,7 @@
     stack_initial_positions,
 )
 from autofit.non_linear.test_mode import is_test_mode
-from autofit.non_linear.samples.mcmc import SamplesMCMC
-from autofit.non_linear.samples.sample import Sample
+from autofit.non_linear.samples.adapter import ChainPosterior, RawSamples
 from autofit.non_linear.search import capabilities as cap
 
 if TYPE_CHECKING:
@@ -53,6 +50,10 @@ class BlackJAXNUTS(AbstractMCMC):
     test_mode_budget = {"num_warmup": 20, "num_samples": 20, "num_chains": 2}
     objective_target = cap.ObjectiveTarget(cap.ObjectiveQuantity.LOG_POSTERIOR, cap.CoordinateSpace.PHYSICAL)
     invalid_value = -float("inf")
+    # The chain dict is plain NumPy, pickled (atomically) after every chunk and kept
+    # after completion: the samples cannot be rebuilt without it. Not a resume state:
+    # every ``_fit`` starts afresh (``resumable = False``).
+    checkpointer = PickleCheckpointer()
 
     __identifier_fields__ = (
         "num_warmup",
@@ -262,8 +263,6 @@ def __init__(
 
         self.logger.debug("Creating BlackJAXNUTS Search")
 
-        conf.instance["output"]["search_internal"] = True
-
     def apply_test_mode(self):
         logger.warning(
             "TEST MODE 1 (reduced iterations): BlackJAXNUTS will run with "
@@ -497,38 +496,16 @@ def run_chunk(rng_key, initial_state, n_steps):
 
     @property
     def backend_filename(self):
-        return self.paths.search_internal_path / "search_internal.pickle"
+        return self.paths.search_internal_path / PICKLE_FILENAME
 
     @property
     def backend(self) -> dict:
-        """Load the pickled search-internal dict written by ``_fit``."""
-        if not Path(self.backend_filename).is_file():
+        """The search-internal dict archived by ``_fit`` (``search_internal.pickle``)."""
+        if not self.checkpointer.exists(self.paths):
             raise FileNotFoundError(
-                f"search_internal.pickle does not exist at "
-                f"{self.paths.search_internal_path}"
+                f"{PICKLE_FILENAME} does not exist at {self.paths.search_internal_path}"
             )
-        with open(self.backend_filename, "rb") as f:
-            return pickle.load(f)
-
-    def output_search_internal(self, search_internal):
-        """
-        Pickle the search-internal dict.
-
-        BlackJAX has no native on-disk format (cf. emcee's HDFBackend), so we
-        round-trip the chain + diagnostics via pickle. We bypass
-        ``self.paths.save_search_internal`` because the autofit dill path
-        chokes on a few numpy/jax-backed members; a direct pickle of
-        already-numpy data is robust.
-
-        ``NullPaths`` (no ``name``/``path_prefix``) sets
-        ``search_internal_path`` to ``None`` to suppress disk output —
-        skip silently in that case.
-        """
-        if self.paths.search_internal_path is None:
-            return
-        os.makedirs(self.paths.search_internal_path, exist_ok=True)
-        with open(self.backend_filename, "wb") as f:
-            pickle.dump(search_internal, f)
+        return self.checkpointer.load(self.paths)
 
     def _test_mode_samples_info(self) -> dict:
         return {
@@ -613,48 +590,32 @@ def samples_info_from(self, search_internal=None):
             "time": self.timer.time if self.timer else None,
         }
 
-    def samples_via_internal_from(self, model, search_internal=None):
+    def raw_samples_from(self, model, search_internal):
         """
-        Convert the BlackJAX chain pickled under ``search_internal/`` into a
-        standard ``SamplesMCMC``. NUTS samples are unweighted draws from the
-        posterior, so weights are 1.0.
+        The BlackJAX chains pickled under ``search_internal/`` as raw samples. NUTS
+        samples are unweighted draws from the posterior, so every weight is 1.
         """
-        search_internal = search_internal if search_internal is not None else self.backend
-
-        positions = search_internal["positions"]  # (n_samples, n_chains, n_dim)
-        log_likelihood_array = search_internal["log_likelihood_history"]  # (n_samples, n_chains)
-
         # Chain-major flatten: all of chain 0's samples (in draw order), then
-        # chain 1's, etc. -- matches `total_walkers = num_chains` below.
-        n_samples, n_chains, n_dim = positions.shape
-        positions_chain_major = np.moveaxis(positions, 0, 1).reshape(
-            n_chains * n_samples, n_dim
-        )
-        log_likelihood_chain_major = np.moveaxis(log_likelihood_array, 0, 1).reshape(
-            n_chains * n_samples
-        )
-
-        parameter_lists = positions_chain_major.tolist()
-        log_likelihood_list = [float(x) for x in log_likelihood_chain_major]
-        log_prior_list = model.log_prior_list_from(parameter_lists=parameter_lists)
-        weight_list = [1.0] * len(parameter_lists)
-
-        sample_list = Sample.from_lists(
-            model=model,
-            parameter_lists=parameter_lists,
-            log_likelihood_list=log_likelihood_list,
-            log_prior_list=log_prior_list,
-            weight_list=weight_list,
-        )
-
-        return SamplesMCMC(
-            model=model,
-            sample_list=sample_list,
-            samples_info=self.samples_info_from(search_internal=search_internal),
-            auto_correlation_settings=self.auto_correlation_settings,
-            auto_correlations=self.auto_correlations_from(
-                search_internal=search_internal
+        # chain 1's, etc. -- matches `total_walkers = num_chains` below. NUTS keeps
+        # every post-warmup draw (no burn-in removal or thinning).
+        parameters, log_likelihood = ChainPosterior.from_arrays(
+            chain=search_internal["positions"],  # (n_samples, n_chains, n_dim)
+            log_prob=search_internal["log_likelihood_history"],  # (n_samples, n_chains)
+            chain_major=True,
+            label="BlackJAXNUTS",
+        ).thin(discard=0, thin=1)
+
+        return RawSamples(
+            parameters=parameters,
+            log_likelihood=[float(x) for x in log_likelihood],
+            info=self.samples_info_from(search_internal=search_internal),
+            samples_kwargs=dict(
+                auto_correlation_settings=self.auto_correlation_settings,
+                auto_correlations=self.auto_correlations_from(
+                    search_internal=search_internal
+                ),
             ),
+            label="BlackJAXNUTS",
         )
 
     def auto_correlations_from(self, search_internal=None):
diff --git a/autofit/non_linear/search/mcmc/blackjax/smc/search.py b/autofit/non_linear/search/mcmc/blackjax/smc/search.py
index 0b3f5c48e..33e17dbf4 100644
--- a/autofit/non_linear/search/mcmc/blackjax/smc/search.py
+++ b/autofit/non_linear/search/mcmc/blackjax/smc/search.py
@@ -3,15 +3,13 @@
 import logging
 import math
 import os
-import pickle
 from pathlib import Path
 from typing import List, Optional, Tuple, TYPE_CHECKING
 
 import numpy as np
 
-from autonerves import conf
-
 from autofit.mapper.prior_model.abstract import AbstractPriorModel
+from autofit.non_linear.checkpoint import PICKLE_FILENAME, PickleCheckpointer
 from autofit.non_linear.objective import jax_objective
 from autofit.non_linear.initializer import Initializer, InitializerPrior
 from autofit.non_linear.search.mcmc.abstract_mcmc import AbstractMCMC
@@ -26,7 +24,7 @@
 )
 from autofit.non_linear.search.mcmc.blackjax.smc.samples import SamplesSMC
 from autofit.non_linear.test_mode import is_test_mode
-from autofit.non_linear.samples.sample import Sample
+from autofit.non_linear.samples.adapter import RawSamples
 from autofit.non_linear.search import capabilities as cap
 
 if TYPE_CHECKING:
@@ -52,6 +50,8 @@
 
 
 class SMC(AbstractMCMC):
+    samples_cls = SamplesSMC
+
     # Static capabilities (see ``autofit.non_linear.search.capabilities``); mirrored
     # by ``search/registry.py``. Never identifier fields.
     jax_use = cap.JaxUse.REQUIRED
@@ -69,6 +69,10 @@ class SMC(AbstractMCMC):
     test_mode_budget = {"num_particles": 16, "num_mcmc_steps": 2, "max_smc_steps": 5}
     objective_target = cap.ObjectiveTarget(cap.ObjectiveQuantity.LOG_LIKELIHOOD, cap.CoordinateSpace.PHYSICAL)
     invalid_value = -1.0e99
+    # The chain dict is plain NumPy, pickled (atomically) after every chunk and kept
+    # after completion: the samples cannot be rebuilt without it. Not a resume state:
+    # every ``_fit`` starts afresh (``resumable = False``).
+    checkpointer = PickleCheckpointer()
 
     __identifier_fields__ = (
         "num_particles",
@@ -312,8 +316,6 @@ def __init__(
 
         self.logger.debug("Creating SMC Search")
 
-        conf.instance["output"]["search_internal"] = True
-
     def apply_test_mode(self):
         logger.warning(
             "TEST MODE 1 (reduced iterations): SMC will run with num_particles<=16, num_mcmc_steps=2, "
@@ -661,35 +663,16 @@ def effective_step_size(self, n_dim: int) -> float:
 
     @property
     def backend_filename(self):
-        return self.paths.search_internal_path / "search_internal.pickle"
+        return self.paths.search_internal_path / PICKLE_FILENAME
 
     @property
     def backend(self) -> dict:
-        """Load the pickled search-internal dict written by ``_fit``."""
-        if not Path(self.backend_filename).is_file():
+        """The search-internal dict archived by ``_fit`` (``search_internal.pickle``)."""
+        if not self.checkpointer.exists(self.paths):
             raise FileNotFoundError(
-                f"search_internal.pickle does not exist at {self.paths.search_internal_path}"
+                f"{PICKLE_FILENAME} does not exist at {self.paths.search_internal_path}"
             )
-        with open(self.backend_filename, "rb") as f:
-            return pickle.load(f)
-
-    def output_search_internal(self, search_internal):
-        """
-        Pickle the search-internal dict.
-
-        BlackJAX has no native on-disk format (cf. emcee's HDFBackend), so we round-trip the particle cloud +
-        tempering diagnostics via pickle, bypassing ``self.paths.save_search_internal`` for the same reason
-        `BlackJAXNUTS` does: the autofit dill path chokes on a few numpy/jax-backed members, and a direct pickle
-        of already-numpy data is robust.
-
-        ``NullPaths`` (no ``name``/``path_prefix``) sets ``search_internal_path`` to ``None`` to suppress disk
-        output -- skip silently in that case.
-        """
-        if self.paths.search_internal_path is None:
-            return
-        os.makedirs(self.paths.search_internal_path, exist_ok=True)
-        with open(self.backend_filename, "wb") as f:
-            pickle.dump(search_internal, f)
+        return self.checkpointer.load(self.paths)
 
     def _test_mode_samples_info(self) -> dict:
         return {
@@ -746,23 +729,19 @@ def samples_info_from(self, search_internal=None):
             "time": self.timer.time if self.timer else None,
         }
 
-    def samples_via_internal_from(self, model, search_internal=None):
+    def raw_samples_from(self, model, search_internal):
         """
-        Convert the particle cloud pickled under ``search_internal/`` into a `SamplesSMC`.
+        The particle cloud pickled under ``search_internal/`` as raw samples.
 
         SMC particles are **weighted** samples: each carries the normalised importance weight blackjax
         assigns it at the current temperature, so the weights (not a uniform 1.0) are what the PDF, medians
-        and errors are computed from.
+        and errors are computed from. They are normalised here, by the search, as they always were; the
+        samples adapter never renormalises.
         """
-        search_internal = (
-            search_internal if search_internal is not None else self.backend
-        )
-
         parameter_lists = np.asarray(search_internal["particles"]).tolist()
         log_likelihood_list = [
             float(x) for x in np.asarray(search_internal["log_likelihood_list"])
         ]
-        log_prior_list = model.log_prior_list_from(parameter_lists=parameter_lists)
 
         weights = np.asarray(search_internal["weights"], dtype=float)
         weight_sum = weights.sum()
@@ -771,18 +750,12 @@ def samples_via_internal_from(self, model, search_internal=None):
         else:
             weights = weights / weight_sum
 
-        sample_list = Sample.from_lists(
-            model=model,
-            parameter_lists=parameter_lists,
-            log_likelihood_list=log_likelihood_list,
-            log_prior_list=log_prior_list,
-            weight_list=weights.tolist(),
-        )
-
-        return SamplesSMC(
-            model=model,
-            sample_list=sample_list,
-            samples_info=self.samples_info_from(search_internal=search_internal),
+        return RawSamples(
+            parameters=parameter_lists,
+            log_likelihood=log_likelihood_list,
+            weights=weights.tolist(),
+            info=self.samples_info_from(search_internal=search_internal),
+            label="SMC",
         )
 
 
diff --git a/autofit/non_linear/search/mcmc/emcee/search.py b/autofit/non_linear/search/mcmc/emcee/search.py
index 3b158e375..5f0dcc1cc 100644
--- a/autofit/non_linear/search/mcmc/emcee/search.py
+++ b/autofit/non_linear/search/mcmc/emcee/search.py
@@ -7,18 +7,20 @@
 
 import numpy as np
 
-from autonerves import conf
-
 from autofit import exc
 from autofit.mapper.model_mapper import ModelMapper
 from autofit.mapper.prior_model.abstract import AbstractPriorModel
+from autofit.non_linear.checkpoint import (
+    EMCEE_FILENAME,
+    NativeFileCheckpointer,
+    load_emcee_backend,
+)
 from autofit.non_linear.initializer import Initializer
 from autofit.non_linear.search.mcmc.abstract_mcmc import AbstractMCMC
 from autofit.non_linear.search.mcmc.auto_correlations import AutoCorrelationsSettings
 from autofit.non_linear.search.mcmc.auto_correlations import AutoCorrelations
 from autofit.non_linear.test_mode import is_test_mode
-from autofit.non_linear.samples.sample import Sample
-from autofit.non_linear.samples.mcmc import SamplesMCMC
+from autofit.non_linear.samples.adapter import ChainPosterior, RawSamples
 from autofit.non_linear.search import capabilities as cap
 
 if TYPE_CHECKING:
@@ -49,6 +51,12 @@ class Emcee(AbstractMCMC):
     test_mode_budget = {"nwalkers": 20, "nsteps": 10}
     objective_target = cap.ObjectiveTarget(cap.ObjectiveQuantity.LOG_POSTERIOR, cap.CoordinateSpace.PHYSICAL)
     invalid_value = -float("inf")
+    # emcee's own HDF backend is both the archive and the resume state; it is kept
+    # after completion because the samples cannot be rebuilt without it.
+    checkpointer = NativeFileCheckpointer(
+        EMCEE_FILENAME, loader=load_emcee_backend, retain_after_completion=True
+    )
+    resume_state = checkpointer
 
     __identifier_fields__ = ("nwalkers",)
 
@@ -127,8 +135,6 @@ def __init__(
 
         self.logger.debug("Creating Emcee Search")
 
-        conf.instance["output"]["search_internal"] = True
-
     def apply_test_mode(self):
         logger.warning(
             "TEST MODE 1 (reduced iterations): Sampler will run with "
@@ -252,20 +258,6 @@ def _fit(self, model: AbstractPriorModel, analysis):
 
         return search_internal, fitness
 
-    def output_search_internal(self, search_internal):
-        """
-        Output the sampler results to hard-disk in their internal format.
-
-        Emcee uses a backend to store and load results, therefore the outputting of the search internal to a
-        dill file is disabled.
-
-        Parameters
-        ----------
-        sampler
-            The nautilus sampler object containing the results of the model-fit.
-        """
-        pass
-
     def samples_info_from(self, search_internal=None, auto_correlations=None):
         search_internal = search_internal or self.backend
 
@@ -283,24 +275,23 @@ def samples_info_from(self, search_internal=None, auto_correlations=None):
             "time": self.timer.time if self.timer else None,
         }
 
-    def samples_via_internal_from(self, model, search_internal=None):
+    def raw_samples_from(self, model, search_internal):
         """
-        Returns a `Samples` object from the emcee internal results.
+        The emcee chain after burn-in and thinning, as raw samples.
 
-        The samples contain all information on the parameter space sampling (e.g. the parameters,
-        log likelihoods, etc.).
-
-        The internal search results are converted from the native format used by the search to lists of values
-        (e.g. `parameter_lists`, `log_likelihood_list`).
+        The parameters and log posteriors come out of emcee under the *same*
+        ``discard`` / ``thin`` (PyAutoFit#1628), through ``ChainPosterior.thin``,
+        which falls back to the whole chain when burn-in removal leaves no draws; the log likelihood is the log
+        posterior minus the log prior (``samples_from_raw``), and every draw has
+        weight 1.
 
         Parameters
         ----------
         model
             Maps input vectors of unit parameter values to physical values and model instances via priors.
+        search_internal
+            The ``emcee.EnsembleSampler`` or its ``HDFBackend``.
         """
-
-        search_internal = search_internal or self.backend
-
         # Computed once per conversion and shared by the burn-in, `samples_info` and
         # `SamplesMCMC`; each computation integrates the whole chain twice.
         auto_correlations = self.auto_correlations_from(
@@ -315,55 +306,22 @@ def samples_via_internal_from(self, model, search_internal=None):
             discard = int(3.0 * np.max(auto_correlations.times))
             thin = int(np.max(auto_correlations.times) / 2.0)
 
-        samples_after_burn_in = search_internal.get_chain(
-            discard=discard, thin=thin, flat=True
-        )
-
-        parameter_lists = samples_after_burn_in.tolist()
-
-        log_prior_list = model.log_prior_list_from(parameter_lists=parameter_lists)
-
-        # The log posteriors must be requested with the *same* `discard` and `thin`
-        # as the chain above, otherwise sample `i`'s parameters are paired with a
-        # different sample's log posterior (PyAutoFit#1628).
-        log_posterior_list = search_internal.get_log_prob(
-            discard=discard, thin=thin, flat=True
-        ).tolist()
-
-        if len(parameter_lists) != len(log_posterior_list):
-            raise exc.SamplesException(
-                "The number of Emcee parameter samples does not match the number of log "
-                "posterior values returned by the sampler: "
-                f"{len(parameter_lists)} parameter samples versus "
-                f"{len(log_posterior_list)} log posterior values. "
-                "The parameters and log posteriors are therefore not in correspondence "
-                "and the samples cannot be built."
-            )
-
-        log_likelihood_list = [
-            log_posterior - log_prior
-            for log_posterior, log_prior in zip(log_posterior_list, log_prior_list)
-        ]
+        parameters, log_posterior = ChainPosterior.from_sampler(
+            search_internal, label="Emcee"
+        ).thin(discard=discard, thin=thin)
 
-        weight_list = len(log_likelihood_list) * [1.0]
-
-        sample_list = Sample.from_lists(
-            model=model,
-            parameter_lists=parameter_lists,
-            log_likelihood_list=log_likelihood_list,
-            log_prior_list=log_prior_list,
-            weight_list=weight_list,
-        )
-
-        return SamplesMCMC(
-            model=model,
-            sample_list=sample_list,
-            samples_info=self.samples_info_from(
+        return RawSamples(
+            parameters=parameters,
+            log_posterior=log_posterior,
+            info=self.samples_info_from(
                 search_internal=search_internal,
                 auto_correlations=auto_correlations,
             ),
-            auto_correlation_settings=self.auto_correlation_settings,
-            auto_correlations=auto_correlations,
+            samples_kwargs=dict(
+                auto_correlation_settings=self.auto_correlation_settings,
+                auto_correlations=auto_correlations,
+            ),
+            label="Emcee",
         )
 
     def auto_correlations_from(self, search_internal=None):
@@ -390,7 +348,7 @@ def auto_correlations_from(self, search_internal=None):
 
     @property
     def backend_filename(self):
-        return self.paths.search_internal_path / "search_internal.hdf"
+        return self.paths.search_internal_path / EMCEE_FILENAME
 
     @property
     def backend(self) -> "emcee.backends.HDFBackend":
diff --git a/autofit/non_linear/search/mcmc/zeus/search.py b/autofit/non_linear/search/mcmc/zeus/search.py
index 9fbd0a62b..e0ac628f2 100644
--- a/autofit/non_linear/search/mcmc/zeus/search.py
+++ b/autofit/non_linear/search/mcmc/zeus/search.py
@@ -13,9 +13,8 @@
 from autofit.non_linear.search.mcmc.abstract_mcmc import AbstractMCMC
 from autofit.non_linear.search.mcmc.auto_correlations import AutoCorrelationsSettings
 from autofit.non_linear.search.mcmc.auto_correlations import AutoCorrelations
-from autofit.non_linear.samples.sample import Sample
 from autofit.non_linear.test_mode import is_test_mode
-from autofit.non_linear.samples.mcmc import SamplesMCMC
+from autofit.non_linear.samples.adapter import ChainPosterior, RawSamples
 from autofit.non_linear.search import capabilities as cap
 
 if TYPE_CHECKING:
@@ -297,10 +296,13 @@ def _fit(self, model: AbstractPriorModel, analysis):
 
         return search_internal, fitness
 
-    def samples_info_from(self, search_internal=None):
+    def samples_info_from(self, search_internal=None, auto_correlations=None):
         search_internal = search_internal or self.paths.load_search_internal()
 
-        auto_correlations = self.auto_correlations_from(search_internal=search_internal)
+        if auto_correlations is None:
+            auto_correlations = self.auto_correlations_from(
+                search_internal=search_internal
+            )
 
         return {
             "check_size": auto_correlations.check_size,
@@ -311,108 +313,53 @@ def samples_info_from(self, search_internal=None):
             "time": self.timer.time if self.timer else None,
         }
 
-    def samples_via_internal_from(self, model, search_internal=None):
+    def raw_samples_from(self, model, search_internal):
         """
-        Returns a `Samples` object from the zeus internal results.
-
-        The samples contain all information on the parameter space sampling (e.g. the parameters,
-        log likelihoods, etc.).
+        The zeus chain after burn-in and thinning, as raw samples.
 
-        The internal search results are converted from the native format used by the search to lists of values
-        (e.g. `parameter_lists`, `log_likelihood_list`).
+        The parameters and log posteriors come out of zeus under the *same*
+        ``discard`` / ``thin`` (PyAutoFit#1628), through ``ChainPosterior.thin``,
+        which falls back to the whole chain when burn-in removal leaves no draws; the log likelihood is the log
+        posterior minus the log prior (``samples_from_raw``), and every draw has
+        weight 1.
 
         Parameters
         ----------
         model
             Maps input vectors of unit parameter values to physical values and model instances via priors.
+        search_internal
+            The ``zeus.EnsembleSampler``.
         """
+        # Computed once per conversion and shared by the burn-in, `samples_info` and
+        # `SamplesMCMC`.
+        auto_correlations = self.auto_correlations_from(
+            search_internal=search_internal
+        )
 
-        search_internal = search_internal or self.paths.load_search_internal()
-
-        test_mode = is_test_mode()
-
-        if test_mode:
-
+        if is_test_mode():
             discard = 5
             thin = 5
 
         else:
-            auto_correlations = self.auto_correlations_from(
-                search_internal=search_internal
-            )
-
             discard = int(3.0 * np.max(auto_correlations.times))
             thin = int(np.max(auto_correlations.times) / 2.0)
 
-        samples_after_burn_in = search_internal.get_chain(
-            discard=discard, thin=thin, flat=True
-        )
-
-        if not test_mode and len(samples_after_burn_in) == 0:
-
-            logging.info(
-                """
-                After thinnng the Zeus samples in order to remove burn-in, no samples were left.
-                
-                To create a samples object containing samples, so that the code can continue and results
-                can be inspected, the full list of samples before removing burn-in has been used. This may 
-                indicate that the sampler has not converged and therefore your results may not be reliable.
-                
-                To fix this, run Zeus with more steps to ensure convergence is achieved or change the auto
-                correlation settings to be less aggressive in thinning samples.                
-                """
-            )
-
-            discard = 0
-            thin = 1
-
-            samples_after_burn_in = search_internal.get_chain(flat=True)
-
-        parameter_lists = samples_after_burn_in.tolist()
-
-        # The log posteriors must be requested with the *same* `discard` and `thin`
-        # as whichever branch above produced the chain, otherwise sample `i`'s
-        # parameters are paired with a different sample's log posterior, and the
-        # `zip` below silently truncates to the shorter list (PyAutoFit#1628).
-        log_posterior_list = search_internal.get_log_prob(
-            discard=discard, thin=thin, flat=True
-        ).tolist()
-
-        if len(parameter_lists) != len(log_posterior_list):
-            raise exc.SamplesException(
-                "The number of Zeus parameter samples does not match the number of log "
-                "posterior values returned by the sampler: "
-                f"{len(parameter_lists)} parameter samples versus "
-                f"{len(log_posterior_list)} log posterior values. "
-                "The parameters and log posteriors are therefore not in correspondence "
-                "and the samples cannot be built."
-            )
-
-        log_prior_list = model.log_prior_list_from(parameter_lists=parameter_lists)
+        parameters, log_posterior = ChainPosterior.from_sampler(
+            search_internal, label="Zeus"
+        ).thin(discard=discard, thin=thin)
 
-        log_likelihood_list = [
-            log_posterior - log_prior
-            for log_posterior, log_prior in zip(log_posterior_list, log_prior_list)
-        ]
-
-        weight_list = len(log_likelihood_list) * [1.0]
-
-        sample_list = Sample.from_lists(
-            model=model,
-            parameter_lists=parameter_lists,
-            log_likelihood_list=log_likelihood_list,
-            log_prior_list=log_prior_list,
-            weight_list=weight_list,
-        )
-
-        return SamplesMCMC(
-            model=model,
-            sample_list=sample_list,
-            samples_info=self.samples_info_from(search_internal=search_internal),
-            auto_correlation_settings=self.auto_correlation_settings,
-            auto_correlations=self.auto_correlations_from(
-                search_internal=search_internal
+        return RawSamples(
+            parameters=parameters,
+            log_posterior=log_posterior,
+            info=self.samples_info_from(
+                search_internal=search_internal,
+                auto_correlations=auto_correlations,
+            ),
+            samples_kwargs=dict(
+                auto_correlation_settings=self.auto_correlation_settings,
+                auto_correlations=auto_correlations,
             ),
+            label="Zeus",
         )
 
     def auto_correlations_from(self, search_internal=None):
diff --git a/autofit/non_linear/search/mle/bfgs/search.py b/autofit/non_linear/search/mle/bfgs/search.py
index 6dfa30563..ba8665bc1 100644
--- a/autofit/non_linear/search/mle/bfgs/search.py
+++ b/autofit/non_linear/search/mle/bfgs/search.py
@@ -13,8 +13,7 @@
     ClipperPriorBoxJoint,
 )
 from autofit.non_linear.initializer import AbstractInitializer
-from autofit.non_linear.samples.sample import Sample
-from autofit.non_linear.samples.samples import Samples
+from autofit.non_linear.samples.adapter import RawSamples
 from autofit.non_linear.search import capabilities as cap
 
 import numpy as np
@@ -361,55 +360,21 @@ def _fit(
 
         return search_internal, fitness
 
-    def samples_via_internal_from(
-        self, model: AbstractPriorModel, search_internal=None
-    ):
+    def raw_samples_from(self, model: AbstractPriorModel, search_internal):
         """
-        Returns a `Samples` object from the LBFGS internal results.
-
-        The samples contain all information on the parameter space sampling (e.g. the parameters,
-        log likelihoods, etc.).
-
-        The internal search results are converted from the native format used by the search to lists of values
-        (e.g. `parameter_lists`, `log_likelihood_list`).
+        The LBFGS internal results as raw samples: the full history when the start
+        point is plotted, otherwise the single optimum ``x``.
 
         Parameters
         ----------
         model
             Maps input vectors of unit parameter values to physical values and model instances via priors.
+        search_internal
+            The scipy ``OptimizeResult``-like object the search stores.
         """
-
-        if search_internal is None:
-            search_internal = self.paths.load_search_internal()
-
         x0 = search_internal.x
         total_iterations = search_internal.nit
 
-        if self.should_plot_start_point:
-
-            parameter_lists = search_internal.parameters_history_list
-            log_prior_list = model.log_prior_list_from(parameter_lists=parameter_lists)
-            log_likelihood_list = search_internal.log_likelihood_history_list
-
-        else:
-
-            parameter_lists = [list(x0)]
-            log_prior_list = model.log_prior_list_from(parameter_lists=parameter_lists)
-            log_posterior_list = np.array([search_internal.log_posterior_list])
-            log_likelihood_list = [
-                lp - prior for lp, prior in zip(log_posterior_list, log_prior_list)
-            ]
-
-        weight_list = len(log_likelihood_list) * [1.0]
-
-        sample_list = Sample.from_lists(
-            model=model,
-            parameter_lists=parameter_lists,
-            log_likelihood_list=log_likelihood_list,
-            log_prior_list=log_prior_list,
-            weight_list=weight_list,
-        )
-
         samples_info = {
             "total_iterations": total_iterations,
             # Prior-support enforcement (PyAutoFit#1477). The name only, with
@@ -424,10 +389,26 @@ def samples_via_internal_from(
             "time": self.timer.time if self.timer else None,
         }
 
-        return Samples(
-            model=model,
-            sample_list=sample_list,
-            samples_info=samples_info,
+        if self.should_plot_start_point:
+            return RawSamples(
+                parameters=search_internal.parameters_history_list,
+                log_likelihood=search_internal.log_likelihood_history_list,
+                info=samples_info,
+                label="LBFGS",
+            )
+
+        parameter_lists = [list(x0)]
+        log_prior_list = model.log_prior_list_from(parameter_lists=parameter_lists)
+        log_posterior_list = np.array([search_internal.log_posterior_list])
+
+        return RawSamples(
+            parameters=parameter_lists,
+            log_likelihood=[
+                lp - prior for lp, prior in zip(log_posterior_list, log_prior_list)
+            ],
+            log_prior=log_prior_list,
+            info=samples_info,
+            label="LBFGS",
         )
 
 
diff --git a/autofit/non_linear/search/mle/drawer/search.py b/autofit/non_linear/search/mle/drawer/search.py
index 3491776bb..741edb52b 100644
--- a/autofit/non_linear/search/mle/drawer/search.py
+++ b/autofit/non_linear/search/mle/drawer/search.py
@@ -7,7 +7,7 @@
 from autofit.mapper.prior_model.abstract import AbstractPriorModel
 from autofit.non_linear.search.mle.abstract_mle import AbstractMLE
 from autofit.non_linear.initializer import AbstractInitializer
-from autofit.non_linear.samples import Samples, Sample
+from autofit.non_linear.samples.adapter import RawSamples
 from autofit.non_linear.search import capabilities as cap
 
 if TYPE_CHECKING:
@@ -150,18 +150,32 @@ def run(self, ctx):
 
         return search_internal
 
-    def raw_samples_from(self, model, internal):
+    def raw_samples_from(self, model, search_internal):
         """
-        The drawn points as ``RawSamples``: log posteriors, unit weights, and the internal
-        dictionary itself as ``samples_info``.
-        """
-        from autofit.non_linear.search.fit_context import RawSamples
+        The Drawer's draws as raw samples: the parameter lists and log posteriors
+        ``run`` stores, each with weight 1. The stored dictionary (including the run
+        ``time`` recorded by ``run``) is the ``samples_info``.
+
+        The ``search_internal`` dictionary returned by `run` is used when passed, so a fit with `NullPaths`
+        (which writes nothing to disk) can still build its samples; otherwise it is loaded from the output folder.
 
+        Parameters
+        ----------
+        model
+            Maps input vectors of unit parameter values to physical values and model instances via priors.
+        search_internal
+            The dictionary of parameter lists, log posteriors and run time that `run` returns.
+        """
         return RawSamples(
-            parameters=internal["parameter_lists"],
-            log_posterior=internal["log_posterior_list"],
-            info=self.info_from(internal),
+            parameters=search_internal["parameter_lists"],
+            log_posterior=search_internal["log_posterior_list"],
+            info=self.info_from(search_internal),
+            label="Drawer",
         )
 
     def info_from(self, internal):
+        """
+        The stored dictionary itself (including the run ``time`` recorded by ``run``)
+        is the ``samples_info``.
+        """
         return internal
diff --git a/autofit/non_linear/search/mle/multi_start_gradient/search.py b/autofit/non_linear/search/mle/multi_start_gradient/search.py
index 9103e176b..490036fb9 100644
--- a/autofit/non_linear/search/mle/multi_start_gradient/search.py
+++ b/autofit/non_linear/search/mle/multi_start_gradient/search.py
@@ -26,8 +26,8 @@
 )
 from autofit.non_linear.scaler import AbstractScaler, ScalerNone
 from autofit.non_linear.initializer import AbstractInitializer
-from autofit.non_linear.samples.sample import Sample
-from autofit.non_linear.samples.samples import Samples
+from autofit.non_linear.checkpoint import DillCheckpointer
+from autofit.non_linear.samples.adapter import RawSamples
 from autofit.non_linear.search import capabilities as cap
 from autofit.non_linear.search.mle.multi_start_gradient.convergence import (
     MultiStartGradientConvergence,
@@ -67,6 +67,9 @@ class AbstractMultiStartGradient(AbstractMLE):
     # ``FitException`` early return (numpy) and a failed traced assertion still return
     # ``-inf``. Normalising it is the A3 objective adapter's job, not A1's.
     invalid_value = float("inf")
+    # The dill written after every step is both the archive and the resume state.
+    checkpointer = DillCheckpointer()
+    resume_state = checkpointer
 
     __identifier_fields__ = ("clipper",)
 
@@ -1997,19 +2000,14 @@ def _broad_starts(self, model, value_and_grad_single, jnp):
 
         return jnp.stack(starts)
 
-    def samples_via_internal_from(
-        self, model: AbstractPriorModel, search_internal=None
-    ):
+    def raw_samples_from(self, model: AbstractPriorModel, search_internal):
         """
-        Returns a `Samples` object from the MultiStartGradient internal results.
+        The MultiStartGradient internal results as raw samples.
 
-        The best-basin (maximum-log-posterior) start is the first sample; every
-        start's final point is retained as a diagnostic sample so per-start basin
-        spread can be inspected downstream.
+        The best-basin (maximum-log-posterior) start is the first sample (weight 1);
+        every start's final point is retained as a diagnostic sample (weight 0, log
+        likelihood NaN) so per-start basin spread can be inspected downstream.
         """
-        if search_internal is None:
-            search_internal = self.paths.load_search_internal()
-
         best_params = np.asarray(search_internal["best_params"])
         per_start_params = np.asarray(search_internal["params"])
         total_steps = int(search_internal["total_steps"])
@@ -2030,14 +2028,6 @@ def samples_via_internal_from(
 
         weight_list = [1.0] + [0.0] * (len(parameter_lists) - 1)
 
-        sample_list = Sample.from_lists(
-            model=model,
-            parameter_lists=parameter_lists,
-            log_likelihood_list=log_likelihood_list,
-            log_prior_list=log_prior_list,
-            weight_list=weight_list,
-        )
-
         samples_info = {
             "n_starts": self.n_starts,
             "n_steps": self.n_steps,
@@ -2128,10 +2118,13 @@ def samples_via_internal_from(
             "time": self.timer.time if self.timer else None,
         }
 
-        return Samples(
-            model=model,
-            sample_list=sample_list,
-            samples_info=samples_info,
+        return RawSamples(
+            parameters=parameter_lists,
+            log_likelihood=log_likelihood_list,
+            log_prior=log_prior_list,
+            weights=weight_list,
+            info=samples_info,
+            label=type(self).__name__,
         )
 
 
diff --git a/autofit/non_linear/search/nest/abstract_nest.py b/autofit/non_linear/search/nest/abstract_nest.py
index 32bfae58d..acefeb3b2 100644
--- a/autofit/non_linear/search/nest/abstract_nest.py
+++ b/autofit/non_linear/search/nest/abstract_nest.py
@@ -12,12 +12,15 @@
     InitializerParamBounds,
 )
 from autofit.non_linear.plot import corner_anesthetic
+from autofit.non_linear.samples.nest import SamplesNest
 
 if TYPE_CHECKING:
     from autofit.database.sqlalchemy_ import sa
 
 
 class AbstractNest(NonLinearSearch, ABC):
+    samples_cls = SamplesNest
+
     def __init__(
         self,
         name: Optional[str] = None,
diff --git a/autofit/non_linear/search/nest/dynesty/search/abstract.py b/autofit/non_linear/search/nest/dynesty/search/abstract.py
index d22bc8d29..e6328ec51 100644
--- a/autofit/non_linear/search/nest/dynesty/search/abstract.py
+++ b/autofit/non_linear/search/nest/dynesty/search/abstract.py
@@ -14,8 +14,7 @@
 from autofit.mapper.prior_model.abstract import AbstractPriorModel
 from autofit.non_linear.paths.null import NullPaths
 from autofit.non_linear.search.nest.abstract_nest import AbstractNest
-from autofit.non_linear.samples.sample import Sample
-from autofit.non_linear.samples.nest import SamplesNest
+from autofit.non_linear.samples.adapter import RawSamples
 from autofit.non_linear.test_mode import is_test_mode
 from autofit.non_linear.search import capabilities as cap
 
@@ -56,6 +55,12 @@ def __enter__(self):
     return ForkPool
 
 
+DYNESTY_FILENAME = "savestate.save"
+"""
+dynesty's checkpoint file, the search's resume state.
+"""
+
+
 def prior_transform(cube, model):
     phys_cube = model.vector_from_unit_vector(
         unit_vector=cube,
@@ -358,51 +363,49 @@ def samples_info_from(self, search_internal=None):
             "number_live_points": self.number_live_points,
         }
 
-    def samples_via_internal_from(self, model, search_internal=None):
+    def load_search_internal(self):
         """
-        Returns a `Samples` object from the dynesty internal results.
-
-        The samples contain all information on the parameter space sampling (e.g. the parameters,
-        log likelihoods, etc.).
+        The archive (``search_internal.dill``, written when the fit completed) or,
+        for a run that has not completed, the sampler restored from its resume state
+        (``savestate.save``).
+        """
+        try:
+            return super().load_search_internal()
+        except FileNotFoundError:
+            return self.search_internal
 
-        The internal search results are converted from the native format used by the search to lists of values
-        (e.g. `parameter_lists`, `log_likelihood_list`).
+    def raw_samples_from(self, model, search_internal):
+        """
+        The dynesty run's samples, log likelihoods and nested-sampling weights
+        (``exp(logwt - logz[-1])``) as raw samples.
 
         Parameters
         ----------
         model
             Maps input vectors of unit parameter values to physical values and model instances via priors.
+        search_internal
+            The dynesty sampler.
         """
-        search_internal = search_internal or self.search_internal
-
-        parameter_lists = search_internal.results.samples.tolist()
-        log_prior_list = model.log_prior_list_from(parameter_lists=parameter_lists)
-        log_likelihood_list = list(search_internal.results.logl)
-
-        weight_list = list(
-            np.exp(
-                np.asarray(search_internal.results.logwt)
-                - search_internal.results.logz[-1]
-            )
-        )
-
-        sample_list = Sample.from_lists(
-            model=model,
-            parameter_lists=parameter_lists,
-            log_likelihood_list=log_likelihood_list,
-            log_prior_list=log_prior_list,
-            weight_list=weight_list,
-        )
-
-        return SamplesNest(
-            model=model,
-            sample_list=sample_list,
-            samples_info=self.samples_info_from(search_internal=search_internal),
+        return RawSamples(
+            parameters=search_internal.results.samples.tolist(),
+            log_likelihood=list(search_internal.results.logl),
+            weights=list(
+                np.exp(
+                    np.asarray(search_internal.results.logwt)
+                    - search_internal.results.logz[-1]
+                )
+            ),
+            info=self.samples_info_from(search_internal=search_internal),
+            label="Dynesty",
         )
 
     @property
     def search_internal(self):
-        raise NotImplementedError
+        """
+        The sampler restored from the resume state (``savestate.save``) of a run that
+        has not completed.
+        """
+        return self.resume_state.load(self.paths)
 
     def iterations_from(
         self, search_internal: "Union[NestedSampler, DynamicNestedSampler]"
@@ -547,7 +550,7 @@ def checkpoint_file(self) -> str:
         If autofit is not outputting results to hard-disk (e.g. paths is `NullPaths`), this function is bypassed.
         """
         try:
-            return str(self.paths.search_internal_path / "savestate.save")
+            return str(self.paths.search_internal_path / DYNESTY_FILENAME)
         except TypeError:
             pass
 
@@ -619,17 +622,6 @@ def search_internal_from(
     ):
         raise NotImplementedError()
 
-    def output_search_internal(self, search_internal):
-
-        self.paths.save_search_internal(
-            obj=search_internal,
-        )
-
-        try:
-            os.remove(self.checkpoint_file)
-        except (TypeError, FileNotFoundError):
-            pass
-
     def check_pool(self, uses_pool: bool, pool):
         if (uses_pool and pool is None) or (not uses_pool and pool is not None):
             raise exc.SearchException(
diff --git a/autofit/non_linear/search/nest/dynesty/search/dynamic.py b/autofit/non_linear/search/nest/dynesty/search/dynamic.py
index 705bb8a98..6478fb125 100644
--- a/autofit/non_linear/search/nest/dynesty/search/dynamic.py
+++ b/autofit/non_linear/search/nest/dynesty/search/dynamic.py
@@ -3,10 +3,25 @@
 
 from autofit.mapper.prior_model.abstract import AbstractPriorModel
 
-from .abstract import AbstractDynesty, prior_transform
+from autofit.non_linear.checkpoint import NativeFileCheckpointer
+
+from .abstract import DYNESTY_FILENAME, AbstractDynesty, prior_transform
+
+
+def _restore_dynamic(path):
+    """
+    Restore a dynesty sampler from its ``savestate.save`` resume state.
+    """
+    from dynesty.dynesty import DynamicNestedSampler as Sampler
+
+    return Sampler.restore(str(path))
 
 
 class DynestyDynamic(AbstractDynesty):
+    # dynesty's own checkpoint, deleted once the fit completes (the archive is
+    # ``search_internal.dill``).
+    resume_state = NativeFileCheckpointer(DYNESTY_FILENAME, loader=_restore_dynamic)
+
     __identifier_fields__ = (
         "bound",
         "sample",
@@ -98,11 +113,6 @@ def run_kwargs(self) -> Dict:
             "nlive_init": self.nlive_init,
         }
 
-    @property
-    def search_internal(self):
-        from dynesty.dynesty import DynamicNestedSampler
-        return DynamicNestedSampler.restore(self.checkpoint_file)
-
     def search_internal_from(
             self,
             model: AbstractPriorModel,
diff --git a/autofit/non_linear/search/nest/dynesty/search/static.py b/autofit/non_linear/search/nest/dynesty/search/static.py
index b2bb2cf44..75b616529 100644
--- a/autofit/non_linear/search/nest/dynesty/search/static.py
+++ b/autofit/non_linear/search/nest/dynesty/search/static.py
@@ -7,12 +7,27 @@
 
 from autofit.mapper.prior_model.abstract import AbstractPriorModel
 
-from .abstract import AbstractDynesty, prior_transform
+from autofit.non_linear.checkpoint import NativeFileCheckpointer
+
+from .abstract import DYNESTY_FILENAME, AbstractDynesty, prior_transform
 
 if TYPE_CHECKING:
     from autofit.database.sqlalchemy_ import sa
 
+def _restore_static(path):
+    """
+    Restore a dynesty sampler from its ``savestate.save`` resume state.
+    """
+    from dynesty import NestedSampler as Sampler
+
+    return Sampler.restore(str(path))
+
+
 class DynestyStatic(AbstractDynesty):
+    # dynesty's own checkpoint, deleted once the fit completes (the archive is
+    # ``search_internal.dill``).
+    resume_state = NativeFileCheckpointer(DYNESTY_FILENAME, loader=_restore_static)
+
     __identifier_fields__ = (
         "nlive",
         "bound",
@@ -105,11 +120,6 @@ def run_kwargs(self) -> Dict:
             "logl_max": self.logl_max,
         }
 
-    @property
-    def search_internal(self):
-        from dynesty import NestedSampler as StaticSampler
-        return StaticSampler.restore(self.checkpoint_file)
-
     def search_internal_from(
         self,
         model: AbstractPriorModel,
diff --git a/autofit/non_linear/search/nest/nautilus/search.py b/autofit/non_linear/search/nest/nautilus/search.py
index c5802bfa3..9e57c9afc 100644
--- a/autofit/non_linear/search/nest/nautilus/search.py
+++ b/autofit/non_linear/search/nest/nautilus/search.py
@@ -13,11 +13,11 @@
 from autofit import exc
 from autofit.mapper.prior_model.abstract import AbstractPriorModel
 from autofit.mapper.prior.vectorized import PriorVectorized
+from autofit.non_linear.checkpoint import DillCheckpointer, NativeFileCheckpointer
 from autofit.non_linear.parallel import fork_context
 from autofit.non_linear.paths.null import NullPaths
 from autofit.non_linear.search.nest import abstract_nest
-from autofit.non_linear.samples.sample import Sample
-from autofit.non_linear.samples.nest import SamplesNest
+from autofit.non_linear.samples.adapter import RawSamples
 from autofit.non_linear.test_mode import is_test_mode
 from autofit.non_linear.search import capabilities as cap
 
@@ -27,6 +27,23 @@
 
 logger = logging.getLogger(__name__)
 
+NAUTILUS_FILENAME = "checkpoint.hdf5"
+"""
+nautilus's checkpoint file, the search's resume state.
+"""
+
+
+def _resumed_by_nautilus(path):
+    """
+    A nautilus checkpoint cannot be opened on its own: nautilus reopens it inside a
+    ``Sampler`` built with the run's prior and likelihood, which ``run`` does.
+    """
+    raise NotImplementedError(
+        f"The nautilus checkpoint {path} is resumed by nautilus.Sampler(filepath=...) "
+        "inside the search's run(ctx); it holds no samples until the fit completes."
+    )
+
+
 
 # How often `_LikelihoodWorkerPool.map` wakes up to check that the pool's
 # workers are the ones it was built with. Read at call time (not bound at
@@ -172,6 +189,14 @@ class Nautilus(abstract_nest.AbstractNest):
     test_mode_budget = {"n_like_max": 1}
     objective_target = cap.ObjectiveTarget(cap.ObjectiveQuantity.LOG_LIKELIHOOD, cap.CoordinateSpace.UNIT_CUBE)
     invalid_value = -1.0e99
+    # The archive is the sampler dill-dumped without its multiprocessing pools (which
+    # cannot be pickled); the resume state is nautilus's own ``checkpoint.hdf5``,
+    # which nautilus itself reopens (``Sampler(filepath=...)``) and which is deleted
+    # once the fit completes.
+    checkpointer = DillCheckpointer(strip_attributes=("pool_l", "pool_s"))
+    resume_state = NativeFileCheckpointer(
+        NAUTILUS_FILENAME, loader=_resumed_by_nautilus
+    )
 
     __identifier_fields__ = (
         "n_live",
@@ -405,7 +430,7 @@ def checkpoint_file(self):
         If autofit is not outputting results to hard-disk (e.g. paths is `NullPaths`), this function is bypassed.
         """
         try:
-            return self.paths.search_internal_path / "checkpoint.hdf5"
+            return self.paths.search_internal_path / NAUTILUS_FILENAME
         except TypeError:
             pass
 
@@ -706,37 +731,6 @@ def iterations_from(
 
         return iterations, total_iterations
 
-    def output_search_internal(self, search_internal):
-        """
-        Output the sampler results to hard-disk in their internal format.
-
-        The multiprocessing `Pool` object cannot be pickled and thus the sampler cannot be saved to hard-disk. This
-        function therefore extracts the necessary information from the sampler and saves it to hard-disk.
-
-        Parameters
-        ----------
-        sampler
-            The nautilus sampler object containing the results of the model-fit.
-        """
-
-        pool_l = search_internal.pool_l
-        pool_s = search_internal.pool_s
-
-        search_internal.pool_l = None
-        search_internal.pool_s = None
-
-        self.paths.save_search_internal(
-            obj=search_internal,
-        )
-
-        search_internal.pool_l = pool_l
-        search_internal.pool_s = pool_s
-
-        try:
-            os.remove(self.checkpoint_file)
-        except (TypeError, FileNotFoundError):
-            pass
-
     def samples_info_from(self, search_internal=None):
         return {
             "log_evidence": search_internal.log_z,
@@ -746,22 +740,26 @@ def samples_info_from(self, search_internal=None):
             "number_live_points": int(search_internal.n_live),
         }
 
-    samples_cls = SamplesNest
-
-    def raw_samples_from(self, model, internal):
-        """
-        Nautilus's posterior as ``RawSamples``: parameters, log likelihoods and the
-        exponentiated log weights.
+    def raw_samples_from(self, model, search_internal):
         """
-        from autofit.non_linear.search.fit_context import RawSamples
+        The nautilus posterior (``Sampler.posterior()``: points, log weights, log
+        likelihoods) as raw samples, with weights ``exp(log_w)``.
 
-        parameters, log_weights, log_likelihoods = internal.posterior()
+        Parameters
+        ----------
+        model
+            Maps input vectors of unit parameter values to physical values and model instances via priors.
+        search_internal
+            The nautilus sampler.
+        """
+        parameters, log_weights, log_likelihoods = search_internal.posterior()
 
         return RawSamples(
-            parameters=parameters.tolist(),
-            log_likelihood=log_likelihoods.tolist(),
-            weights=np.exp(log_weights).tolist(),
-            info=self.info_from(internal),
+            parameters=parameters,
+            log_likelihood=log_likelihoods,
+            weights=np.exp(log_weights),
+            info=self.info_from(search_internal),
+            label="Nautilus",
         )
 
     def info_from(self, internal):
diff --git a/autofit/non_linear/search/nest/nss/search.py b/autofit/non_linear/search/nest/nss/search.py
index d90ff2261..2856e34fb 100644
--- a/autofit/non_linear/search/nest/nss/search.py
+++ b/autofit/non_linear/search/nest/nss/search.py
@@ -9,11 +9,12 @@
 import numpy as np
 
 from autofit.mapper.prior_model.abstract import AbstractPriorModel
+from autofit.non_linear.checkpoint import NativeFileCheckpointer
 from autofit.non_linear.fitness import Fitness, get_log_likelihood_ceiling
 from autofit.non_linear.paths.null import NullPaths
 from autofit.non_linear.search.nest import abstract_nest
 from .samples import NSSamples
-from autofit.non_linear.samples.sample import Sample
+from autofit.non_linear.samples.adapter import RawSamples
 from autofit.non_linear.test_mode import is_test_mode
 from autofit.non_linear.search import capabilities as cap
 
@@ -193,6 +194,8 @@ def __init__(
 
 
 class NSS(abstract_nest.AbstractNest):
+    samples_cls = NSSamples
+
     # Static capabilities (see ``autofit.non_linear.search.capabilities``); mirrored
     # by ``search/registry.py``. Never identifier fields.
     jax_use = cap.JaxUse.REQUIRED
@@ -213,6 +216,9 @@ class NSS(abstract_nest.AbstractNest):
     # physical-space prior density.
     objective_target = cap.ObjectiveTarget(cap.ObjectiveQuantity.LOG_LIKELIHOOD, cap.CoordinateSpace.PHYSICAL)
     invalid_value = NSS_INVALID_LOG_LIKELIHOOD
+    # The resume state is NSS's own atomic ``nss_checkpoint.pkl`` (``_fit`` writes,
+    # reads and deletes it); the archive is the default ``search_internal.dill``.
+    resume_state = NativeFileCheckpointer(_CHECKPOINT_FILENAME, loader=_load_checkpoint)
 
     __identifier_fields__ = (
         "n_live",
@@ -657,18 +663,9 @@ def samples_info_from(self, search_internal: Optional[_NSSInternal] = None):
             "ess": int(search_internal.ess),
         }
 
-    def samples_via_internal_from(
-        self,
-        model: AbstractPriorModel,
-        search_internal: Optional[_NSSInternal] = None,
-    ):
-        """Convert the stored ``_NSSInternal`` holder into an ``NSSamples``."""
-
-        if search_internal is None:
-            search_internal = self.paths.load_search_internal()
-
-        parameter_lists = np.asarray(search_internal.positions).tolist()
-        log_likelihood_list = np.asarray(search_internal.loglikelihoods).tolist()
+    def raw_samples_from(self, model, search_internal: _NSSInternal):
+        """The stored ``_NSSInternal`` holder as raw samples, with its log weights
+        exponentiated and normalised by the search (as they always were)."""
 
         log_w = np.asarray(search_internal.log_weights)
         log_w_norm = log_w - log_w.max()
@@ -676,23 +673,11 @@ def samples_via_internal_from(
         weight_total = weights.sum()
         if weight_total > 0:
             weights = weights / weight_total
-        weight_list = weights.tolist()
-
-        log_prior_list = [
-            sum(model.log_prior_list_from_vector(vector=vector))
-            for vector in parameter_lists
-        ]
-
-        sample_list = Sample.from_lists(
-            model=model,
-            parameter_lists=parameter_lists,
-            log_likelihood_list=log_likelihood_list,
-            log_prior_list=log_prior_list,
-            weight_list=weight_list,
-        )
 
-        return NSSamples(
-            model=model,
-            sample_list=sample_list,
-            samples_info=self.samples_info_from(search_internal=search_internal),
+        return RawSamples(
+            parameters=np.asarray(search_internal.positions),
+            log_likelihood=np.asarray(search_internal.loglikelihoods),
+            weights=weights,
+            info=self.samples_info_from(search_internal=search_internal),
+            label="NSS",
         )
diff --git a/autofit/non_linear/search/registry.py b/autofit/non_linear/search/registry.py
index 18bcf0dca..2b95f21ad 100644
--- a/autofit/non_linear/search/registry.py
+++ b/autofit/non_linear/search/registry.py
@@ -149,6 +149,7 @@ def _capabilities(
     test_mode_budget: Dict[str, Any],
     objective_target: Tuple[str, str],
     invalid_value: str,
+    checkpointer: str = "dill",
 ) -> Dict[str, Any]:
     quantity, space = objective_target
     values = dict(
@@ -167,6 +168,7 @@ def _capabilities(
         test_mode_budget=dict(test_mode_budget),
         objective_target={"quantity": quantity, "space": space},
         invalid_value=invalid_value,
+        checkpointer=checkpointer,
     )
     return {name: values[name] for name in CAPABILITY_ATTRIBUTES}
 
@@ -299,6 +301,7 @@ def _multi_start(
             test_mode_budget=_EMCEE_ZEUS_BUDGET,
             objective_target=("log_posterior", "physical"),
             invalid_value="-inf",
+            checkpointer="native:search_internal.hdf",
         ),
         example=f"{_WORKSPACE}/mcmc.py#Search: Emcee",
         integration_test=f"{_WORKSPACE_TEST}/Emcee.py",
@@ -351,6 +354,7 @@ def _multi_start(
             test_mode_budget={"num_warmup": 20, "num_samples": 20, "num_chains": 2},
             objective_target=("log_posterior", "physical"),
             invalid_value="-inf",
+            checkpointer="pickle",
         ),
         example=f"{_WORKSPACE}/mcmc.py#Search: BlackJAXNUTS",
         integration_test=f"{_WORKSPACE_TEST}/BlackJAXNUTS.py",
@@ -381,6 +385,7 @@ def _multi_start(
             },
             objective_target=("log_likelihood", "physical"),
             invalid_value="-1e+99",
+            checkpointer="pickle",
         ),
         example=None,
         integration_test=f"{_WORKSPACE_TEST}/SMC.py",
diff --git a/docs/_generate_searches.py b/docs/_generate_searches.py
index 2daef4e9e..0747cc32b 100644
--- a/docs/_generate_searches.py
+++ b/docs/_generate_searches.py
@@ -246,26 +246,40 @@ def searches_index_rst(manifest: dict) -> str:
         "What each search's objective returns, the coordinates its backend proposes "
         "points in (``unit_cube`` backends map through the prior transform; the "
         "likelihood always receives physical parameters), the value it returns for an "
-        "invalid model, and the reduced budget ``PYAUTO_TEST_MODE=1`` applies.\n\n",
+        "invalid model, where it archives its internal state when a fit completes, "
+        "and the reduced budget ``PYAUTO_TEST_MODE=1`` applies.\n\n",
+    ]
+    header = [
+        "Search",
+        "Objective",
+        "Coordinates",
+        "Invalid value",
+        "Archive",
+        "Test-mode budget",
     ]
-    header = ["Search", "Objective", "Coordinates", "Invalid value", "Test-mode budget"]
     rows = [
         [
             _link(search),
             f"``{search['capabilities']['objective_target']['quantity']}``",
             f"``{search['capabilities']['objective_target']['space']}``",
             f"``{search['capabilities']['invalid_value']}``",
+            f"``{search['capabilities']['checkpointer']}``",
             _budget(search["capabilities"]["test_mode_budget"]),
         ]
         for search in manifest["searches"]
     ]
-    lines += _list_table(header, rows, "16 18 12 12 42")
+    lines += _list_table(header, rows, "14 16 11 11 16 32")
     lines += [
         "The invalid value is what the backend actually sees, not yet normalised. For "
         "the ``neg2_log_posterior`` minimizers it is conditional: a NaN or infinite "
         "likelihood is replaced by ``-inf`` and then multiplied by -2 into ``inf`` "
         "(the value listed), while a model rejected by a ``FitException`` or a failed "
         "assertion returns ``-inf``.\n\n",
+        "The archive is ``dill`` (``search_internal.dill``), ``pickle`` "
+        "(``search_internal.pickle``, plain NumPy data) or ``native:<file>`` (a file "
+        "the backend writes itself). Emcee, BlackJAXNUTS and SMC keep their archive "
+        "after a fit completes whatever the ``output.search_internal`` config says, "
+        "because their samples cannot be rebuilt without it.\n\n",
     ]
     lines += [
         "Column meanings\n",
diff --git a/docs/design/checkpointing.md b/docs/design/checkpointing.md
new file mode 100644
index 000000000..31e766c0f
--- /dev/null
+++ b/docs/design/checkpointing.md
@@ -0,0 +1,122 @@
+# Design note: archives, resume states and checkpointing
+
+Status: search-extensibility phase A3 (decision D5). Implemented in
+`autofit/non_linear/checkpoint.py`; the samples conversion that reads the archive is
+`autofit/non_linear/samples/adapter.py`. This note fixes the meaning of the two kinds
+of internal state, how they are written, what happens when they are corrupt, and which
+files survive the end of a fit.
+
+## Two kinds of internal state
+
+A search's `search_internal/` folder used to hold one thing that meant two things: a
+checkpoint an interrupted run resumes from, and the post-hoc archive of the backend's
+results. The config key `output.search_internal: false` deletes the folder at the end
+of a fit, so searches whose results could not be rebuilt without it (Emcee,
+BlackJAX NUTS, SMC) forced the key on in their `__init__`, which leaked to every later
+search in the process. A3 separates the two:
+
+| Role | Class attribute | Default | Written | Read by |
+|---|---|---|---|---|
+| **archive** (`result_internal`) | `NonLinearSearch.checkpointer` | `DillCheckpointer()` | at the end of the fit (`output_search_internal` -> `checkpointer.finalize`), and by searches that also save it mid-run | `Result.search_internal`, `samples_via_internal_from` on a completed folder |
+| **resume state** | `NonLinearSearch.resume_state` | `None` | by the backend during the run | the search's own resume path; `samples_via_internal_from` falls back to it when no archive exists yet |
+
+Only searches that genuinely resume an interrupted run declare a resume state, and
+`resumable = True` is declared only for them: Dynesty and Nautilus (native backend
+checkpoints), NSS (its own pickle), MultiStart (whose archive dill is also its resume
+state) and Emcee (whose HDF backend is both). BlackJAX NUTS and SMC start every `_fit`
+afresh, so they are not resumable and have no resume state.
+
+| Search | Archive | Resume state |
+|---|---|---|
+| Emcee | `NativeFileCheckpointer("search_internal.hdf", load_emcee_backend, retain_after_completion=True)` | the same file |
+| Zeus, BFGS, LBFGS, Drawer | `DillCheckpointer()` | none |
+| BlackJAX NUTS, SMC | `PickleCheckpointer()` (`search_internal.pickle`, retained) | none |
+| DynestyStatic, DynestyDynamic | `DillCheckpointer()` | `NativeFileCheckpointer("savestate.save", <Sampler>.restore)` |
+| Nautilus | `DillCheckpointer(strip_attributes=("pool_l", "pool_s"))` | `NativeFileCheckpointer("checkpoint.hdf5", ...)`; only nautilus can reopen it (inside a `Sampler` built with the run's prior and likelihood), so its loader raises `NotImplementedError` |
+| NSS | `DillCheckpointer()` | `NativeFileCheckpointer("nss_checkpoint.pkl", _load_checkpoint)` (written, read and deleted by NSS's own `_fit`) |
+| MultiStart* | `DillCheckpointer()` | the same file |
+
+Every existing filename is kept, so output folders written before A3 still load.
+
+## Atomic writes
+
+Every file autofit writes itself is written atomically: `Checkpointer.save` dumps to a
+sibling temporary file (`<name>.<pid>.tmp`) and `os.replace`s it into place
+(`autofit.tools.util.open_atomic`). A write interrupted by an exception, a
+`KeyboardInterrupt`, a full disk or a killed process therefore leaves the previous
+file whole, and the temporary file is removed on the exception paths.
+
+Native files are written by their backend, with the backend's own guarantees:
+dynesty's `save_sampler` writes `savestate.save.tmp` and renames it; NSS's
+`_save_checkpoint` uses the same tmp-and-rename; emcee's `HDFBackend` and nautilus's
+`checkpoint.hdf5` are HDF5 files appended in place by `h5py`, so an interrupted write
+can leave them unreadable. `NativeFileCheckpointer.save` and `finalize` are no-ops:
+autofit never writes or rewrites a native file.
+
+## Corruption
+
+A corrupt file is never silently replaced by a different source of samples:
+
+- `Checkpointer.load` raises whatever its deserialiser raises (`EOFError`,
+  `pickle.UnpicklingError`, `OSError` from `h5py`, ...). It never converts a corrupt
+  file into `FileNotFoundError`.
+- `NonLinearSearch.samples_from` falls back to `samples.csv` (with a WARNING) only for
+  `FileNotFoundError` and `NotImplementedError` (A0b's narrowed fallback), so a
+  corrupt archive surfaces as an error rather than as silently older samples.
+- On the resume path a search decides. MultiStart treats a corrupt dill as absent and
+  starts a fresh run with a warning (the state is only a resume aid). Dynesty, Nautilus
+  and NSS let their backend's error propagate, because resuming from a damaged
+  checkpoint is not safe and starting over silently would discard the run; deleting
+  the named file restarts the fit.
+
+## Retention precedence
+
+When a fit completes, `NonLinearSearch.post_fit_output` decides which files survive,
+in this order:
+
+1. **`checkpointer.retain_after_completion`.** When true (Emcee, BlackJAX NUTS, SMC),
+   the archive is always kept: their results cannot be rebuilt without it. This
+   replaces the three `conf.instance["output"]["search_internal"] = True` mutations
+   those searches made in `__init__`; constructing a search never changes the
+   configuration.
+2. **`output.search_internal`** (`config/output.yaml`). Otherwise, `true` keeps the
+   archive and `false` removes the whole `search_internal/` folder (archive, resume
+   state and timer files).
+3. **The resume state** is deleted once the fit completes, because there is nothing
+   left to resume, unless it is the archive's own file (Emcee, MultiStart) or declares
+   `retain_after_completion`.
+4. **Zipping.** Whatever survives is archived in `<identifier>.zip` and restored by
+   `paths.restore()`, so a completed folder unzipped later loads its archive again.
+
+A retained archive is therefore always readable after completion: a completed Dynesty
+folder keeps `search_internal.dill`, which is what `samples_via_internal_from` reads
+(`savestate.save` is the resume state and is removed).
+
+## Loading, and outputs written before A3
+
+Loading is injected rather than hard-coded in `paths`: `DirectoryPaths` knows its
+search (`paths.search`), and `DirectoryPaths.load_search_internal()` asks that
+search's `checkpointer`. `Result.search_internal` and every `samples_via_internal_from`
+go through it.
+
+Format-aware legacy detection is kept, not replaced by a dill-only fallback. The emcee
+probe that used to live inline in `DirectoryPaths.load_search_internal` ("a nasty
+hack") moved to `checkpoint.load_legacy_search_internal(directory)`, which tries, in
+order, `search_internal.hdf` (an emcee `HDFBackend`), `search_internal.pickle` (NUTS,
+SMC) and `search_internal.dill`. It is used when the paths has no search (a folder
+opened on its own) and when a checkpointer's own archive file is absent, so an output
+folder written before its search chose a strategy still loads.
+
+Per output kind:
+
+| Paths | Archive save | Archive load |
+|---|---|---|
+| `DirectoryPaths` (and `SubDirectoryPaths`) | the strategy's file under `files/search_internal/` | the strategy, then the legacy probe |
+| zipped output | restored from the `.zip` by `paths.restore()` | as `DirectoryPaths` |
+| `DatabasePaths` | no-op (results live in the database) | `None` |
+| `NullPaths` | no-op | `None` |
+| summary-only output (no `search_internal/`, e.g. `output.search_internal: false`) | n/a | `FileNotFoundError`, so `samples_from` and `Result.samples` use `samples.csv` |
+
+The aggregator never reads the archive: it rebuilds samples from `samples.csv` and
+`samples_info.json["class_path"]`, which A3 leaves unchanged (the golden fixtures in
+`test_autofit/non_linear/samples/golden/` pin `samples.csv` byte for byte).
diff --git a/docs/design/run_ctx.md b/docs/design/run_ctx.md
index 91c4cb18d..1f088d04b 100644
--- a/docs/design/run_ctx.md
+++ b/docs/design/run_ctx.md
@@ -129,7 +129,12 @@ pins the following details the table left open; each is flagged for review in th
 - **`ctx.update(internal)`** is synchronous: the backend is paused while it runs, so the
   internal state is read as it stands (no copy). The snapshot hand-off arrives with A3's
   samples adapter, which converts the samples once.
-- **`ctx.resume`, `ctx.checkpointer`** are `None` until A3 fills them; **`ctx.rng`** is a
+- **`ctx.checkpointer`** is the search's archive strategy, the class attribute
+  `NonLinearSearch.checkpointer` (A3, `autofit/non_linear/checkpoint.py`,
+  `docs/design/checkpointing.md`). **`ctx.resume`** is the search's
+  `NonLinearSearch.resume_state` when the interrupted run's file exists, else `None`: the
+  `Checkpointer` handle, not the loaded state, because a backend may reopen its own native
+  file (`Nautilus` resumes through `Sampler(filepath=...)`). **`ctx.rng`** is a
   `SeedSequence` of the search's `seed` attribute, if any, until A4's search-level seed.
 - **`ctx.close(failed)`** is the cleanup the lifecycle rules require; on failure it closes
   the pools the context built, shuts the quick-update threads down and releases the
@@ -141,5 +146,6 @@ pins the following details the table left open; each is flagged for review in th
 - **Search-side hooks** (not context members): `fitness_overrides(analysis)` lets a search
   add `Fitness` arguments (`Nautilus`: `batched`, `batch_size`), and `samples_cls` names the
   `Samples` class `raw_samples_from`'s result is converted into. `RawSamples` and
-  `samples_from_raw` live provisionally in `autofit/non_linear/search/fit_context.py` until
-  A3's `samples/adapter.py` replaces them.
+  `samples_from_raw` are A3's `autofit/non_linear/samples/adapter.py` (re-exported from
+  `autofit/non_linear/search/fit_context.py`), and `samples_via_internal_from` is
+  implemented once on `NonLinearSearch` over `raw_samples_from` for every search.
diff --git a/docs/index.md b/docs/index.md
index 723aa00fb..bf8b68910 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -105,6 +105,7 @@ cookbooks/multi_level_model
 searches/index
 searches/citations
 design/run_ctx
+design/checkpointing
 ```
 
 ```{toctree}
diff --git a/docs/searches/index.rst b/docs/searches/index.rst
index a8288e623..8bea2811b 100644
--- a/docs/searches/index.rst
+++ b/docs/searches/index.rst
@@ -220,95 +220,113 @@ Capabilities
 Objectives
 ----------
 
-What each search's objective returns, the coordinates its backend proposes points in (``unit_cube`` backends map through the prior transform; the likelihood always receives physical parameters), the value it returns for an invalid model, and the reduced budget ``PYAUTO_TEST_MODE=1`` applies.
+What each search's objective returns, the coordinates its backend proposes points in (``unit_cube`` backends map through the prior transform; the likelihood always receives physical parameters), the value it returns for an invalid model, where it archives its internal state when a fit completes, and the reduced budget ``PYAUTO_TEST_MODE=1`` applies.
 
 .. list-table::
    :header-rows: 1
-   :widths: 16 18 12 12 42
+   :widths: 14 16 11 11 16 32
 
    * - Search
      - Objective
      - Coordinates
      - Invalid value
+     - Archive
      - Test-mode budget
    * - :class:`~autofit.Emcee`
      - ``log_posterior``
      - ``physical``
      - ``-inf``
+     - ``native:search_internal.hdf``
      - ``nwalkers=20``, ``nsteps=10``
    * - :class:`~autofit.Zeus`
      - ``log_posterior``
      - ``physical``
      - ``-inf``
+     - ``dill``
      - ``nwalkers=20``, ``nsteps=10``
    * - :class:`~autofit.BlackJAXNUTS`
      - ``log_posterior``
      - ``physical``
      - ``-inf``
+     - ``pickle``
      - ``num_warmup=20``, ``num_samples=20``, ``num_chains=2``
    * - :class:`~autofit.SMC`
      - ``log_likelihood``
      - ``physical``
      - ``-1e+99``
+     - ``pickle``
      - ``num_particles=16``, ``num_mcmc_steps=2``, ``max_smc_steps=5``
    * - :class:`~autofit.DynestyStatic`
      - ``log_likelihood``
      - ``unit_cube``
      - ``-1e+99``
+     - ``dill``
      - ``maxcall=1``
    * - :class:`~autofit.DynestyDynamic`
      - ``log_likelihood``
      - ``unit_cube``
      - ``-1e+99``
+     - ``dill``
      - ``maxcall=1``
    * - :class:`~autofit.Nautilus`
      - ``log_likelihood``
      - ``unit_cube``
      - ``-1e+99``
+     - ``dill``
      - ``n_like_max=1``
    * - :class:`~autofit.NSS`
      - ``log_likelihood``
      - ``physical``
      - ``-1e+30``
+     - ``dill``
      - ``termination=-1.0``
    * - :class:`~autofit.Drawer`
      - ``log_posterior``
      - ``physical``
      - ``-inf``
+     - ``dill``
      - --
    * - :class:`~autofit.BFGS`
      - ``neg2_log_posterior``
      - ``physical``
      - ``inf``
+     - ``dill``
      - --
    * - :class:`~autofit.LBFGS`
      - ``neg2_log_posterior``
      - ``physical``
      - ``inf``
+     - ``dill``
      - --
    * - :class:`~autofit.MultiStartAdam`
      - ``neg2_log_posterior``
      - ``physical``
      - ``inf``
+     - ``dill``
      - ``convergence.window=1``, ``convergence.min_steps=1``
    * - :class:`~autofit.MultiStartADABelief`
      - ``neg2_log_posterior``
      - ``physical``
      - ``inf``
+     - ``dill``
      - ``convergence.window=1``, ``convergence.min_steps=1``
    * - :class:`~autofit.MultiStartLion`
      - ``neg2_log_posterior``
      - ``physical``
      - ``inf``
+     - ``dill``
      - ``convergence.window=1``, ``convergence.min_steps=1``
    * - :class:`~autofit.MultiStartProdigy`
      - ``neg2_log_posterior``
      - ``physical``
      - ``inf``
+     - ``dill``
      - ``convergence.window=1``, ``convergence.min_steps=1``
 
 The invalid value is what the backend actually sees, not yet normalised. For the ``neg2_log_posterior`` minimizers it is conditional: a NaN or infinite likelihood is replaced by ``-inf`` and then multiplied by -2 into ``inf`` (the value listed), while a model rejected by a ``FitException`` or a failed assertion returns ``-inf``.
 
+The archive is ``dill`` (``search_internal.dill``), ``pickle`` (``search_internal.pickle``, plain NumPy data) or ``native:<file>`` (a file the backend writes itself). Emcee, BlackJAXNUTS and SMC keep their archive after a fit completes whatever the ``output.search_internal`` config says, because their samples cannot be rebuilt without it.
+
 Column meanings
 ---------------
 
diff --git a/test_autofit/non_linear/paths/test_search_internal_loading.py b/test_autofit/non_linear/paths/test_search_internal_loading.py
new file mode 100644
index 000000000..c8d463624
--- /dev/null
+++ b/test_autofit/non_linear/paths/test_search_internal_loading.py
@@ -0,0 +1,154 @@
+"""
+Loading a search's internal state per output kind (search-extensibility phase A3, D5):
+directory (through the search's injected ``checkpointer`` or, without a search, the
+format-aware legacy probe), zipped, database, null and summary-only outputs.
+"""
+import logging
+from pathlib import Path
+
+import dill
+import numpy as np
+import pytest
+
+import autofit as af
+from autofit.non_linear.checkpoint import load_legacy_search_internal
+from autofit.non_linear.paths.directory import DirectoryPaths
+from autofit.non_linear.paths.null import NullPaths
+from autofit.non_linear.result import Result
+from autonerves import conf
+
+CONFIG = Path(__file__).parents[2] / "config"
+
+
+@pytest.fixture(name="output", autouse=True)
+def make_output(tmp_path):
+    conf.instance.push(new_path=str(CONFIG), output_path=str(tmp_path))
+    return tmp_path
+
+
+def _write_dill(paths, obj):
+    with open(paths.search_internal_path / "search_internal.dill", "wb") as f:
+        dill.dump(obj, f)
+
+
+def test_directory_without_a_search_uses_the_legacy_probe():
+    paths = DirectoryPaths(name="loading_legacy")
+    _write_dill(paths, {"state": 1})
+
+    assert paths.search is None
+    assert paths.load_search_internal() == {"state": 1}
+
+
+def test_directory_with_a_search_loads_through_its_checkpointer():
+    search = af.Drawer(name="loading_injected")
+    _write_dill(search.paths, {"state": 2})
+
+    assert search.paths.search is search
+    assert search.paths.load_search_internal() == {"state": 2}
+    assert search.load_search_internal() == {"state": 2}
+
+
+def _emcee_hdf(paths):
+    emcee = pytest.importorskip("emcee")
+    pytest.importorskip("h5py")
+
+    backend = emcee.backends.HDFBackend(
+        str(paths.search_internal_path / "search_internal.hdf")
+    )
+    sampler = emcee.EnsembleSampler(
+        nwalkers=4,
+        ndim=1,
+        log_prob_fn=lambda x: -0.5 * float(np.sum(x**2)),
+        backend=backend,
+    )
+    sampler.run_mcmc(np.random.default_rng(1).normal(size=(4, 1)), 3)
+    return emcee
+
+
+def test_emcee_hdf_loads_with_and_without_the_search():
+    """
+    The emcee HDF detection that used to live inline in
+    ``DirectoryPaths.load_search_internal`` is the Emcee checkpointer's loader, and is
+    kept as the first format of the legacy probe for a folder opened on its own.
+    """
+    search = af.Emcee(name="loading_emcee")
+    emcee = _emcee_hdf(search.paths)
+
+    via_search = search.paths.load_search_internal()
+    assert isinstance(via_search, emcee.backends.HDFBackend)
+    assert via_search.iteration == 3
+
+    legacy = load_legacy_search_internal(search.paths.search_internal_path)
+    assert isinstance(legacy, emcee.backends.HDFBackend)
+
+
+def test_zipped_output_loads_after_restore():
+    search = af.Drawer(name="loading_zipped")
+    paths = search.paths
+    _write_dill(paths, {"state": 3})
+
+    paths.zip_remove()
+    assert Path(paths._zip_path).exists()
+
+    paths.restore()
+
+    assert paths.load_search_internal() == {"state": 3}
+
+
+def test_database_paths_load_nothing(session):
+    paths = af.DatabasePaths(session=session, name="loading_database")
+
+    assert paths.load_search_internal() is None
+    assert Result(samples_summary=None, paths=paths).search_internal is None
+
+
+def test_null_paths_load_nothing():
+    paths = NullPaths()
+
+    assert paths.load_search_internal() is None
+    assert Result(samples_summary=None, paths=paths).search_internal is None
+
+
+@pytest.fixture(name="search_internal_off")
+def make_search_internal_off(tmp_path):
+    original_configs = list(conf.instance.configs)
+    config = tmp_path / "config_off"
+    config.mkdir()
+    (config / "output.yaml").write_text("search_internal: false\n")
+    conf.instance.push(new_path=str(config))
+    conf.instance["general"]["output"]["samples_to_csv"] = True
+    yield
+    conf.instance.configs = original_configs
+
+
+def test_summary_only_output_falls_back_to_samples_csv(
+    search_internal_off, monkeypatch, caplog
+):
+    """
+    A completed output without ``search_internal/`` (``output.search_internal:
+    false``) has no archive: loading raises ``FileNotFoundError``,
+    ``Result.search_internal`` is ``None`` and the samples come from ``samples.csv``.
+    """
+    monkeypatch.setenv("PYAUTO_TEST_MODE", "1")
+
+    analysis = af.ex.Analysis(data=np.full(10, 5.0), noise_map=np.ones(10))
+    search = af.Drawer(name="loading_summary_only", total_draws=5)
+    search.fit(model=af.Model(af.ex.Gaussian), analysis=analysis)
+
+    resumed = af.Drawer(name="loading_summary_only", total_draws=5)
+    result = resumed.fit(model=af.Model(af.ex.Gaussian), analysis=analysis)
+    resumed.paths.restore()
+
+    with pytest.raises(FileNotFoundError):
+        resumed.paths.load_search_internal()
+
+    assert Result(samples_summary=None, paths=resumed.paths).search_internal is None
+
+    with caplog.at_level(logging.WARNING):
+        samples = resumed.samples_from(resumed.paths.model)
+
+    assert len(samples.sample_list) == len(result.samples.sample_list)
+    assert any(
+        "could not be loaded from its internal results" in record.getMessage()
+        for record in caplog.records
+    )
diff --git a/test_autofit/non_linear/samples/golden/.gitattributes b/test_autofit/non_linear/samples/golden/.gitattributes
new file mode 100644
index 000000000..0c6cf9492
--- /dev/null
+++ b/test_autofit/non_linear/samples/golden/.gitattributes
@@ -0,0 +1,3 @@
+# Byte-exact golden fixtures: samples.csv is written with CRLF line endings by the csv
+# module and is compared byte for byte, so git must not normalise it.
+* -text
diff --git a/test_autofit/non_linear/samples/golden/__init__.py b/test_autofit/non_linear/samples/golden/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/test_autofit/non_linear/samples/golden/cases.py b/test_autofit/non_linear/samples/golden/cases.py
new file mode 100644
index 000000000..11b0c6299
--- /dev/null
+++ b/test_autofit/non_linear/samples/golden/cases.py
@@ -0,0 +1,129 @@
+"""
+The golden samples cases: how each stored backend internal is loaded and converted.
+
+Shared by ``generate.py`` (which writes the fixtures on the reference checkout) and
+``test_golden_samples.py`` (which replays the conversion and compares the tables), so the
+two can never drift apart.
+"""
+import json
+import pickle
+from pathlib import Path
+
+
+HERE = Path(__file__).parent
+
+#: name -> (search class path inside ``autofit``, constructor kwargs, internal file,
+#: modules the conversion needs).
+CASES = {
+    "emcee": ("Emcee", dict(nwalkers=12, nsteps=400), "emcee.pickle", ("emcee",)),
+    "dynesty": ("DynestyStatic", dict(nlive=40, maxcall=4000), "dynesty.pickle", ()),
+    "nautilus": ("Nautilus", dict(n_live=100, n_like_max=4000), "nautilus.pickle", ()),
+    "nuts": (
+        "BlackJAXNUTS",
+        dict(num_warmup=50, num_samples=60, num_chains=2),
+        "nuts.pickle",
+        ("jax", "blackjax"),
+    ),
+    "smc": ("SMC", dict(num_particles=200), "smc.pickle", ()),
+}
+
+
+def model_from(name):
+    from autonerves.dictable import from_dict
+
+    with open(HERE / name / "model.json") as f:
+        return from_dict(json.load(f))
+
+
+class DynestyResults:
+    """
+    The fields of a ``dynesty`` ``Results`` that sample conversion reads.
+    """
+
+    def __init__(self, samples, logl, logwt, logz, ncall):
+        self.samples = samples
+        self.logl = logl
+        self.logwt = logwt
+        self.logz = logz
+        self.ncall = ncall
+
+
+class DynestyInternal:
+    """
+    A stored ``dynesty`` sampler reduced to its ``results``: everything
+    ``samples_via_internal_from`` reads, without the sampler's likelihood, prior
+    transform, pool or bounds (which tie a pickled sampler to one dynesty version).
+    """
+
+    def __init__(self, results: dict):
+        self.results = DynestyResults(**results)
+
+
+class NautilusInternal:
+    """
+    A stored ``nautilus.Sampler`` reduced to what sample conversion reads: the
+    output of ``posterior()`` and the ``log_z`` / ``n_like`` / ``n_live``
+    attributes (the sampler's neural-network bounds make the real object ~15 MB).
+    """
+
+    def __init__(self, state: dict):
+        self._posterior = (
+            state["parameters"],
+            state["log_weights"],
+            state["log_likelihoods"],
+        )
+        self.log_z = state["log_z"]
+        self.n_like = state["n_like"]
+        self.n_live = state["n_live"]
+
+    def posterior(self):
+        return self._posterior
+
+
+def internal_from(name):
+    """
+    The stored internal of case ``name`` in the form the search converts: emcee's own
+    in-memory ``Backend`` (the same ``get_chain`` / ``get_log_prob`` slicing as its
+    ``HDFBackend``), the reduced dynesty and nautilus samplers above, and the plain
+    NumPy dicts NUTS and SMC pickle.
+    """
+    _, _, filename, _ = CASES[name]
+
+    with open(HERE / name / filename, "rb") as f:
+        state = pickle.load(f)
+
+    if name == "dynesty":
+        return DynestyInternal(state)
+    if name == "nautilus":
+        return NautilusInternal(state)
+    return state
+
+
+def search_from(name):
+    import autofit as af
+
+    class_name, kwargs, _, _ = CASES[name]
+    return getattr(af, class_name)(**kwargs)
+
+
+def samples_from(name):
+    """
+    Convert the stored internal of case ``name`` to a ``Samples`` object through the
+    search's public ``samples_via_internal_from``, under ``NullPaths`` (no timer).
+    """
+    search = search_from(name)
+    model = model_from(name)
+    return search.samples_via_internal_from(
+        model=model, search_internal=internal_from(name)
+    )
+
+
+def samples_info_for_comparison(samples):
+    """
+    ``samples_info`` without the run-dependent ``time`` key, JSON round-tripped so
+    NumPy scalars compare as the plain values ``samples_info.json`` stores.
+    """
+    from autofit.tools.util import NumpyEncoder
+
+    info = {k: v for k, v in samples.samples_info.items() if k != "time"}
+    return json.loads(json.dumps(info, cls=NumpyEncoder))
diff --git a/test_autofit/non_linear/samples/golden/dynesty/dynesty.pickle b/test_autofit/non_linear/samples/golden/dynesty/dynesty.pickle
new file mode 100644
index 000000000..5803d74d0
Binary files /dev/null and b/test_autofit/non_linear/samples/golden/dynesty/dynesty.pickle differ
diff --git a/test_autofit/non_linear/samples/golden/dynesty/model.json b/test_autofit/non_linear/samples/golden/dynesty/model.json
new file mode 100644
index 000000000..2fa3226b2
--- /dev/null
+++ b/test_autofit/non_linear/samples/golden/dynesty/model.json
@@ -0,0 +1,24 @@
+{
+    "class_path": "autofit.example.model.Gaussian",
+    "type": "model",
+    "arguments": {
+        "centre": {
+            "type": "Gaussian",
+            "id": 3,
+            "mean": 25.0,
+            "sigma": 10.0
+        },
+        "normalization": {
+            "type": "Uniform",
+            "id": 4,
+            "lower_limit": 0.0,
+            "upper_limit": 50.0
+        },
+        "sigma": {
+            "type": "LogUniform",
+            "id": 5,
+            "lower_limit": 0.1,
+            "upper_limit": 20.0
+        }
+    }
+}
\ No newline at end of file
diff --git a/test_autofit/non_linear/samples/golden/dynesty/samples.csv b/test_autofit/non_linear/samples/golden/dynesty/samples.csv
new file mode 100644
index 000000000..b0ec6630d
--- /dev/null
+++ b/test_autofit/non_linear/samples/golden/dynesty/samples.csv
@@ -0,0 +1,730 @@
+            centre,    normalization,              sigma,     log_likelihood,            log_prior,      log_posterior,                 weight
+21.090624895240904,45.07935892249056, 0.1485816977809966, -5986.966442202521,   1.8302042500623512, -5985.136237952458,                    0.0
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+ 2.223655039478732,46.54207452940832, 1.5176729039877959, -2929.390953741875,  -3.0109876262890642, -2932.401941368164,                    0.0
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diff --git a/test_autofit/non_linear/samples/golden/dynesty/samples_info.json b/test_autofit/non_linear/samples/golden/dynesty/samples_info.json
new file mode 100644
index 000000000..bde3c5f73
--- /dev/null
+++ b/test_autofit/non_linear/samples/golden/dynesty/samples_info.json
@@ -0,0 +1,7 @@
+{
+    "class_path": "autofit.non_linear.samples.nest.SamplesNest",
+    "log_evidence": -1794.7419342233939,
+    "number_live_points": 40,
+    "total_accepted_samples": 729,
+    "total_samples": 3777
+}
\ No newline at end of file
diff --git a/test_autofit/non_linear/samples/golden/emcee/emcee.pickle b/test_autofit/non_linear/samples/golden/emcee/emcee.pickle
new file mode 100644
index 000000000..5a8ed767b
Binary files /dev/null and b/test_autofit/non_linear/samples/golden/emcee/emcee.pickle differ
diff --git a/test_autofit/non_linear/samples/golden/emcee/model.json b/test_autofit/non_linear/samples/golden/emcee/model.json
new file mode 100644
index 000000000..2fa3226b2
--- /dev/null
+++ b/test_autofit/non_linear/samples/golden/emcee/model.json
@@ -0,0 +1,24 @@
+{
+    "class_path": "autofit.example.model.Gaussian",
+    "type": "model",
+    "arguments": {
+        "centre": {
+            "type": "Gaussian",
+            "id": 3,
+            "mean": 25.0,
+            "sigma": 10.0
+        },
+        "normalization": {
+            "type": "Uniform",
+            "id": 4,
+            "lower_limit": 0.0,
+            "upper_limit": 50.0
+        },
+        "sigma": {
+            "type": "LogUniform",
+            "id": 5,
+            "lower_limit": 0.1,
+            "upper_limit": 20.0
+        }
+    }
+}
\ No newline at end of file
diff --git a/test_autofit/non_linear/samples/golden/emcee/samples.csv b/test_autofit/non_linear/samples/golden/emcee/samples.csv
new file mode 100644
index 000000000..dba592954
--- /dev/null
+++ b/test_autofit/non_linear/samples/golden/emcee/samples.csv
@@ -0,0 +1,145 @@
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diff --git a/test_autofit/non_linear/samples/golden/emcee/samples_info.json b/test_autofit/non_linear/samples/golden/emcee/samples_info.json
new file mode 100644
index 000000000..d2c88df78
--- /dev/null
+++ b/test_autofit/non_linear/samples/golden/emcee/samples_info.json
@@ -0,0 +1,8 @@
+{
+    "change_threshold": 0.01,
+    "check_size": 100,
+    "class_path": "autofit.non_linear.samples.mcmc.SamplesMCMC",
+    "required_length": 50,
+    "total_steps": 400,
+    "total_walkers": 12
+}
\ No newline at end of file
diff --git a/test_autofit/non_linear/samples/golden/generate.py b/test_autofit/non_linear/samples/golden/generate.py
new file mode 100644
index 000000000..8223f176a
--- /dev/null
+++ b/test_autofit/non_linear/samples/golden/generate.py
@@ -0,0 +1,175 @@
+"""
+Regenerate the golden ``samples.csv`` fixtures (search-extensibility phase A3).
+
+Run ONLY on a checkout whose sample conversion is the reference: ``origin/main``
+before the A3 samples adapter landed (generated at 0dbf258c4). It runs a small real
+fit with each of Emcee, DynestyStatic, Nautilus, BlackJAXNUTS and SMC and stores
+the backend's own internal state; then, in a fresh process, it converts that stored
+state back through ``samples_via_internal_from`` (``cases.samples_from``) and writes
+the ``samples.csv`` and ``samples_info.json`` the conversion produces.
+``test_golden_samples.py`` replays the same conversion and requires ``samples.csv``
+to match: same header and row order, values equal to floating-point roundoff.
+
+    python test_autofit/non_linear/samples/golden/generate.py [case ...]
+
+The internals are stored so that they load without the fit that produced them:
+
+- ``emcee.hdf``: emcee's own ``HDFBackend`` file;
+- ``dynesty.dill`` / ``nautilus.dill``: the sampler with its likelihood, prior
+  transform and pools removed (conversion reads only the stored points, weights
+  and bounds);
+- ``nuts.pickle`` / ``smc.pickle``: the plain NumPy dicts those searches pickle.
+
+``model.json`` is the fitted model, so the fixtures do not depend on the prior
+configuration of the config directory the tests run under.
+"""
+import json
+import os
+import pickle
+import shutil
+import subprocess
+import sys
+import tempfile
+from pathlib import Path
+
+HERE = Path(__file__).parent
+ROOT = HERE.parents[3]
+
+
+def fit(name):
+    import numpy as np
+
+    import autofit as af
+    from autonerves import conf
+    from autonerves.dictable import to_dict
+
+    from test_autofit.non_linear.samples.golden.cases import CASES
+
+    class_name, kwargs, filename, _ = CASES[name]
+
+    rng = np.random.default_rng(1)
+    xvalues = np.arange(50)
+    data = 25.0 * np.exp(-0.5 * ((xvalues - 25.0) / 5.0) ** 2) + rng.normal(size=50)
+    noise_map = np.ones(50)
+
+    model = af.Model(af.ex.Gaussian)
+    # Non-uniform priors so the log prior column, and the log-likelihood derived from
+    # a log posterior (Emcee), are not trivially zero.
+    model.centre = af.GaussianPrior(mean=25.0, sigma=10.0)
+    model.normalization = af.UniformPrior(lower_limit=0.0, upper_limit=50.0)
+    model.sigma = af.LogUniformPrior(lower_limit=0.1, upper_limit=20.0)
+
+    output = Path(tempfile.mkdtemp())
+    conf.instance.push(
+        new_path=str(Path(af.__file__).parent / "config"), output_path=str(output)
+    )
+
+    np.random.seed(1)
+    use_jax = name in ("nuts", "smc")
+    analysis = af.ex.Analysis(data=data, noise_map=noise_map, use_jax=use_jax)
+    search = getattr(af, class_name)(name=f"golden_{name}", **kwargs)
+    result = search.fit(model=model, analysis=analysis)
+
+    directory = HERE / name
+    shutil.rmtree(directory, ignore_errors=True)
+    directory.mkdir()
+
+    if name == "emcee":
+        import emcee
+
+        search.paths.restore()
+        internal = emcee.backends.HDFBackend(
+            filename=str(search.paths.search_internal_path / "search_internal.hdf"),
+            read_only=True,
+        )
+        state = emcee.backends.Backend()
+        state.reset(internal.shape[0], internal.shape[1])
+        state.chain = internal.get_chain()
+        state.log_prob = internal.get_log_prob()
+        state.accepted = np.asarray(internal.accepted)
+        state.iteration = internal.iteration
+        state.random_state = internal.random_state
+        state.blobs = None
+    else:
+        internal = result.search_internal
+        if name == "dynesty":
+            results = internal.results
+            state = {
+                key: np.asarray(getattr(results, key))
+                for key in ("samples", "logl", "logwt", "logz", "ncall")
+            }
+        elif name == "nautilus":
+            parameters, log_weights, log_likelihoods = internal.posterior()
+            state = {
+                "parameters": parameters,
+                "log_weights": log_weights,
+                "log_likelihoods": log_likelihoods,
+                "log_z": internal.log_z,
+                "n_like": internal.n_like,
+                "n_live": internal.n_live,
+            }
+        else:
+            state = internal
+
+    with open(directory / filename, "wb") as f:
+        pickle.dump(state, f)
+
+    # The stored form must convert exactly as the live internal does.
+    from test_autofit.non_linear.samples.golden.cases import internal_from, search_from
+
+    def table(search_internal):
+        samples = search_from(name).samples_via_internal_from(
+            model=model, search_internal=search_internal
+        )
+        path = output / "check.csv"
+        samples.write_table(filename=path)
+        return path.read_bytes()
+
+    assert table(internal) == table(internal_from(name)), name
+
+    with open(directory / "model.json", "w") as f:
+        json.dump(to_dict(model), f, indent=4)
+
+    shutil.rmtree(output, ignore_errors=True)
+
+
+def convert(name):
+    from autofit.non_linear.paths.null import NullPaths  # noqa: F401
+
+    from test_autofit.non_linear.samples.golden.cases import (
+        HERE as CASES_HERE,
+        samples_from,
+        samples_info_for_comparison,
+    )
+
+    samples = samples_from(name)
+    samples.write_table(filename=CASES_HERE / name / "samples.csv")
+    with open(CASES_HERE / name / "samples_info.json", "w") as f:
+        json.dump(samples_info_for_comparison(samples), f, indent=4, sort_keys=True)
+
+
+def main():
+    from test_autofit.non_linear.samples.golden.cases import CASES
+
+    names = sys.argv[2:] if len(sys.argv) > 2 else list(CASES)
+
+    if len(sys.argv) > 1 and sys.argv[1] in ("fit", "convert"):
+        action = sys.argv[1]
+        for name in names:
+            (fit if action == "fit" else convert)(name)
+        return
+
+    env = {**os.environ, "PYAUTO_SKIP_VISUALIZATION": "1"}
+    env.pop("PYAUTO_TEST_MODE", None)
+    names = sys.argv[1:] or list(CASES)
+    for action in ("fit", "convert"):
+        for name in names:
+            # One fresh process per step: conversion must not see the fit's state.
+            subprocess.run(
+                [sys.executable, __file__, action, name], check=True, cwd=ROOT, env=env
+            )
+
+
+if __name__ == "__main__":
+    sys.path.insert(0, str(ROOT))
+    main()
diff --git a/test_autofit/non_linear/samples/golden/nautilus/model.json b/test_autofit/non_linear/samples/golden/nautilus/model.json
new file mode 100644
index 000000000..2fa3226b2
--- /dev/null
+++ b/test_autofit/non_linear/samples/golden/nautilus/model.json
@@ -0,0 +1,24 @@
+{
+    "class_path": "autofit.example.model.Gaussian",
+    "type": "model",
+    "arguments": {
+        "centre": {
+            "type": "Gaussian",
+            "id": 3,
+            "mean": 25.0,
+            "sigma": 10.0
+        },
+        "normalization": {
+            "type": "Uniform",
+            "id": 4,
+            "lower_limit": 0.0,
+            "upper_limit": 50.0
+        },
+        "sigma": {
+            "type": "LogUniform",
+            "id": 5,
+            "lower_limit": 0.1,
+            "upper_limit": 20.0
+        }
+    }
+}
\ No newline at end of file
diff --git a/test_autofit/non_linear/samples/golden/nautilus/nautilus.pickle b/test_autofit/non_linear/samples/golden/nautilus/nautilus.pickle
new file mode 100644
index 000000000..43ed61522
Binary files /dev/null and b/test_autofit/non_linear/samples/golden/nautilus/nautilus.pickle differ
diff --git a/test_autofit/non_linear/samples/golden/nautilus/samples.csv b/test_autofit/non_linear/samples/golden/nautilus/samples.csv
new file mode 100644
index 000000000..d1c96f5cf
--- /dev/null
+++ b/test_autofit/non_linear/samples/golden/nautilus/samples.csv
@@ -0,0 +1,3201 @@
+             centre,      normalization,              sigma,     log_likelihood,             log_prior,      log_posterior,                 weight
+  37.01836263934637,  48.86600736804591, 3.7291577471492054,-2678.5600263210076,   -2.0383876057582757,-2680.5984139267657,                    0.0
+ 39.458279203150084, 30.194573287424824,  3.665248541114958, -2738.764938571652,    -2.344105335385845,-2741.1090439070376,                    0.0
+  42.48480246107236,  33.99198582418544,  4.351143455487934,-2759.9201825602904,    -2.999030259402251, -2762.919212819693,                    0.0
+ 10.196270739092357,  9.531968748040459, 0.5383654119453887,-2754.0936732985333,  -0.47653425513548064,-2754.5702075536688,                    0.0
+  41.16969534919491, 23.752025512054182, 0.1168093475033989, -3147.882945196737,    0.8399169433665086,-3147.0430282533707,                    0.0
+  8.435993167943614,  40.98267742818721, 0.1912870185322003, -2751.657430330786,   0.28214865239126175, -2751.375281678395,                    0.0
+-1.3042620438018133,  44.14760668184716, 0.3580391821767445, -2734.076281921002,   -2.4324581571956694,-2736.5087400781977,                    0.0
+  3.148466475775699, 23.530589086304683, 0.1927341149657383,-3436.5032184411234,   -0.7410039042375733, -3437.244222345361,                    0.0
+ 20.250032490250724,  9.683914070189365, 18.702184548224185,-2674.4735630902715,   -3.0414512945165475, -2677.515014384788,                    0.0
+ 30.995579996610864, 12.587811732456528,0.21410698092498198, -2749.750508876181,    1.3615445805308894,  -2748.38896429565,                    0.0
+ 44.962198374953786,  17.85045725970294, 0.4275395517412434,-2871.3429178575566,   -1.1427383436130927,-2872.4856562011696,                    0.0
+  18.98575279755061,  43.99154344637318,0.29178988988214966, -3819.132495676654,    1.0508654438552156,-3818.0816302327985,                    0.0
+ 20.845776831973623,  7.614545700395064,   18.3596338728297, -2685.998012813104,   -2.9964422940753344,-2688.9944551071794,                    0.0
+  37.58136429981601, 14.344975882297023, 0.4532377559319384, -2776.302684748567,-0.0001151945719011227,-2776.3027999431392,                    0.0
+  32.32177945449317,0.04446418791433926, 1.4901539452624943,-2733.6772206008645,   -0.6669217054896391, -2734.344142306354,                    0.0
+ 22.027457672858056, 0.7715061711214111,0.10773299248781837, -2676.590614235917,    2.1839193653638738, -2674.406694870553,                    0.0
+ 13.081152732379062, 12.025992634570793,  5.376599344771709, -2683.428543557201,    -2.392350683213799, -2685.820894240415,                    0.0
+ 12.567891797817218,   22.9072729691093,  5.762030438212835,-2647.8452695674437,    -2.524076490980008,-2650.3693460584236,                    0.0
+ 11.654834455283126, 0.5744072401058786,  3.170375045071024,-2733.1525262408127,   -2.0443171087184737,-2735.1968433495313,                    0.0
+ 39.381726516707545, 19.699253518960845, 1.2214068024509928, -2778.571635961737,   -1.2341735991866338,-2779.8058095609235,                    0.0
+ 14.985632189031648, 48.117971733507574, 0.2924915625856346,  -4620.37798110336,    0.7278816453205589, -4619.650099458039,                    0.0
+  18.52622788498666, 3.2633889336568576, 0.8531216333517521,-2701.6531308703093,  -0.05069548011466593, -2701.703826350424,                    0.0
+  19.27683113593412,   48.7378493209368, 0.3934906675878352,-2919.7079737956806,    0.7689246185951263,-2918.9390491770855,                    0.0
+  38.17558831895943,  20.06709288082475, 0.5588966930140588,-2823.5479552900197,   -0.2861900080748834,-2823.8341452980944,                    0.0
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diff --git a/test_autofit/non_linear/samples/golden/nautilus/samples_info.json b/test_autofit/non_linear/samples/golden/nautilus/samples_info.json
new file mode 100644
index 000000000..d579ef3d1
--- /dev/null
+++ b/test_autofit/non_linear/samples/golden/nautilus/samples_info.json
@@ -0,0 +1,7 @@
+{
+    "class_path": "autofit.non_linear.samples.nest.SamplesNest",
+    "log_evidence": -1795.2976164340837,
+    "number_live_points": 100,
+    "total_accepted_samples": 3200,
+    "total_samples": 3200
+}
\ No newline at end of file
diff --git a/test_autofit/non_linear/samples/golden/nuts/model.json b/test_autofit/non_linear/samples/golden/nuts/model.json
new file mode 100644
index 000000000..2fa3226b2
--- /dev/null
+++ b/test_autofit/non_linear/samples/golden/nuts/model.json
@@ -0,0 +1,24 @@
+{
+    "class_path": "autofit.example.model.Gaussian",
+    "type": "model",
+    "arguments": {
+        "centre": {
+            "type": "Gaussian",
+            "id": 3,
+            "mean": 25.0,
+            "sigma": 10.0
+        },
+        "normalization": {
+            "type": "Uniform",
+            "id": 4,
+            "lower_limit": 0.0,
+            "upper_limit": 50.0
+        },
+        "sigma": {
+            "type": "LogUniform",
+            "id": 5,
+            "lower_limit": 0.1,
+            "upper_limit": 20.0
+        }
+    }
+}
\ No newline at end of file
diff --git a/test_autofit/non_linear/samples/golden/nuts/nuts.pickle b/test_autofit/non_linear/samples/golden/nuts/nuts.pickle
new file mode 100644
index 000000000..da36d86fb
Binary files /dev/null and b/test_autofit/non_linear/samples/golden/nuts/nuts.pickle differ
diff --git a/test_autofit/non_linear/samples/golden/nuts/samples.csv b/test_autofit/non_linear/samples/golden/nuts/samples.csv
new file mode 100644
index 000000000..4cc95953f
--- /dev/null
+++ b/test_autofit/non_linear/samples/golden/nuts/samples.csv
@@ -0,0 +1,121 @@
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diff --git a/test_autofit/non_linear/samples/golden/nuts/samples_info.json b/test_autofit/non_linear/samples/golden/nuts/samples_info.json
new file mode 100644
index 000000000..c7b3794a5
--- /dev/null
+++ b/test_autofit/non_linear/samples/golden/nuts/samples_info.json
@@ -0,0 +1,525 @@
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+        ],
+        [
+            55,
+            1
+        ],
+        [
+            56,
+            0
+        ],
+        [
+            56,
+            1
+        ],
+        [
+            57,
+            0
+        ],
+        [
+            57,
+            1
+        ],
+        [
+            58,
+            0
+        ],
+        [
+            58,
+            1
+        ],
+        [
+            59,
+            0
+        ],
+        [
+            59,
+            1
+        ]
+    ],
+    "ess_bulk_min": 2.7246029667882956,
+    "ess_bulk_per_param": [
+        2.7529628717730277,
+        43.99451221801807,
+        2.7246029667882956
+    ],
+    "ess_min": 1.017272130229103,
+    "ess_per_param": [
+        1.017272130229103,
+        39.447234941380074,
+        1.0174227011011134
+    ],
+    "ess_tail_min": 12.634789238645023,
+    "ess_tail_per_param": [
+        13.28768343748807,
+        67.9964391870483,
+        12.634789238645023
+    ],
+    "inverse_mass_matrix_kind": "none",
+    "mean_acceptance": 0.6220862356261563,
+    "n_divergent": 119,
+    "n_logl_evals": 595,
+    "num_chains": 2,
+    "num_samples": 60,
+    "num_warmup": 50,
+    "required_length": 50,
+    "rhat_max": 2.1018341033554115,
+    "rhat_per_param": [
+        2.0781350380956507,
+        1.0163052566620607,
+        2.1018341033554115
+    ],
+    "total_steps": 60,
+    "total_walkers": 2,
+    "tree_depth_histogram": {
+        "1": 18,
+        "2": 40,
+        "3": 39,
+        "4": 19,
+        "5": 4
+    },
+    "warm_start_source": "InitializerBall"
+}
\ No newline at end of file
diff --git a/test_autofit/non_linear/samples/golden/smc/model.json b/test_autofit/non_linear/samples/golden/smc/model.json
new file mode 100644
index 000000000..2fa3226b2
--- /dev/null
+++ b/test_autofit/non_linear/samples/golden/smc/model.json
@@ -0,0 +1,24 @@
+{
+    "class_path": "autofit.example.model.Gaussian",
+    "type": "model",
+    "arguments": {
+        "centre": {
+            "type": "Gaussian",
+            "id": 3,
+            "mean": 25.0,
+            "sigma": 10.0
+        },
+        "normalization": {
+            "type": "Uniform",
+            "id": 4,
+            "lower_limit": 0.0,
+            "upper_limit": 50.0
+        },
+        "sigma": {
+            "type": "LogUniform",
+            "id": 5,
+            "lower_limit": 0.1,
+            "upper_limit": 20.0
+        }
+    }
+}
\ No newline at end of file
diff --git a/test_autofit/non_linear/samples/golden/smc/samples.csv b/test_autofit/non_linear/samples/golden/smc/samples.csv
new file mode 100644
index 000000000..513f5092a
--- /dev/null
+++ b/test_autofit/non_linear/samples/golden/smc/samples.csv
@@ -0,0 +1,201 @@
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diff --git a/test_autofit/non_linear/samples/golden/smc/samples_info.json b/test_autofit/non_linear/samples/golden/smc/samples_info.json
new file mode 100644
index 000000000..2961f8b25
--- /dev/null
+++ b/test_autofit/non_linear/samples/golden/smc/samples_info.json
@@ -0,0 +1,88 @@
+{
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+        -30.430854721131045,
+        -48.95808070598031,
+        -85.36781286735801,
+        -176.44181828051043,
+        -385.20307699568895,
+        -605.1767735462062,
+        -409.92518366332735
+    ],
+    "max_log_likelihood_list": [
+        -1873.081612092529,
+        -1810.1515248986411,
+        -1816.5956630869034,
+        -1798.373343170037,
+        -1785.5740233574256,
+        -1785.5740233574256,
+        -1781.9668969251127,
+        -1781.9668969251127,
+        -1781.9668969251127,
+        -1781.9668969251127,
+        -1781.9668969251127,
+        -1781.9668969251127
+    ],
+    "n_smc_steps": 12,
+    "num_integration_steps": 8,
+    "num_mcmc_steps": 5,
+    "num_particles": 200,
+    "step_size": 0.019637378012158674,
+    "target_ess": 0.5,
+    "total_samples": 200,
+    "warm_start_source": "InitializerPrior",
+    "whitening_kind": "prior-scale diagonal (cold)"
+}
\ No newline at end of file
diff --git a/test_autofit/non_linear/samples/golden/smc/smc.pickle b/test_autofit/non_linear/samples/golden/smc/smc.pickle
new file mode 100644
index 000000000..71b7ecd99
Binary files /dev/null and b/test_autofit/non_linear/samples/golden/smc/smc.pickle differ
diff --git a/test_autofit/non_linear/samples/test_adapter.py b/test_autofit/non_linear/samples/test_adapter.py
new file mode 100644
index 000000000..e4914ee63
--- /dev/null
+++ b/test_autofit/non_linear/samples/test_adapter.py
@@ -0,0 +1,222 @@
+import numpy as np
+import pytest
+
+import autofit as af
+from autofit import exc
+from autofit.non_linear.samples.adapter import (
+    ChainPosterior,
+    RawSamples,
+    invalid_count,
+    is_invalid,
+    log_prior_list_from,
+    samples_from_raw,
+)
+from autofit.non_linear.search.mcmc.auto_correlations import AutoCorrelations
+
+
+@pytest.fixture(name="model")
+def make_model():
+    model = af.Model(af.ex.Gaussian)
+    model.centre = af.GaussianPrior(mean=1.0, sigma=2.0)
+    model.normalization = af.UniformPrior(lower_limit=0.0, upper_limit=10.0)
+    model.sigma = af.UniformPrior(lower_limit=0.0, upper_limit=10.0)
+    return model
+
+
+PARAMETERS = [[1.0, 2.0, 3.0], [1.5, 2.5, 3.5]]
+
+
+def test_log_likelihood_from_log_posterior(model):
+    log_prior = log_prior_list_from(model, PARAMETERS)
+
+    samples = samples_from_raw(
+        model,
+        RawSamples(parameters=PARAMETERS, log_posterior=[-1.0, -2.0]),
+        af.SamplesMCMC,
+    )
+
+    assert samples.log_prior_list == log_prior
+    assert samples.log_likelihood_list == [
+        -1.0 - log_prior[0],
+        -2.0 - log_prior[1],
+    ]
+    assert samples.weight_list == [1.0, 1.0]
+
+
+def test_weights_are_never_renormalised(model):
+    samples = samples_from_raw(
+        model,
+        RawSamples(
+            parameters=PARAMETERS, log_likelihood=[-1.0, -2.0], weights=[3.0, 5.0]
+        ),
+        af.SamplesNest,
+    )
+
+    assert samples.weight_list == [3.0, 5.0]
+
+
+def test_time_and_class_path_are_injected(model):
+    samples = samples_from_raw(
+        model,
+        RawSamples(parameters=PARAMETERS, log_likelihood=[-1.0, -2.0], info={"a": 1}),
+        af.Samples,
+        time=12.5,
+    )
+
+    assert samples.samples_info["a"] == 1
+    assert samples.samples_info["time"] == 12.5
+    assert samples.samples_info["class_path"].endswith(".Samples")
+
+
+def test_a_search_time_wins_over_the_injected_one(model):
+    samples = samples_from_raw(
+        model,
+        RawSamples(
+            parameters=PARAMETERS, log_likelihood=[-1.0, -2.0], info={"time": 3.0}
+        ),
+        af.Samples,
+        time=12.5,
+    )
+
+    assert samples.samples_info["time"] == 3.0
+
+
+@pytest.mark.parametrize(
+    "field, name",
+    [
+        ("log_posterior", "log posterior"),
+        ("log_likelihood", "log likelihood"),
+    ],
+)
+def test_length_mismatch_raises(model, field, name):
+    with pytest.raises(exc.SamplesException, match=f"number of {name} values"):
+        samples_from_raw(
+            model,
+            RawSamples(parameters=PARAMETERS, label="Test", **{field: [-1.0]}),
+            af.Samples,
+        )
+
+
+def test_weight_length_mismatch_raises(model):
+    with pytest.raises(exc.SamplesException, match="weight"):
+        samples_from_raw(
+            model,
+            RawSamples(
+                parameters=PARAMETERS, log_likelihood=[-1.0, -2.0], weights=[1.0]
+            ),
+            af.Samples,
+        )
+
+
+def test_exactly_one_of_log_likelihood_and_log_posterior():
+    with pytest.raises(exc.SamplesException):
+        RawSamples(parameters=PARAMETERS)
+    with pytest.raises(exc.SamplesException):
+        RawSamples(parameters=PARAMETERS, log_likelihood=[1.0], log_posterior=[1.0])
+
+
+@pytest.mark.parametrize("value", [-1.0e99, -1.0e30, -np.inf, -1.0e99 - 3.0])
+def test_sentinels_are_recognised(value):
+    assert is_invalid(value)
+
+
+@pytest.mark.parametrize("value", [0.0, -1.0e10, np.nan, "x"])
+def test_ordinary_values_are_not_sentinels(value):
+    assert not is_invalid(value)
+
+
+def test_sentinels_are_kept_as_stored(model):
+    samples = samples_from_raw(
+        model,
+        RawSamples(parameters=PARAMETERS, log_likelihood=[-1.0e99, -np.inf]),
+        af.Samples,
+    )
+
+    assert invalid_count(samples.log_likelihood_list) == 2
+    assert samples.log_likelihood_list[0] == -1.0e99
+    assert samples.log_likelihood_list[1] == -np.inf
+
+
+def _chain(n_steps=6, n_chains=2, n_dim=3):
+    chain = np.arange(n_steps * n_chains * n_dim, dtype=float).reshape(
+        n_steps, n_chains, n_dim
+    )
+    log_prob = chain[:, :, 0] * 10.0
+    return chain, log_prob
+
+
+def _emcee_backend(chain, log_prob):
+    emcee = pytest.importorskip("emcee")
+
+    backend = emcee.backends.Backend()
+    backend.reset(chain.shape[1], chain.shape[2])
+    backend.chain = chain
+    backend.log_prob = log_prob
+    backend.iteration = chain.shape[0]
+    backend.accepted = np.ones(chain.shape[1])
+    return backend
+
+
+def test_from_arrays_thins_like_emcee():
+    chain, log_prob = _chain()
+    backend = _emcee_backend(chain, log_prob)
+
+    for discard, thin in [(0, 1), (1, 2), (2, 3)]:
+        expected = ChainPosterior.from_sampler(backend).thin(discard, thin)
+        result = ChainPosterior.from_arrays(chain, log_prob).thin(discard, thin)
+
+        assert np.array_equal(result[0], expected[0])
+        assert np.array_equal(result[1], expected[1])
+
+
+def test_from_arrays_chain_major():
+    chain, log_prob = _chain()
+
+    parameters, log_values = ChainPosterior.from_arrays(
+        chain, log_prob, chain_major=True
+    ).thin(0, 1)
+
+    assert np.array_equal(parameters, np.moveaxis(chain, 0, 1).reshape(-1, 3))
+    assert np.array_equal(log_values, np.moveaxis(log_prob, 0, 1).reshape(-1))
+
+
+def test_empty_chain_falls_back_to_the_whole_chain():
+    chain, log_prob = _chain()
+
+    parameters, log_values = ChainPosterior.from_arrays(chain, log_prob).thin(
+        discard=100, thin=5
+    )
+
+    assert len(parameters) == chain.shape[0] * chain.shape[1]
+    assert len(log_values) == len(parameters)
+
+
+def test_emcee_gets_the_empty_chain_fallback(model, monkeypatch):
+    """
+    Emcee shares Zeus's fallback: burn-in removal that leaves nothing builds the
+    samples from the whole chain instead of failing.
+    """
+    monkeypatch.delenv("PYAUTO_TEST_MODE", raising=False)
+
+    rng = np.random.default_rng(1)
+    chain = rng.uniform(1.0, 3.0, size=(4, 6, 3))
+    backend = _emcee_backend(chain, -np.sum(chain**2, axis=2))
+
+    search = af.Emcee()
+
+    times = np.array([10.0, 10.0, 10.0])
+    monkeypatch.setattr(
+        type(search),
+        "auto_correlations_from",
+        lambda self, search_internal=None: AutoCorrelations(
+            check_size=2,
+            required_length=1,
+            change_threshold=0.01,
+            times=times,
+            previous_times=times,
+        ),
+    )
+
+    samples = search.samples_via_internal_from(model=model, search_internal=backend)
+
+    assert len(samples.sample_list) == 24
diff --git a/test_autofit/non_linear/samples/test_golden_samples.py b/test_autofit/non_linear/samples/test_golden_samples.py
new file mode 100644
index 000000000..eb18f270f
--- /dev/null
+++ b/test_autofit/non_linear/samples/test_golden_samples.py
@@ -0,0 +1,108 @@
+"""
+Golden ``samples.csv`` fixtures for the search-extensibility phase A3 samples adapter.
+
+Each case under ``golden/`` holds a backend internal stored from a small real fit
+(Emcee, DynestyStatic, Nautilus, BlackJAXNUTS, SMC), the fitted model, and the
+``samples.csv`` / ``samples_info.json`` that ``samples_via_internal_from`` produced
+from that internal on ``origin/main`` before the adapter landed (``golden/generate.py``).
+Converting the same internal today must reproduce ``samples.csv``: the same header,
+the same rows in the same order, and every parameter, log likelihood, log prior, log
+posterior and weight equal to within floating-point roundoff, with no renormalisation
+(decision D13).
+
+The comparison is to ``CSV_RTOL`` rather than byte for byte because NumPy's
+vectorised ``exp`` / ``log`` loops are dispatched per CPU (AVX2 vs AVX-512), so the
+same NumPy version on a different runner can land one unit in the last place away:
+on CI the dynesty and nautilus weights (and two nautilus log priors) differed from the
+AVX2-generated fixtures by at most 1 ULP (relative 2.2e-16). Any renormalisation,
+reordering or dropped row changes values by many orders of magnitude more.
+
+``samples_info`` is compared exactly except for the convergence diagnostics BlackJAX
+itself computes (ESS, bulk/tail ESS, rank-normalised R-hat), whose estimators change
+between BlackJAX releases; for those only the shape is pinned.
+"""
+import csv
+import importlib.util
+import json
+
+import numpy as np
+import pytest
+
+from test_autofit.non_linear.samples.golden import cases
+
+pytestmark = pytest.mark.filterwarnings("ignore::FutureWarning")
+
+
+def _skip_missing(name):
+    missing = [
+        module
+        for module in cases.CASES[name][3]
+        if importlib.util.find_spec(module) is None
+    ]
+    if missing:
+        pytest.skip(f"golden case {name} needs {missing}")
+
+
+@pytest.fixture(autouse=True)
+def reference_mode(monkeypatch):
+    # The fixtures were converted outside test mode, where the MCMC burn-in and
+    # thinning come from the auto-correlation times rather than fixed values.
+    monkeypatch.delenv("PYAUTO_TEST_MODE", raising=False)
+
+
+#: Relative tolerance on every numeric ``samples.csv`` entry: ~4500x the largest drift
+#: measured across CI runners (2.2e-16), far below any change the adapter could make.
+CSV_RTOL = 1e-12
+
+#: ``samples_info`` keys computed by ``blackjax.diagnostics`` rather than by autofit.
+BACKEND_DIAGNOSTIC_KEYS = (
+    "ess_min",
+    "ess_per_param",
+    "ess_bulk_min",
+    "ess_bulk_per_param",
+    "ess_tail_min",
+    "ess_tail_per_param",
+    "rhat_max",
+    "rhat_per_param",
+)
+
+
+def _read_csv(path):
+    with open(path, newline="") as f:
+        rows = list(csv.reader(f))
+    header = [column.strip() for column in rows[0]]
+    values = np.array([[float(value) for value in row] for row in rows[1:]])
+    return header, values
+
+
+@pytest.mark.parametrize("name", list(cases.CASES))
+def test_samples_csv_is_unchanged(name, tmp_path):
+    _skip_missing(name)
+
+    samples = cases.samples_from(name)
+    samples.write_table(filename=tmp_path / "samples.csv")
+
+    header, values = _read_csv(tmp_path / "samples.csv")
+    expected_header, expected_values = _read_csv(cases.HERE / name / "samples.csv")
+
+    assert header == expected_header
+    assert values.shape == expected_values.shape
+    np.testing.assert_allclose(values, expected_values, rtol=CSV_RTOL, atol=0.0)
+
+
+@pytest.mark.parametrize("name", list(cases.CASES))
+def test_samples_info_is_unchanged(name):
+    _skip_missing(name)
+
+    samples = cases.samples_from(name)
+
+    with open(cases.HERE / name / "samples_info.json") as f:
+        expected = json.load(f)
+
+    info = cases.samples_info_for_comparison(samples)
+
+    for key in BACKEND_DIAGNOSTIC_KEYS:
+        if key in expected:
+            assert np.shape(info.pop(key)) == np.shape(expected.pop(key)), key
+
+    assert info == expected
diff --git a/test_autofit/non_linear/search/test_conformance.py b/test_autofit/non_linear/search/test_conformance.py
index b513ced5b..78e186602 100644
--- a/test_autofit/non_linear/search/test_conformance.py
+++ b/test_autofit/non_linear/search/test_conformance.py
@@ -35,15 +35,6 @@
 
 ROSTER = searches_under_test()
 
-CONFIG_MUTATION_XFAIL = {
-    name: (
-        "__init__ sets conf.instance['output']['search_internal'] = True; "
-        "removed in search-extensibility phase A3."
-    )
-    for name in ("Emcee", "BlackJAXNUTS", "SMC")
-}
-
-
 def _params(xfails=None, raises=None):
     """
     The roster as pytest params, with strict xfail marks for the named entries.
@@ -163,8 +154,13 @@ def make_search_internal_off(tmp_path):
     conf.instance.configs = original_configs
 
 
-@pytest.mark.parametrize("entry", _params(CONFIG_MUTATION_XFAIL, raises=AssertionError))
+@pytest.mark.parametrize("entry", _params())
 def test_config_unchanged_by_construction(entry, search_internal_off):
+    """
+    Constructing a search never changes the configuration. Emcee, BlackJAXNUTS and
+    SMC used to force ``output.search_internal`` on; their archives now declare
+    ``retain_after_completion`` instead (search-extensibility phase A3).
+    """
     cls = _search_class(entry)
     assert conf.instance["output"]["search_internal"] is False
 
diff --git a/test_autofit/non_linear/search/test_conformance_backend.py b/test_autofit/non_linear/search/test_conformance_backend.py
index 69431769e..33a2679df 100644
--- a/test_autofit/non_linear/search/test_conformance_backend.py
+++ b/test_autofit/non_linear/search/test_conformance_backend.py
@@ -253,21 +253,6 @@
     "NSS": "NSSamples",
 }
 
-# Dynesty's ``samples_via_internal_from`` reads the in-run checkpoint
-# (``search_internal/savestate.save``), which ``output_search_internal`` deletes at the
-# end of a fit after dumping ``search_internal.dill``. A completed Dynesty output folder
-# therefore always falls back to ``samples.csv``. One checkpoint / internal-state path
-# per search is phase A3 (survey 01 §5.3 item 2, §9 phase 3).
-DISK_INTERNAL_XFAIL = {
-    name: (
-        "samples_via_internal_from reads the deleted savestate.save checkpoint, not the "
-        "search_internal.dill a completed fit keeps, so it falls back to samples.csv; "
-        "unified in search-extensibility phase A3."
-    )
-    for name in ("DynestyStatic", "DynestyDynamic")
-}
-
-
 # ``Emcee``, ``Zeus``, ``BlackJAXNUTS`` and ``SMC`` take ``auto_correlation_settings=
 # AutoCorrelationsSettings()`` as a default argument, built once at import time, so the
 # test-mode ``check_size = 1`` reduction applies only when ``PYAUTO_TEST_MODE`` is set
@@ -611,8 +596,14 @@ def raise_error(*args, **kwargs):
     assert any(_FALLBACK_MESSAGE in record.getMessage() for record in caplog.records)
 
 
-@pytest.mark.parametrize("entry", _params(DISK_INTERNAL_XFAIL, raises=AssertionError))
+@pytest.mark.parametrize("entry", _params())
 def test_restored_output_samples_from_none_loads_the_internal_state(entry, backend_run):
+    """
+    A completed, restored output folder keeps the archive ``samples_via_internal_from``
+    reads (for Dynesty ``search_internal.dill``; its ``savestate.save`` resume state is
+    deleted on completion), so the samples load from it without the ``samples.csv``
+    fallback.
+    """
     outcome = backend_run(entry).disk_samples_from
 
     assert outcome.error is None
diff --git a/test_autofit/non_linear/search/test_fit_context.py b/test_autofit/non_linear/search/test_fit_context.py
index 1cbd88c19..85ad6159a 100644
--- a/test_autofit/non_linear/search/test_fit_context.py
+++ b/test_autofit/non_linear/search/test_fit_context.py
@@ -64,7 +64,9 @@ def spy(self, ctx):
     for member in FROZEN_MEMBERS:
         assert hasattr(ctx, member), member
     assert ctx.test_mode_level == 1
-    assert ctx.resume is None and ctx.checkpointer is None
+    # A3: the archive strategy is the search's; Drawer has no resume state.
+    assert ctx.checkpointer == search.checkpointer
+    assert ctx.resume is None
 
     # Never stored on the search.
     assert not any(isinstance(value, FitContext) for value in vars(search).values())
@@ -155,3 +157,33 @@ def test_schedule_chunks_cover_the_budget():
     from autofit.non_linear.search.fit_context import UpdateSchedule
 
     assert list(UpdateSchedule(search).chunks(10)) == [4, 4, 2]
+
+
+def test_resume_is_the_resume_state_only_when_its_file_exists(tmp_path):
+    from autofit.non_linear.checkpoint import NativeFileCheckpointer
+
+    class ResumableToy(ToySearch):
+        resume_state = NativeFileCheckpointer("toy_checkpoint.bin", loader=open)
+
+    search = ResumableToy()
+    search.paths = af.DirectoryPaths(name="toy", path_prefix=str(tmp_path))
+    search.paths.model = af.Model(af.ex.Gaussian)
+
+    def ctx():
+        return FitContext(
+            search=search,
+            model=search.paths.model,
+            analysis=None,
+            fitness=None,
+            pool=None,
+            test_mode_level=0,
+        )
+
+    assert ctx().resume is None
+    assert ctx().checkpointer is search.checkpointer
+
+    path = search.resume_state.path(search.paths)
+    path.parent.mkdir(parents=True, exist_ok=True)
+    path.write_bytes(b"state")
+
+    assert ctx().resume is search.resume_state
diff --git a/test_autofit/non_linear/test_checkpoint.py b/test_autofit/non_linear/test_checkpoint.py
new file mode 100644
index 000000000..fe3d207ac
--- /dev/null
+++ b/test_autofit/non_linear/test_checkpoint.py
@@ -0,0 +1,309 @@
+"""
+The archive / resume-state strategies of ``autofit.non_linear.checkpoint`` (search-
+extensibility phase A3, decision D5; ``docs/design/checkpointing.md``).
+"""
+import os
+import pickle
+from pathlib import Path
+
+import dill
+import pytest
+
+import autofit as af
+from autofit.non_linear.checkpoint import (
+    DillCheckpointer,
+    NativeFileCheckpointer,
+    PickleCheckpointer,
+    checkpointer_kind,
+    load_legacy_search_internal,
+)
+from autofit.non_linear.paths.directory import DirectoryPaths
+from autofit.non_linear.paths.null import NullPaths
+from autonerves import conf
+
+from test_autofit.non_linear.search.conformance_roster import searches_under_test
+
+
+class Holder:
+    def __init__(self):
+        self.value = 1
+        self.pool = lambda: None  # a lambda dill can serialise; stripped anyway
+
+
+@pytest.fixture(name="paths")
+def make_paths(tmp_path):
+    conf.instance.push(
+        new_path=str(Path(__file__).parents[1] / "config"), output_path=str(tmp_path)
+    )
+    return DirectoryPaths(name="checkpoint_test")
+
+
+def test_dill_round_trip(paths):
+    checkpointer = DillCheckpointer()
+
+    assert not checkpointer.exists(paths)
+
+    checkpointer.save(paths, {"a": 1})
+
+    assert checkpointer.exists(paths)
+    assert checkpointer.load(paths) == {"a": 1}
+    assert checkpointer.path(paths) == paths.search_internal_path / "search_internal.dill"
+
+
+def test_dill_strips_and_restores_attributes(paths):
+    checkpointer = DillCheckpointer(strip_attributes=("pool",))
+    holder = Holder()
+    pool = holder.pool
+
+    checkpointer.save(paths, holder)
+
+    assert holder.pool is pool
+    assert checkpointer.load(paths).pool is None
+
+
+def test_pickle_round_trip(paths):
+    checkpointer = PickleCheckpointer()
+
+    checkpointer.finalize(paths, {"positions": [1, 2]})
+
+    with open(paths.search_internal_path / "search_internal.pickle", "rb") as f:
+        assert pickle.load(f) == {"positions": [1, 2]}
+    assert checkpointer.load(paths) == {"positions": [1, 2]}
+    assert checkpointer.retain_after_completion
+
+
+def test_writes_are_atomic(paths, monkeypatch):
+    checkpointer = PickleCheckpointer()
+    checkpointer.save(paths, {"good": True})
+
+    def broken_dump(obj, f):
+        f.write(b"partial")
+        raise RuntimeError("disk full")
+
+    monkeypatch.setattr(pickle, "dump", broken_dump)
+
+    with pytest.raises(RuntimeError):
+        checkpointer.save(paths, {"good": False})
+
+    monkeypatch.undo()
+    assert checkpointer.load(paths) == {"good": True}
+    assert not any(
+        name.endswith(".tmp") for name in os.listdir(paths.search_internal_path)
+    )
+
+
+def test_corrupt_archive_raises_rather_than_being_treated_as_absent(paths):
+    path = DillCheckpointer().path(paths)
+    path.write_bytes(b"not a dill")
+
+    with pytest.raises(Exception) as error:
+        DillCheckpointer().load(paths)
+
+    assert not isinstance(error.value, FileNotFoundError)
+
+
+def test_native_file(paths):
+    checkpointer = NativeFileCheckpointer("state.bin", loader=lambda p: p.read_bytes())
+
+    checkpointer.save(paths, "ignored")
+    checkpointer.finalize(paths, "ignored")
+    assert not checkpointer.exists(paths)
+
+    with pytest.raises(FileNotFoundError):
+        checkpointer.load(paths)
+
+    (paths.search_internal_path / "state.bin").write_bytes(b"abc")
+
+    assert checkpointer.load(paths) == b"abc"
+
+    checkpointer.discard(paths)
+    assert not checkpointer.exists(paths)
+    checkpointer.discard(paths)  # discarding an absent file is a no-op
+
+
+@pytest.mark.parametrize(
+    "checkpointer",
+    [
+        DillCheckpointer(),
+        PickleCheckpointer(),
+        NativeFileCheckpointer("x", loader=lambda p: None),
+    ],
+)
+def test_null_paths_store_nothing(checkpointer):
+    paths = NullPaths()
+
+    checkpointer.save(paths, {"a": 1})
+
+    assert checkpointer.path(paths) is None
+    assert not checkpointer.exists(paths)
+    assert checkpointer.load(paths) is None
+
+
+def test_database_paths_store_nothing(session):
+    paths = af.DatabasePaths(session=session, name="checkpoint_database")
+
+    DillCheckpointer().save(paths, {"a": 1})
+    PickleCheckpointer().save(paths, {"a": 1})
+
+    assert DillCheckpointer().load(paths) is None
+    assert PickleCheckpointer().load(paths) is None
+
+
+def test_legacy_detection(paths):
+    directory = paths.search_internal_path
+
+    with pytest.raises(FileNotFoundError):
+        load_legacy_search_internal(directory)
+
+    with open(directory / "search_internal.dill", "wb") as f:
+        dill.dump("dill", f)
+    assert load_legacy_search_internal(directory) == "dill"
+
+    with open(directory / "search_internal.pickle", "wb") as f:
+        pickle.dump("pickle", f)
+    assert load_legacy_search_internal(directory) == "pickle"
+
+
+def test_archive_falls_back_to_the_legacy_probe(paths):
+    """
+    An output folder written before its search chose a strategy (here: a pickle
+    archive where the search now expects a dill) still loads.
+    """
+    with open(paths.search_internal_path / "search_internal.pickle", "wb") as f:
+        pickle.dump("legacy", f)
+
+    assert DillCheckpointer().load(paths) == "legacy"
+
+
+def test_checkpointer_kind():
+    assert checkpointer_kind(DillCheckpointer()) == "dill"
+    assert checkpointer_kind(PickleCheckpointer()) == "pickle"
+    assert (
+        checkpointer_kind(NativeFileCheckpointer("f.hdf", loader=lambda p: None))
+        == "native:f.hdf"
+    )
+    assert checkpointer_kind(None) is None
+
+
+# --- per-search declarations -----------------------------------------------------------
+
+
+@pytest.mark.parametrize(
+    "entry", searches_under_test(), ids=lambda entry: entry.name
+)
+def test_resume_state_implies_resumable(entry):
+    missing = entry.missing_requirements()
+    if missing:
+        pytest.skip(f"{entry.name} needs {missing}")
+    cls = entry.resolve()
+
+    if cls.resume_state is not None:
+        assert cls.resumable
+    assert cls.checkpointer is not None
+
+
+def test_nuts_and_smc_have_no_resume_state():
+    from autofit.non_linear.search.mcmc.blackjax.nuts.search import BlackJAXNUTS
+    from autofit.non_linear.search.mcmc.blackjax.smc.search import SMC
+
+    for cls in (BlackJAXNUTS, SMC):
+        assert cls.resume_state is None
+        assert not cls.resumable
+        assert isinstance(cls.checkpointer, PickleCheckpointer)
+        assert cls.checkpointer.retain_after_completion
+
+
+def test_emcee_archive_is_its_hdf_backend():
+    assert isinstance(af.Emcee.checkpointer, NativeFileCheckpointer)
+    assert af.Emcee.checkpointer.filename == "search_internal.hdf"
+    assert af.Emcee.checkpointer.retain_after_completion
+    assert af.Emcee.resume_state is af.Emcee.checkpointer
+
+
+# --- retention precedence ---------------------------------------------------------------
+
+
+@pytest.fixture(name="search_internal_off")
+def make_search_internal_off(tmp_path):
+    original_configs = list(conf.instance.configs)
+    (tmp_path / "output.yaml").write_text("search_internal: false\n")
+    conf.instance.push(new_path=str(tmp_path))
+    yield
+    conf.instance.configs = original_configs
+
+
+def test_retention_precedence(search_internal_off):
+    """
+    ``retain_after_completion`` keeps the archive whatever the config says; otherwise
+    ``output.search_internal: false`` removes the folder.
+    """
+    assert conf.instance["output"]["search_internal"] is False
+
+    assert not af.DynestyStatic().retains_search_internal
+    assert not af.Drawer().retains_search_internal
+    assert af.Emcee().retains_search_internal
+    # Retained by declaration, not by mutating the configuration.
+    assert conf.instance["output"]["search_internal"] is False
+
+
+def test_output_search_internal_discards_the_resume_state(paths):
+    search = af.DynestyStatic()
+    search.paths = paths
+
+    savestate = paths.search_internal_path / "savestate.save"
+    savestate.write_bytes(b"resume")
+
+    search.output_search_internal({"archive": True})
+
+    assert not savestate.exists()
+    assert DillCheckpointer().load(paths) == {"archive": True}
+
+
+def test_output_search_internal_keeps_a_resume_state_that_is_the_archive(paths):
+    search = af.MultiStartAdam() if hasattr(af, "MultiStartAdam") else None
+    if search is None:
+        pytest.skip("MultiStartAdam is not exported")
+    search.paths = paths
+
+    search.output_search_internal({"params": [1.0]})
+
+    assert DillCheckpointer().load(paths) == {"params": [1.0]}
+
+
+def _archived(search):
+    import zipfile
+
+    with zipfile.ZipFile(search.paths._zip_path) as archive:
+        return set(archive.namelist())
+
+
+def test_a_retained_archive_survives_with_search_internal_off(
+    search_internal_off, monkeypatch
+):
+    """
+    With ``output.search_internal: false`` a completed Emcee fit still keeps its HDF
+    archive (``retain_after_completion``), while a Drawer fit loses its dill, and
+    the Emcee fit leaves the configuration untouched for the Drawer fit after it.
+    """
+    import numpy as np
+
+    monkeypatch.setenv("PYAUTO_TEST_MODE", "1")
+    model = af.Model(af.ex.Gaussian)
+    analysis = af.ex.Analysis(data=np.full(10, 5.0), noise_map=np.ones(10))
+
+    emcee = af.Emcee(
+        name="retention_emcee",
+        unique_tag="checkpoint_retention",
+        auto_correlation_settings=af.AutoCorrelationsSettings(
+            check_for_convergence=False, check_size=5, required_length=2
+        ),
+    )
+    emcee.fit(model=model, analysis=analysis)
+
+    drawer = af.Drawer(
+        name="retention_drawer", unique_tag="checkpoint_retention", total_draws=5
+    )
+    drawer.fit(model=af.Model(af.ex.Gaussian), analysis=analysis)
+
+    assert "files/search_internal/search_internal.hdf" in _archived(emcee)
+    assert not any("search_internal/" in name for name in _archived(drawer))