diff --git a/docs/dev/adrs/accepted/analysis-cif-fit-state.md b/docs/dev/adrs/accepted/analysis-cif-fit-state.md
index 9883a3b94..0cc5e658a 100644
--- a/docs/dev/adrs/accepted/analysis-cif-fit-state.md
+++ b/docs/dev/adrs/accepted/analysis-cif-fit-state.md
@@ -26,7 +26,7 @@ Analysis-owned fit state needs to persist:
 - pre-fit scalar snapshots for recovery workflows
 - compact status metadata for the latest saved fit projection
 - deterministic correlation summaries
-- minimizer-specific fit outputs on the active `_minimizer.*` category
+- minimizer-specific fit outputs on the paired `_fit_result.*` category
 - per-parameter posterior summaries on `_fit_parameter`
 - large posterior arrays and plot caches in `analysis/results.h5`
 
@@ -47,8 +47,7 @@ Persist analysis-owned fit state as explicit analysis categories in
 
 Do not add a dedicated `_fit_state` category or
 `_fit_state.schema_version`. Persisted fit state is detected from
-`_fit_result`, `_fit_parameter`, `_fit_parameter_correlation`, and
-fit-output fields on `_minimizer.*`.
+`_fit_result`, `_fit_parameter`, and `_fit_parameter_correlation`.
 
 ### Common fit-state categories
 
@@ -77,7 +76,8 @@ pre-fit scalar snapshots:
 - `posterior_gelman_rubin`
 - `posterior_effective_sample_size_bulk`
 
-`_fit_result` stores the latest saved fit header:
+`_fit_result` stores the latest saved fit header and scalar
+family-specific fit outputs:
 
 - `result_kind`
 - `success`
@@ -92,9 +92,9 @@ pairs are stored.
 
 ### Minimizer fit projection
 
-The active `_minimizer.*` category stores both user-selected solver
-inputs and fit-filled outputs. Deterministic minimizer classes store
-compact fit output counts:
+The active `_minimizer.*` category stores user-selected solver inputs
+only. Scalar outputs are written to the paired `_fit_result.*` category.
+Deterministic fit-result classes add compact fit output counts:
 
 - `objective_name`
 - `objective_value`
@@ -104,8 +104,6 @@ compact fit output counts:
 - `degrees_of_freedom`
 - `covariance_available`
 - `correlation_available`
-- `runtime_seconds`
-- `iterations_performed`
 - `exit_reason`
 
 Do not persist a `_deterministic_parameter_result` category. Final
@@ -113,8 +111,7 @@ deterministic parameter values and uncertainties already persist in the
 model CIF files, and restored deterministic ordering comes from
 `_fit_parameter`.
 
-Bayesian minimizer classes store sampler inputs and fit outputs under
-`_minimizer.*`, including:
+Bayesian minimizer classes store sampler inputs under `_minimizer.*`:
 
 - `sampling_steps`
 - `burn_in_steps`
@@ -123,7 +120,9 @@ Bayesian minimizer classes store sampler inputs and fit outputs under
 - `parallel_workers`
 - `initialization_method`
 - `random_seed`
-- `runtime_seconds`
+
+Bayesian fit-result classes store scalar outputs under `_fit_result.*`:
+
 - `point_estimate_name`
 - `sampler_completed`
 - `credible_interval_inner`
@@ -170,10 +169,10 @@ posterior displays.
 Load order is:
 
 1. standard analysis configuration
-2. common fit-state categories
-3. `_minimizer.*` fit-output fields according to the active
-   `_minimizer.type`
-4. posterior sidecar arrays when a Bayesian result is expected
+2. `_minimizer.*` settings according to the active `_minimizer.type`
+3. common and family-specific `_fit_result.*` fields on the paired class
+4. `_fit_parameter` and `_fit_parameter_correlation`
+5. posterior sidecar arrays when a Bayesian result is expected
 
 Persist backend runtime objects, optimizer instances, and raw driver
 payloads nowhere in this design.
diff --git a/docs/dev/adrs/accepted/display-ux.md b/docs/dev/adrs/accepted/display-ux.md
index 0b33b708e..379bdb8c8 100644
--- a/docs/dev/adrs/accepted/display-ux.md
+++ b/docs/dev/adrs/accepted/display-ux.md
@@ -200,6 +200,12 @@ Use these naming rules:
   path for `versus`.
 - `posterior.*` names are used only when posterior samples are required.
 
+`project.display.fit.results()` also prints a "Settings used" block
+above the result tables. The block is sourced from
+`analysis.minimizer.*` so the minimizer inputs and paired
+`analysis.fit_result.*` outputs are visible from the accepted display
+facade without adding a new `Analysis`-level display method.
+
 ## Rejected Alternatives
 
 Flat display facade:
diff --git a/docs/dev/adrs/accepted/minimizer-category-consolidation.md b/docs/dev/adrs/accepted/minimizer-category-consolidation.md
index f86b56a95..ed1e24f36 100644
--- a/docs/dev/adrs/accepted/minimizer-category-consolidation.md
+++ b/docs/dev/adrs/accepted/minimizer-category-consolidation.md
@@ -55,31 +55,34 @@ samplers.
 
 ## Decision
 
-### 1. Unified `minimizer` category replaces all sampler-input and fit-result categories
+### 1. Unified `minimizer` category replaces sampler-input categories
 
 Introduce a single switchable category `minimizer` on `Analysis`. Its
 concrete class is determined by `Analysis.minimizer_type`. The category
-holds both user-writable inputs and fit-filled outputs in one place.
+now holds user-writable minimizer inputs only. The later
+[`minimizer-input-output-split.md`](minimizer-input-output-split.md) ADR
+reverses the fit-output half of this rule: scalar fit outputs live on
+the paired `fit_result` category instead of on `minimizer`.
 
 The following categories are removed:
 
 - `bayesian_sampler` — fields move into the Bayesian concrete classes of
   `minimizer`.
 - `bayesian_result`, `bayesian_convergence` — fields move into the
-  Bayesian concrete classes of `minimizer` (`runtime_seconds`,
+  Bayesian concrete classes of `fit_result` (`fitting_time`,
   `acceptance_rate_mean`, `gelman_rubin_max`,
   `effective_sample_size_min`, `best_log_posterior`, …).
 - `deterministic_result` — fields move into the deterministic concrete
-  classes of `minimizer` (`runtime_seconds`, `iterations_performed`,
-  `exit_reason`, …).
+  classes of `fit_result` (`fitting_time`, `iterations`,
+  `objective_value`, `exit_reason`, …).
 - `bayesian_parameter_posterior` — replaced by `Parameter.posterior`
   (see §3).
 - `bayesian_distribution_cache`, `bayesian_pair_cache`,
   `bayesian_predictive_dataset` — replaced by HDF5 sidecar (see §4).
 
-`fit_result` and `fit_parameter` (analysis-owned bounds, success flag,
-reduced chi-square, message, fit time, iterations) remain unchanged as
-fit-mode-agnostic header categories.
+`fit_parameter` (analysis-owned bounds) remains a fit-state category.
+`fit_result` remains the common fit header category and is extended by
+the input/output split ADR with family-specific scalar outputs.
 
 ### 2. Selectors move to the `Analysis` owner
 
@@ -388,10 +391,11 @@ category's class-level `_engine_metadata` dict.
 
 ### Trade-offs
 
-- `minimizer` is the first category that mixes writable user inputs and
-  writable fit-filled outputs in the same scope. This is a small new
-  convention but is the natural generalization of how `Parameter`
-  already holds both user input and refined value on the same object.
+- `minimizer` no longer mixes writable user inputs and fit-filled
+  outputs in the same scope. That stricter boundary is recorded by
+  [`minimizer-input-output-split.md`](minimizer-input-output-split.md);
+  `Parameter` remains the refinement-in-place precedent for model values
+  rather than minimizer diagnostics.
 - The set of `_minimizer.*` tags present in CIF depends on the active
   `_fitting.minimizer_type`. Loading a CIF whose tags don't match the
   minimizer's allowed set raises (clear validation, not silent
@@ -461,9 +465,10 @@ category count for each new sampler and entrenches the convention break.
 
 ### D. Strict input-only `minimizer` plus a separate `fit_result`
 
-Keep the categories single-concept (inputs xor outputs) at the cost of
-two-place lookup for related info. Rejected in favour of the
-one-category-mixes-both shape (§1, §"Trade-offs") because the existing
-`Parameter` model already mixes input and refined value on the same
-object, and one-place discoverability is more valuable than strict
-purity.
+Originally rejected in favour of the one-category-mixes-both shape (§1,
+§"Trade-offs"). Reversed by
+[`minimizer-input-output-split.md`](minimizer-input-output-split.md)
+after implementation showed the `Parameter` analogy does not hold for
+minimizer settings versus fit diagnostics. The current design keeps
+`minimizer` input-only and moves scalar fit outputs to the paired
+`fit_result` category.
diff --git a/docs/dev/adrs/accepted/minimizer-input-output-split.md b/docs/dev/adrs/accepted/minimizer-input-output-split.md
new file mode 100644
index 000000000..13a33a5be
--- /dev/null
+++ b/docs/dev/adrs/accepted/minimizer-input-output-split.md
@@ -0,0 +1,407 @@
+# ADR: Minimizer Input/Output Split
+
+**Status:** Accepted **Date:** 2026-05-24
+
+## Status Note
+
+This proposal revisits and supersedes Alternative D in
+[`minimizer-category-consolidation.md`](minimizer-category-consolidation.md)
+("Strict input-only `minimizer` plus a separate `fit_result`"), which
+was rejected during the consolidation work on the assumption that the
+input/output mix on `analysis.minimizer` was symmetric with the
+input/output mix on `Parameter`. Implementation experience shows that
+analogy does not hold and the mix has produced measurable UX and
+duplication problems documented below.
+
+## Context
+
+After
+[`minimizer-category-consolidation.md`](minimizer-category-consolidation.md)
+landed, `analysis.minimizer` holds both writable user inputs and
+fit-filled outputs in a single namespace. A user typing
+`analysis.minimizer.help()` before any fit sees roughly twenty
+properties, the majority of which are `None`, `0`, or empty strings,
+with no signal which they may write and which the fit fills in.
+
+The current shape on the live category surfaces:
+
+- **Writable inputs:** `max_iterations` (LSQ); `sampling_steps`,
+  `burn_in_steps`, `thinning_interval`, `population_size`,
+  `parallel_workers`, `initialization_method`, `random_seed` (Bayesian).
+- **Fit-filled outputs (no public setter, only `_set_*` internals):**
+  `objective_name`, `objective_value`, `n_data_points`, `n_parameters`,
+  `n_free_parameters`, `degrees_of_freedom`, `covariance_available`,
+  `correlation_available`, `runtime_seconds`, `iterations_performed`,
+  `exit_reason` (LSQ); `runtime_seconds`, `point_estimate_name`,
+  `sampler_completed`, `credible_interval_inner`,
+  `credible_interval_outer`, `acceptance_rate_mean`, `gelman_rubin_max`,
+  `effective_sample_size_min`, `best_log_posterior` (Bayesian).
+
+Three current output fields straddle `analysis.minimizer` and
+`analysis.fit_result`:
+
+| Output concept          | Field on `analysis.minimizer` | Field on `analysis.fit_result`  | Relationship                                                                                                                                        |
+| ----------------------- | ----------------------------- | ------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------- |
+| Wall time               | `runtime_seconds`             | `fitting_time`                  | Real duplication — same scalar in two places.                                                                                                       |
+| Iteration count         | `iterations_performed` (LSQ)  | `iterations`                    | Real duplication — same scalar in two places.                                                                                                       |
+| Objective vs reduced χ² | `objective_value` (raw χ²)    | `reduced_chi_square` (χ² / dof) | Cross-category misplacement — two related but distinct scalars where the raw value sits on `minimizer` instead of with the rest of the fit outputs. |
+
+So a reader who wants "how long did the fit take" must already pick
+between two places. The current layout has both **input/output mixed
+inside `minimizer`** and **fit-output content split across `minimizer`
+and `fit_result`** (whether the two scalars per row are the same value
+or not). §2 resolves each row above explicitly.
+
+The consolidation ADR's two-line argument for keeping inputs and outputs
+together was:
+
+1. **Symmetry with `Parameter`.** A `Parameter` holds both its user-set
+   initial value and its refined value plus uncertainty.
+2. **One-place discoverability** > strict purity.
+
+The symmetry argument does not actually transfer. `Parameter.value` and
+`Parameter.uncertainty` describe the _same scalar quantity_ before and
+after refinement; they share a name, semantics, and lifecycle.
+`minimizer.sampling_steps` (a user request) and
+`minimizer.gelman_rubin_max` (a diagnostic the sampler reports) are
+about completely different things and only share a namespace because the
+consolidation ADR put them there. "One-place" is also already broken:
+scalar fit outputs are split across `minimizer`, `fit_result`,
+`fit_parameters`, and `fit_parameter_correlations` today.
+
+## Decision
+
+### 1. Split `analysis.minimizer` into inputs and outputs
+
+`analysis.minimizer` keeps only **writable user settings**. The
+fit-filled output fields move to `analysis.fit_result`, which gets a
+class hierarchy parallel to `minimizer` so each minimizer family can
+declare its own output schema.
+
+After this ADR:
+
+| Category                              | Role                                               | Writable                    |
+| ------------------------------------- | -------------------------------------------------- | --------------------------- |
+| `analysis.minimizer`                  | user-supplied settings                             | yes                         |
+| `analysis.fit_result`                 | scalar fit outputs                                 | no (internal `_set_*` only) |
+| `analysis.fit_parameters`             | per-parameter snapshots and posterior summary rows | no                          |
+| `analysis.fit_parameter_correlations` | upper-triangle correlation rows                    | no                          |
+
+**`fit_result` is not a user-facing switchable category.** It is an
+internal projection paired with the active `minimizer`. It does not
+expose `fit_result.type` or `fit_result.show_supported()`; the only way
+the user changes the active `fit_result` class is by setting
+`analysis.minimizer.type`, which the owner's `_swap_minimizer` hook uses
+to instantiate both `self._minimizer` and `self._fit_result` atomically.
+This is an explicit, documented exception to the global selector
+contract from
+[`switchable-category-owned-selectors.md`](switchable-category-owned-selectors.md)
+§1 because there is no user choice involved at the `fit_result` level —
+the minimizer family fully determines the result schema. See the new
+exception text added to that ADR (listed under §"ADRs amended").
+
+**Family mapping is one-to-one between minimizer family and result
+class.** Every minimizer registered under `MinimizerTypeEnum` maps to
+exactly one `FitResult` concrete class according to its family:
+
+| `MinimizerTypeEnum` member | Minimizer family | Paired `FitResult` class |
+| -------------------------- | ---------------- | ------------------------ |
+| `LMFIT`                    | LSQ              | `LeastSquaresFitResult`  |
+| `LMFIT_LEASTSQ`            | LSQ              | `LeastSquaresFitResult`  |
+| `LMFIT_LEAST_SQUARES`      | LSQ              | `LeastSquaresFitResult`  |
+| `DFOLS`                    | LSQ              | `LeastSquaresFitResult`  |
+| `BUMPS`                    | LSQ              | `LeastSquaresFitResult`  |
+| `BUMPS_LM`                 | LSQ              | `LeastSquaresFitResult`  |
+| `BUMPS_AMOEBA`             | LSQ              | `LeastSquaresFitResult`  |
+| `BUMPS_DE`                 | LSQ              | `LeastSquaresFitResult`  |
+| `BUMPS_DREAM`              | Bayesian         | `BayesianFitResult`      |
+| `EMCEE` _(when added)_     | Bayesian         | `BayesianFitResult`      |
+
+The pairing rule is encoded once on the minimizer base classes
+(`LeastSquaresMinimizerBase._fit_result_class = LeastSquaresFitResult`,
+`BayesianMinimizerBase._fit_result_class = BayesianFitResult`) so
+`_swap_minimizer` reads the paired class off the new minimizer instance
+and does not need a per-tag dispatch.
+
+This preserves the consolidation ADR's "no `_bayesian_*` mirror"
+guarantee — there is exactly one output category, not seven — while
+making the input/output boundary unambiguous.
+
+### 2. Field assignments
+
+**`analysis.minimizer` after the split** (writable settings only):
+
+- LSQ: `max_iterations`.
+- Bayesian: `sampling_steps`, `burn_in_steps`, `thinning_interval`,
+  `population_size`, `parallel_workers`, `initialization_method`,
+  `random_seed`.
+
+`credible_interval_inner` and `credible_interval_outer` **stay on the
+output side** in this ADR, attached to `BayesianFitResult` (see below).
+They are persisted with the fixed values `0.68` and `0.95`, matching the
+per-parameter interval columns (`posterior_interval_68_low/high`,
+`posterior_interval_95_low/high`). Promoting the levels to user-writable
+settings would let the user choose a 50% interval that then gets
+persisted under a column named `posterior_interval_68_low` — a
+data-integrity problem rather than a UX problem. A future suggestion ADR
+can promote them to settings and generalise the column naming (e.g.
+`posterior_interval_low_<level>`) in one combined change; doing one
+without the other is unsafe and out of scope here.
+
+**`analysis.fit_result` after the split** (outputs only). Common fields
+live on `FitResultBase`; family-specific fields on the concrete classes:
+
+- `FitResultBase`: `success`, `message`, `iterations`, `fitting_time`,
+  `reduced_chi_square`, `result_kind`.
+- `LeastSquaresFitResult` adds: `objective_name`, `objective_value`,
+  `n_data_points`, `n_parameters`, `n_free_parameters`,
+  `degrees_of_freedom`, `covariance_available`, `correlation_available`,
+  `exit_reason`.
+- `BayesianFitResult` adds: `point_estimate_name`, `sampler_completed`,
+  `credible_interval_inner`, `credible_interval_outer`,
+  `acceptance_rate_mean`, `gelman_rubin_max`,
+  `effective_sample_size_min`, `best_log_posterior`.
+
+The three overlapping pairs from §"Context" are resolved by **dropping
+the `minimizer` copy** and keeping the `fit_result` copy:
+
+- `minimizer.runtime_seconds` removed; `fit_result.fitting_time` is the
+  single source.
+- `minimizer.iterations_performed` removed; `fit_result.iterations` is
+  the single source.
+- `minimizer.objective_value` removed. `LeastSquaresFitResult` keeps
+  **two distinct fields**: `objective_value` (raw χ² returned by the
+  minimizer's objective function) and `reduced_chi_square` (= χ² /
+  `degrees_of_freedom`). They are not duplicates; the unreduced value is
+  what the solver actually optimises and is useful for diagnostics on
+  small-dof fits, while the reduced value is what every user-facing
+  table and plot displays. `BayesianFitResult` does not carry
+  `objective_value` because the Bayesian engine optimises the log
+  posterior rather than χ² directly.
+
+### 3. CIF layout follows the Python split
+
+The `_minimizer.*` block becomes settings-only. A new `_fit_result.*`
+block (already present today for the common header fields) absorbs every
+fit output. The set of `_fit_result.*` tags depends on the active
+`_minimizer.type`, matching the same shape-shifting convention that
+`_minimizer.*` itself already uses per
+[`switchable-category-owned-selectors.md`](switchable-category-owned-selectors.md).
+
+Example deterministic fit:
+
+```
+_minimizer.type             'lmfit (leastsq)'
+_minimizer.max_iterations    1000
+
+_fit_result.result_kind            deterministic
+_fit_result.success                true
+_fit_result.message                converged
+_fit_result.iterations             87
+_fit_result.fitting_time           12.34
+_fit_result.reduced_chi_square     1.42
+_fit_result.objective_name         chi_square
+_fit_result.objective_value        1532.4
+_fit_result.n_data_points          1024
+_fit_result.n_parameters           12
+_fit_result.n_free_parameters      8
+_fit_result.degrees_of_freedom     1016
+_fit_result.covariance_available   true
+_fit_result.correlation_available  true
+_fit_result.exit_reason            converged
+```
+
+Example Bayesian fit:
+
+```
+_minimizer.type                       'bumps (dream)'
+_minimizer.sampling_steps             3000
+_minimizer.burn_in_steps              600
+_minimizer.thinning_interval          1
+_minimizer.population_size            4
+_minimizer.parallel_workers           0
+_minimizer.initialization_method      latin_hypercube
+_minimizer.random_seed                ?
+
+_fit_result.result_kind                bayesian
+_fit_result.success                    true
+_fit_result.message                    'sampler converged'
+_fit_result.iterations                 3000
+_fit_result.fitting_time               124.7
+_fit_result.reduced_chi_square         1.18
+_fit_result.point_estimate_name        best_sample
+_fit_result.sampler_completed          true
+_fit_result.credible_interval_inner    0.68
+_fit_result.credible_interval_outer    0.95
+_fit_result.acceptance_rate_mean       0.27
+_fit_result.gelman_rubin_max           1.03
+_fit_result.effective_sample_size_min  482
+_fit_result.best_log_posterior        -1234.56
+```
+
+### 4. The runtime `analysis.fit_results` object is the same data shaped differently
+
+`analysis.fit_results` (plural, runtime) is the rich `FitResults` /
+`BayesianFitResults` Python object that holds posterior samples,
+predictive summaries, raw engine results, and reporting helpers. This
+ADR does **not** rename it. After the split it remains the
+"give-me-everything" accessor; `analysis.fit_result.*` (singular, CIF
+category) holds the persisted scalar projection of the same fit. The
+naming pair stays as today.
+
+A small UX win is added under the accepted display facade
+([`display-ux.md`](display-ux.md)): the existing
+`project.display.fit.results()` entry point gains a "Settings used"
+table above the existing results tables, populated from
+`analysis.minimizer.*`. No new `Analysis`-level display method is added;
+the user-facing surface stays exactly where the display ADR put it.
+Internally the helper reads `self.minimizer.*` and `self.fit_result.*`
+and renders one combined view.
+
+### 5. Help and discoverability
+
+`analysis.minimizer.help()` after the split lists ~7 properties for
+Bayesian and 1 for LSQ — every one writable. The "is this writable"
+question disappears.
+
+`analysis.fit_result.help()` lists 6 common output properties plus the
+family-specific ones, all clearly read-only.
+
+### 6. No new selector wiring
+
+`analysis.minimizer.type` remains the single user-facing selector. The
+swap hook updates **both** `analysis.minimizer` and
+`analysis.fit_result` instances atomically (via
+`Analysis._swap_minimizer`, which reads the paired `_fit_result_class`
+off the new minimizer base). The paired `fit_result` is not a
+user-facing switchable: there is no `fit_result.type` and no
+`fit_result.show_supported()`, per the documented exception added to
+[`switchable-category-owned-selectors.md`](switchable-category-owned-selectors.md)
+(see §"ADRs amended"). This keeps "one minimizer concept, one
+user-facing type" intact and removes the temptation to swap the result
+class independently of the minimizer.
+
+## Consequences
+
+### Positive
+
+- **Clear writable surface.** `analysis.minimizer.help()` shows only
+  settings. Inputs and outputs no longer mix in one namespace.
+- **Single source for every output field.** The two current real
+  duplications (`runtime_seconds`/`fitting_time` and
+  `iterations_performed`/`iterations`) collapse to one location each.
+  The `objective_value`/`reduced_chi_square` pair is **not** a
+  duplication and both stay (see §2 for the distinction).
+- **Family-specific outputs have a natural home.** Currently
+  `minimizer.gelman_rubin_max` lives on the Bayesian minimizer class;
+  after the split it lives on the paired `BayesianFitResult` class. The
+  two categories pair symmetrically and emcee inherits the pattern for
+  free.
+- **CIF stays compact.** No new CIF blocks beyond `_fit_result.*` which
+  is already present. The settings/outputs split is reflected in the CIF
+  tag prefix.
+- **The "are we done with a fit?" check becomes simple.**
+  `bool(analysis.fit_result.success.value)` answers it directly without
+  scanning a mixed input/output namespace.
+
+### Trade-offs
+
+- **Settings and matching outputs are two-place reads.** Mitigation:
+  `project.display.fit.results()` presents both. The current layout
+  already requires multi-place reads; this just makes the rule
+  consistent.
+- **`fit_result` becomes an internally-paired category.** The pairing
+  cost is small (one paired-instance assignment in the existing
+  `_swap_minimizer` hook) and is invisible to the user — there is no
+  second `fit_result.type` selector. The exception to the global
+  selector contract is documented explicitly in §"ADRs amended"
+  alongside the selector ADR.
+- **Saved CIF files from the post-consolidation layout cannot load
+  unchanged.** Beta posture (no legacy shims) applies. Tutorial fixtures
+  regenerate via `pixi run script-tests`. Tutorial `ed-24` already
+  carries a narrow archive normaliser; the new layout would extend it
+  once.
+- **Reopens a decision from a recently accepted ADR.** Documented
+  explicitly above in §"Status Note".
+
+### ADRs amended by this ADR
+
+- [`minimizer-category-consolidation.md`](minimizer-category-consolidation.md)
+  — §1 ("Unified `minimizer` category replaces all sampler-input and
+  fit-result categories") becomes a partial rule: the unified
+  `minimizer` holds inputs; outputs move to the paired `fit_result`.
+  §"Alternatives Considered → D" updated to record the reversal and the
+  implementation evidence that prompted it.
+- [`analysis-cif-fit-state.md`](analysis-cif-fit-state.md) — §"Minimizer
+  fit projection" rewritten to describe the split (`_minimizer.*`
+  settings-only, `_fit_result.*` outputs including family-specific
+  fields).
+- [`runtime-fit-results.md`](runtime-fit-results.md) — closing paragraph
+  references this ADR alongside the existing two.
+- [`switchable-category-owned-selectors.md`](switchable-category-owned-selectors.md)
+  — §1 ("The category owns its selector") gains a paragraph carving out
+  one documented exception: a category that is fully determined by
+  another category's `type` (today only `fit_result`, derived from
+  `minimizer.type`) is allowed to omit `category.type` and
+  `category.show_supported()`. The mechanism is described in §1 of this
+  ADR. The user-facing selector convention is otherwise unchanged.
+- [`display-ux.md`](display-ux.md) — §"Fit results display" expanded to
+  mention that `project.display.fit.results()` now prints a "Settings
+  used" block above the result tables, sourced from
+  `analysis.minimizer.*`. No new public entry point is added.
+
+## Deferred Work
+
+- **Renaming `analysis.fit_results` (plural runtime object).** The
+  plural/singular pair is mildly confusing but the rename has wide blast
+  radius (tests, tutorials, every BayesianFitResults reference). Track
+  separately if the confusion remains after the combined display lands.
+- **Paired internal categories beyond `minimizer` / `fit_result`.** This
+  ADR introduces the paired pattern for the minimizer only. If future
+  categories grow the same input/output asymmetry (e.g. extinction,
+  peak), apply the same pattern then; do not generalise pre-emptively.
+- **User-configurable credible-interval levels.** The two interval
+  levels currently stay at the hardcoded `0.68` / `0.95`, matching the
+  fixed per-parameter column names (`posterior_interval_68_low` etc.).
+  Promoting the levels to user settings requires generalising the column
+  naming at the same time to avoid the data-integrity hole where a
+  `0.50` level lands in a column called `posterior_interval_68_low`.
+  Both pieces belong in a follow-on ADR so this proposal stays focused
+  on the input/output split.
+- **CIF compatibility helper for ID 35 archive.** The
+  `_normalize_id35_archive_for_tutorial` helper in `ed-24.py` already
+  has a roadmap to deletion; the new CIF layout extends the rename map
+  one more line. No new architecture decision needed.
+
+## Alternatives Considered
+
+### A. Keep current mixed-category layout, fix only the duplications
+
+Drop `minimizer.runtime_seconds`, `.iterations_performed`,
+`.objective_value` and route every reader to `fit_result.*`. Rejected
+because it leaves the input/output mix on `minimizer` intact and
+therefore does not fix the `minimizer.help()` discoverability problem.
+
+### B. Mark fields with metadata, keep one category
+
+Add an `is_input: bool` marker to each descriptor and have
+`minimizer.help()` group inputs vs outputs in display. Rejected because
+it ships the structural problem unchanged — the CIF still mixes both
+under `_minimizer.*`, the duplications with `fit_result` remain, and the
+`_set_*` vs writable-setter split is still ad-hoc.
+
+### C. Move outputs into the runtime `fit_results` object, not a CIF category
+
+Persist only settings in CIF; outputs live in `analysis.fit_results` at
+runtime and `analysis/results.h5` on disk. Rejected because the small
+scalar outputs (success, χ², runtime, R̂) are exactly what users want to
+read from CIF without unpacking HDF5, and the consolidation ADR
+explicitly puts them in CIF (`_minimizer.*` today).
+
+### D. Rename `fit_result` to mirror minimizer (`minimizer_result`)
+
+Make the pairing rule explicit in the name (`<x>` and `<x>_result`).
+Rejected because the recently-accepted
+[`switchable-category-owned-selectors.md`](switchable-category-owned-selectors.md)
+ADR deliberately drops `_type` and other suffixes from category names;
+adding `_result` walks the convention back.
diff --git a/docs/dev/adrs/accepted/runtime-fit-results.md b/docs/dev/adrs/accepted/runtime-fit-results.md
index 282b9841d..c0cbf7b29 100644
--- a/docs/dev/adrs/accepted/runtime-fit-results.md
+++ b/docs/dev/adrs/accepted/runtime-fit-results.md
@@ -30,9 +30,11 @@ raw driver payloads remain runtime-only unless a narrower ADR defines a
 persisted projection. The accepted
 [`analysis-cif-fit-state.md`](analysis-cif-fit-state.md) and
 [`minimizer-category-consolidation.md`](minimizer-category-consolidation.md)
-ADRs define the current compact projection for fit headers,
-minimizer-owned outputs, parameter posterior summaries, and the
-`analysis/results.h5` sidecar.
+ADRs, as amended by
+[`minimizer-input-output-split.md`](minimizer-input-output-split.md),
+define the current compact projection for fit headers, paired fit-result
+outputs, parameter posterior summaries, and the `analysis/results.h5`
+sidecar.
 
 ## Consequences
 
diff --git a/docs/dev/adrs/accepted/switchable-category-owned-selectors.md b/docs/dev/adrs/accepted/switchable-category-owned-selectors.md
index 19fb491bb..f74fdf849 100644
--- a/docs/dev/adrs/accepted/switchable-category-owned-selectors.md
+++ b/docs/dev/adrs/accepted/switchable-category-owned-selectors.md
@@ -101,6 +101,14 @@ Owner-level shims are removed (no `<owner>.<cat>_type`, no
 `show_supported_<cat>_types()`, no `show_current_<cat>_type()`). The
 owner exposes only the category itself, e.g. `analysis.minimizer`.
 
+Exception: an internally paired category whose concrete class is fully
+determined by another category's `type` may omit its own public
+selector. Today this applies only to `analysis.fit_result`, whose class
+is derived from `analysis.minimizer.type` by the
+[`minimizer-input-output-split.md`](minimizer-input-output-split.md)
+ADR. It has no `fit_result.type`, no `fit_result.show_supported()`, and
+no `_fit_result.type` CIF tag.
+
 The owner still owns the swap mechanism (it holds the slot) but the swap
 is _initiated_ from the category through a back-reference.
 
diff --git a/docs/dev/adrs/index.md b/docs/dev/adrs/index.md
index 28859e145..fcdf7bbfa 100644
--- a/docs/dev/adrs/index.md
+++ b/docs/dev/adrs/index.md
@@ -21,6 +21,7 @@ folders.
 | Analysis and fitting | Accepted   | Parameter Correlation Persistence         | Persists deterministic and posterior correlation summaries in `_fit_parameter_correlation`                         | [`parameter-correlation-persistence.md`](accepted/parameter-correlation-persistence.md)      |
 | Analysis and fitting | Suggestion | Fit Output Files and Data Exports         | Narrows remaining archive/export questions after adopting `results.csv` and `results.h5`.                          | [`fit-output-files-and-data-exports.md`](suggestions/fit-output-files-and-data-exports.md)   |
 | Analysis and fitting | Accepted   | Minimizer Category Consolidation          | Collapses the seven Bayesian categories into one owner-level switchable `minimizer` category with HDF5 sidecar.    | [`minimizer-category-consolidation.md`](accepted/minimizer-category-consolidation.md)        |
+| Analysis and fitting | Accepted   | Minimizer Input/Output Split              | Keeps `analysis.minimizer` input-only and moves scalar fit outputs to paired `analysis.fit_result` classes.        | [`minimizer-input-output-split.md`](accepted/minimizer-input-output-split.md)                |
 | Analysis and fitting | Superseded | Parameter-Level Posterior Projection      | Superseded by minimizer-category consolidation; kept as historical context for `parameter.posterior`.              | [`parameter-posterior-summary.md`](suggestions/parameter-posterior-summary.md)               |
 | Analysis and fitting | Suggestion | Undo Fit                                  | Builds rollback semantics and CLI behavior on already-persisted pre-fit scalar snapshots.                          | [`undo-fit.md`](suggestions/undo-fit.md)                                                     |
 | Core model           | Accepted   | Category Owners and Real Datablocks       | Introduces `CategoryOwner` so singleton sections do not pretend to be real CIF datablocks.                         | [`category-owner-sections.md`](accepted/category-owner-sections.md)                          |
diff --git a/docs/dev/adrs/suggestions/iucr-cif-tag-alignment.md b/docs/dev/adrs/suggestions/iucr-cif-tag-alignment.md
new file mode 100644
index 000000000..052412cbd
--- /dev/null
+++ b/docs/dev/adrs/suggestions/iucr-cif-tag-alignment.md
@@ -0,0 +1,184 @@
+# ADR: IUCr CIF Tag Alignment for Fit Outputs
+
+**Status:** Proposed **Date:** 2026-05-24
+
+## Status Note
+
+This suggestion captures research done during the
+[`minimizer-input-output-split`](accepted/minimizer-input-output-split.md)
+work. That ADR's `_fit_result.*` CIF prefix is functional but diverges
+from the IUCr core and powder dictionaries. This proposal records the
+divergence and a plausible alignment path so it is not lost; the work is
+**not** in scope for the input/output-split PR.
+
+## Context
+
+After the input/output split, fit outputs persist under `_fit_result.*`.
+The IUCr maintains two relevant dictionaries that already cover much of
+the same ground:
+
+- [`COMCIFS/cif_core`](https://github.com/COMCIFS/cif_core) — core CIF
+  dictionary, refinement parameters under `_refine_ls.*` (51 items) and
+  aggregate reflection statistics under `_reflns.*`.
+- [`COMCIFS/Powder_Dictionary`](https://github.com/COMCIFS/Powder_Dictionary)
+  (`cif_pow.dic`) — powder-specific refinement under `_pd_proc_ls.*` (9
+  items).
+
+Cross-reference today:
+
+| Concept                 | Our `_fit_result.*`  | IUCr core (`_refine_ls.*`)                       | IUCr powder (`_pd_proc_ls.*`)                    |
+| ----------------------- | -------------------- | ------------------------------------------------ | ------------------------------------------------ |
+| Reduced χ² / GoF        | `reduced_chi_square` | `goodness_of_fit_all` (S, plus `_su`)            | derived from `prof_wR_factor`/`prof_wR_expected` |
+| R-factor unweighted     | —                    | `r_factor_all`, `r_factor_gt`                    | `prof_R_factor`                                  |
+| R-factor weighted       | —                    | `wr_factor_all`, `wr_factor_gt`, `wr_factor_ref` | `prof_wR_factor`                                 |
+| R-expected              | —                    | (derived from S and counts)                      | `prof_wR_expected`                               |
+| Number of data points   | `n_data_points`      | `number_reflns`, `number_reflns_gt`              | derive from `_pd_proc.number_of_points`          |
+| Number of parameters    | `n_parameters`       | `number_parameters`                              | (in `_refine_ls.*`)                              |
+| Number of restraints    | —                    | `number_restraints`                              | same                                             |
+| Number of constraints   | —                    | `number_constraints`                             | same                                             |
+| Shift / σ               | —                    | `shift_over_su_max`, `shift_over_su_mean`        | same                                             |
+| Profile function        | —                    | —                                                | `profile_function`                               |
+| Background function     | —                    | —                                                | `background_function`                            |
+| Wall time               | `fitting_time`       | (none)                                           | (none)                                           |
+| Iteration count         | `iterations`         | (none)                                           | (none)                                           |
+| Success flag, message   | `success`, `message` | (none)                                           | (none)                                           |
+| Bayesian R̂, ESS, accept | various              | (none)                                           | (none)                                           |
+
+Two consequences:
+
+1. **Real gaps.** Our serialization omits R-factors,
+   restraint/constraint counts, shift/σ diagnostics, and powder
+   profile/background function names. These are fields that
+   crystallographers expect in a saved CIF.
+2. **Naming divergence.** Where IUCr does have a tag, we use a different
+   prefix (`_fit_result.*` vs `_refine_ls.*` / `_pd_proc_ls.*`). Our
+   CIFs are valid but cannot be consumed by external tools that expect
+   IUCr-standard names.
+
+## Decision
+
+### 1. Use IUCr tag names where they exist
+
+For every `_fit_result.*` field that has a one-to-one IUCr counterpart,
+emit the IUCr tag instead. The Python attribute name stays
+(`analysis.fit_result.reduced_chi_square`); only the `cif_handler`
+`names` tuple changes. Examples:
+
+- `fit_result.reduced_chi_square` → emits
+  `_refine_ls.goodness_of_fit_all` for single-crystal,
+  `_pd_proc_ls.prof_wR_factor` + `_pd_proc_ls.prof_wR_expected` for
+  powder (or both, with the GoF derived on the powder side).
+- `fit_result.n_data_points` → `_refine_ls.number_reflns`
+  (single-crystal), `_pd_proc.number_of_points` (powder).
+- `fit_result.n_parameters` → `_refine_ls.number_parameters`.
+
+The emitted prefix becomes shape-shifting based on
+`experiment.type.scattering_type` and `experiment.type.sample_form` —
+the same convention powder packages use today. The Python API remains
+uniform.
+
+### 2. Add the missing IUCr fields to `fit_result`
+
+Promote the following from "gap" to "available" on the existing
+`LeastSquaresFitResult`:
+
+- `r_factor_all`, `wr_factor_all` — emitted as the standard tags;
+  computed by the LSQ projection writer from residuals.
+- `prof_r_factor`, `prof_wr_factor`, `prof_wr_expected` —
+  powder-specific variants, computed the same way against the profile
+  data.
+- `number_restraints`, `number_constraints` — current count from the
+  analysis model.
+- `shift_over_su_max`, `shift_over_su_mean` — last-iteration convergence
+  diagnostic.
+- `profile_function`, `background_function` (powder) — string-form
+  descriptions of the active peak and background categories.
+
+### 3. Keep our own prefix for fields IUCr does not cover
+
+`fitting_time`, `iterations`, `success`, `message`, every Bayesian
+diagnostic (`gelman_rubin_max`, `acceptance_rate_mean`, etc.), and the
+`result_kind`/`point_estimate_name` markers have no IUCr home today.
+They stay under a project-specific prefix. Two options for that prefix:
+
+- Keep `_fit_result.*` for the non-IUCr fields only. The CIF then mixes
+  prefixes (`_refine_ls.*` + `_fit_result.*`) which is unusual but
+  legal.
+- Use a clearly-namespaced extension like `_easydiffraction.*` or
+  `_eddict_fit.*` so external tools recognise the fields as non-IUCr.
+
+Decide during implementation; the second option is friendlier to
+external CIF readers.
+
+### 4. Bayesian Rietveld output has no IUCr precedent today
+
+There is no IUCr convention for sampler convergence (R̂, ESS, acceptance)
+or posterior diagnostics. This proposal does not invent one. The
+Bayesian-specific fields stay under the project prefix chosen in §3. If
+a community standard emerges, a follow-on ADR can absorb it.
+
+## Consequences
+
+### Positive
+
+- Saved CIFs become consumable by standard IUCr tools (publCIF,
+  checkCIF, journal submission pipelines).
+- The "what is the R-factor of this fit?" question has an answer in the
+  saved file — currently we only record reduced χ².
+- Powder users get the conventional Rp / Rwp / Rexp triplet.
+- We pick up restraint/constraint accounting that the structure side of
+  the project already tracks but does not currently emit.
+
+### Trade-offs
+
+- Shape-shifting CIF prefix per experiment family is more work to
+  implement than a single `_fit_result.*` prefix. Roughly: one
+  `cif_handler` per descriptor that maps to a different IUCr tag
+  depending on context; the Python API stays uniform.
+- Bayesian fields remain non-standard. There is no way around that until
+  IUCr defines tags for Bayesian Rietveld.
+- Existing saved projects from the post-split layout cannot load
+  unchanged. Beta posture applies; one more legacy-rename pass in
+  tutorial `ed-24` covers it.
+
+### ADRs amended by this ADR
+
+- [`analysis-cif-fit-state.md`](../accepted/analysis-cif-fit-state.md) —
+  replace the `_fit_result.*` projection description with the
+  IUCr-aligned tag set; document the per-experiment-family
+  shape-shifting.
+- [`minimizer-input-output-split.md`](../accepted/minimizer-input-output-split.md)
+  — the `_fit_result.*` examples in §3 are updated to use IUCr tags for
+  the covered fields; the non-IUCr fields keep the project-prefix
+  examples.
+
+## Deferred Work
+
+- The exact prefix for non-IUCr fields (§3 option choice).
+- IUCr-Bayesian alignment if a community standard appears.
+- Single-crystal `r_factor_gt` / `wr_factor_gt` (greater-than-σ subsets)
+  need a "threshold expression" decision. The
+  `_reflns.threshold_expression` field already covers it on the
+  reflection side; the LSQ projection writer needs to know the threshold
+  to compute the `_gt` variants.
+- Whether to also emit `_refine.special_details` for human-readable fit
+  notes.
+
+## Alternatives Considered
+
+### A. Keep `_fit_result.*` as-is
+
+Simplest. Saved CIFs stay self-contained but are not IUCr-portable.
+Defensible if the project never targets external CIF interop.
+
+### B. Emit both prefixes for the covered fields
+
+`_fit_result.reduced_chi_square` and `_refine_ls.goodness_of_fit_all`
+both present, holding the same value. Belt-and-braces, doubles the
+surface area of every saved file, and the two values can drift.
+
+### C. Adopt IUCr tags only for tags we already need
+
+Add the R-factor fields (§2) under our own `_fit_result.*` prefix.
+Smaller diff, fixes the missing-field gap but not the naming divergence.
+Pick if IUCr interop is genuinely not a goal.
diff --git a/docs/dev/issues/open.md b/docs/dev/issues/open.md
index 46e85a912..1e0bfe340 100644
--- a/docs/dev/issues/open.md
+++ b/docs/dev/issues/open.md
@@ -1782,6 +1782,49 @@ optional-diagnostics components.
 
 ---
 
+## 105. 🟢 Remove Orphaned Fit-Result Reset Helper
+
+**Type:** Cleanup **Source:** `minimizer-input-output-split` review 6.
+
+`Analysis._clear_fit_result_projection` is a private method with no
+callers after `_clear_persisted_fit_state` switched to replacing
+`self._fit_result` with a fresh paired result instance.
+
+**TODOs:**
+
+- [analysis.py](src/easydiffraction/analysis/analysis.py#L1217)
+
+**Fix:** delete the unused helper, or reintroduce a caller only if a
+future fit-result reset path genuinely needs to preserve the active
+instance.
+
+**Depends on:** nothing.
+
+---
+
+## 106. 🟢 Document `FitResultBase.result_kind` Default Rationale
+
+**Type:** Code readability **Source:** `minimizer-input-output-split`
+review 6.
+
+Most `FitResultBase` descriptors use `default=None, allow_none=True` so
+pre-fit CIF output serializes unknown values as `?`. `result_kind`
+intentionally keeps a valid enum default because it drives deterministic
+versus Bayesian projection handling, but that exception is not
+documented in code.
+
+**TODOs:**
+
+- [base.py](src/easydiffraction/analysis/categories/fit_result/base.py#L44)
+
+**Fix:** add a short code comment near the `result_kind` descriptor
+explaining why it keeps a concrete default while unknown result values
+use `None`.
+
+**Depends on:** nothing.
+
+---
+
 ## Summary
 
 | #   | Issue                                             | Severity | Type                         |
@@ -1869,3 +1912,5 @@ optional-diagnostics components.
 | 91  | Disable TODO checks in CodeFactor PRs             | 🟢 Low   | CI / Tooling                 |
 | 92  | Make `save()` respect verbosity                   | 🟢 Low   | UX                           |
 | 93  | Eliminate flicker in live progress tables         | 🟡 Med   | UX                           |
+| 105 | Remove orphaned fit-result reset helper           | 🟢 Low   | Cleanup                      |
+| 106 | Document `FitResultBase.result_kind` default      | 🟢 Low   | Code readability             |
diff --git a/docs/dev/package-structure/full.md b/docs/dev/package-structure/full.md
index a29064bdb..1f0663352 100644
--- a/docs/dev/package-structure/full.md
+++ b/docs/dev/package-structure/full.md
@@ -47,10 +47,15 @@
 │   │   │       └── 🏷️ class FitParametersFactory
 │   │   ├── 📁 fit_result
 │   │   │   ├── 📄 __init__.py
+│   │   │   ├── 📄 base.py
+│   │   │   │   └── 🏷️ class FitResultBase
+│   │   │   ├── 📄 bayesian.py
+│   │   │   │   └── 🏷️ class BayesianFitResult
 │   │   │   ├── 📄 default.py
-│   │   │   │   └── 🏷️ class FitResult
-│   │   │   └── 📄 factory.py
-│   │   │       └── 🏷️ class FitResultFactory
+│   │   │   ├── 📄 factory.py
+│   │   │   │   └── 🏷️ class FitResultFactory
+│   │   │   └── 📄 lsq.py
+│   │   │       └── 🏷️ class LeastSquaresFitResult
 │   │   ├── 📁 fitting_mode
 │   │   │   ├── 📄 __init__.py
 │   │   │   ├── 📄 default.py
diff --git a/docs/dev/package-structure/short.md b/docs/dev/package-structure/short.md
index dbe4e8f6d..e0a529800 100644
--- a/docs/dev/package-structure/short.md
+++ b/docs/dev/package-structure/short.md
@@ -29,8 +29,11 @@
 │   │   │   └── 📄 factory.py
 │   │   ├── 📁 fit_result
 │   │   │   ├── 📄 __init__.py
+│   │   │   ├── 📄 base.py
+│   │   │   ├── 📄 bayesian.py
 │   │   │   ├── 📄 default.py
-│   │   │   └── 📄 factory.py
+│   │   │   ├── 📄 factory.py
+│   │   │   └── 📄 lsq.py
 │   │   ├── 📁 fitting_mode
 │   │   │   ├── 📄 __init__.py
 │   │   │   ├── 📄 default.py
diff --git a/docs/dev/plans/minimizer-input-output-split.md b/docs/dev/plans/minimizer-input-output-split.md
new file mode 100644
index 000000000..0d9361a8a
--- /dev/null
+++ b/docs/dev/plans/minimizer-input-output-split.md
@@ -0,0 +1,674 @@
+# Plan: Minimizer Input/Output Split
+
+> This plan follows
+> [`.github/copilot-instructions.md`](../../../.github/copilot-instructions.md).
+> No deliberate exceptions.
+
+## ADR
+
+Implements
+[`docs/dev/adrs/suggestions/minimizer-input-output-split.md`](../adrs/suggestions/minimizer-input-output-split.md).
+This plan promotes that ADR from Suggestion → Accepted during
+implementation (step P1.16).
+
+Affected ADRs that this plan amends (per the ADR's §"ADRs amended"):
+
+- [`accepted/minimizer-category-consolidation.md`](../adrs/accepted/minimizer-category-consolidation.md)
+  — §1 becomes a partial rule; §"Alternatives Considered → D" records
+  the reversal.
+- [`accepted/analysis-cif-fit-state.md`](../adrs/accepted/analysis-cif-fit-state.md)
+  — §"Minimizer fit projection" rewritten for the settings-only
+  `_minimizer.*` / outputs-on-`_fit_result.*` shape.
+- [`accepted/runtime-fit-results.md`](../adrs/accepted/runtime-fit-results.md)
+  — closing paragraph references this ADR alongside the existing two.
+- [`accepted/switchable-category-owned-selectors.md`](../adrs/accepted/switchable-category-owned-selectors.md)
+  — §1 gains the documented "fully-determined paired category"
+  exception.
+- [`accepted/display-ux.md`](../adrs/accepted/display-ux.md) —
+  `project.display.fit.results()` prints a "Settings used" block.
+
+## Branch and PR
+
+- Branch: `minimizer-input-output-split` (continued from the branch the
+  ADR was drafted on). Do not push unless asked.
+- Each step in §"Implementation steps (Phase 1)" must be staged with
+  explicit paths and committed locally **before** moving to the next
+  step. See `.github/copilot-instructions.md` → **Commits**.
+- After P1.17, stop and wait for the user review gate before starting
+  Phase 2.
+
+## Decisions already made (from the ADR)
+
+1. `analysis.minimizer` holds **writable user settings only**.
+2. `analysis.fit_result` becomes a class hierarchy paired with
+   `analysis.minimizer`. `FitResultBase` carries common fields;
+   `LeastSquaresFitResult` and `BayesianFitResult` add family-specific
+   ones.
+3. `fit_result` is **not a user-facing switchable category**. No
+   `fit_result.type`, no `fit_result.show_supported()`. The owner's
+   `_swap_minimizer` hook installs both the minimizer and the paired
+   `fit_result` atomically.
+4. Pairing rule is encoded on the minimizer base classes:
+   `LeastSquaresMinimizerBase._fit_result_class = LeastSquaresFitResult`,
+   `BayesianMinimizerBase._fit_result_class = BayesianFitResult`.
+5. `objective_value` (raw χ²) and `reduced_chi_square` are distinct
+   fields, both kept on `LeastSquaresFitResult`.
+6. `credible_interval_inner` / `credible_interval_outer` stay on the
+   output side (`BayesianFitResult`) at the fixed `0.68` / `0.95`
+   values. User-configurable levels deferred to a follow-on ADR.
+7. The display extension lives under `project.display.fit.results()`; no
+   new `Analysis`-level display method is added.
+8. Beta posture: hard cutover, no shims, no deprecation warnings.
+   Tutorials and saved fixtures regenerate.
+
+## Open questions
+
+- **Existing `analysis.fit_result.from_cif` parameter ordering.** After
+  this split, the CIF restore for `_fit_result.*` must run **after**
+  `_minimizer.*` is read so the paired class is known before the result
+  descriptors load. The current `_restore_*` order in
+  [`serialize.py`](../../../src/easydiffraction/io/cif/serialize.py)
+  reads `_minimizer.*` first via `_swap_minimizer`, then iterates the
+  rest. P1.6 must ensure `_fit_result` is included in the iteration only
+  after the swap has installed the paired class. Confirm during P1.6
+  implementation.
+- **Posterior-summary code path that currently writes
+  `_set_credible_interval_*` on `minimizer`.** After P1.11, the setters
+  move to `BayesianFitResult`. The `_store_posterior_fit_projection`
+  method in
+  [`analysis.py`](../../../src/easydiffraction/analysis/analysis.py)
+  must be updated to call
+  `self._fit_result._set_credible_interval_inner(...)` instead of
+  `self.minimizer._set_*`. Verify the order of operations against the
+  test in
+  [`test_results_sidecar.py`](../../../tests/unit/easydiffraction/io/test_results_sidecar.py).
+
+## Concrete files likely to change
+
+### Created
+
+- `src/easydiffraction/analysis/categories/fit_result/base.py` — rename
+  existing `FitResult` class to `FitResultBase` (or extract a base).
+  Common output descriptors live here.
+- `src/easydiffraction/analysis/categories/fit_result/lsq.py` —
+  `LeastSquaresFitResult` with LSQ-specific output descriptors.
+- `src/easydiffraction/analysis/categories/fit_result/bayesian.py` —
+  `BayesianFitResult` with Bayesian-specific output descriptors,
+  including `credible_interval_inner` / `credible_interval_outer`.
+- _(`src/easydiffraction/analysis/categories/fit_result/factory.py`
+  already exists; this plan extends it rather than creating it. See P1.4
+  — the factory becomes a registration helper for the two new family
+  classes; the authoritative swap mechanism is the `_fit_result_class`
+  attribute on the paired minimizer base, not a factory lookup. The
+  factory is still useful for introspection / testing.)_
+- `tests/unit/easydiffraction/analysis/categories/fit_result/test_base.py`
+- `tests/unit/easydiffraction/analysis/categories/fit_result/test_lsq.py`
+- `tests/unit/easydiffraction/analysis/categories/fit_result/test_bayesian.py`
+- `tests/unit/easydiffraction/analysis/categories/fit_result/test_factory.py`
+
+### Modified
+
+- `src/easydiffraction/analysis/categories/fit_result/__init__.py` — add
+  explicit imports for every new concrete class to trigger factory
+  registration.
+- `src/easydiffraction/analysis/categories/fit_result/default.py` —
+  rewritten to import from the new family modules; the existing
+  `FitResult` is renamed to `FitResultBase` and absorbed.
+- `src/easydiffraction/analysis/categories/minimizer/base.py` — add
+  `_fit_result_class: ClassVar[type]` declaration.
+- `src/easydiffraction/analysis/categories/minimizer/lsq_base.py` — set
+  `_fit_result_class = LeastSquaresFitResult`; **remove**
+  `objective_name`, `objective_value`, `n_data_points`, `n_parameters`,
+  `n_free_parameters`, `degrees_of_freedom`, `covariance_available`,
+  `correlation_available`, `runtime_seconds`, `iterations_performed`,
+  `exit_reason` from descriptor declarations and from
+  `_result_descriptor_names`. `_setting_descriptor_names` stays
+  `('max_iterations',)`.
+- `src/easydiffraction/analysis/categories/minimizer/bayesian_base.py` —
+  set `_fit_result_class = BayesianFitResult`; remove `runtime_seconds`,
+  `point_estimate_name`, `sampler_completed`, `credible_interval_inner`,
+  `credible_interval_outer`, `acceptance_rate_mean`, `gelman_rubin_max`,
+  `effective_sample_size_min`, `best_log_posterior` from descriptor
+  declarations and from `_result_descriptor_names`.
+  `_setting_descriptor_names` keeps the seven Bayesian inputs.
+- `src/easydiffraction/analysis/analysis.py` — extend `_swap_minimizer`
+  to also instantiate the paired `fit_result` via the minimizer's
+  `_fit_result_class`; add `analysis.fit_result` property; route every
+  `self._minimizer._set_*` result-writer call in
+  `_store_least_squares_result_projection` /
+  `_store_posterior_fit_projection` /
+  `_restore_fit_results_from_projection` to `self._fit_result._set_*`
+  instead.
+- `src/easydiffraction/io/cif/serialize.py` — emit/read `_fit_result.*`
+  from the paired class; remove the removed `_minimizer.*` output tags
+  from the serialise/deserialise paths. Update the legacy-tag rejection
+  message.
+- `src/easydiffraction/project/display.py` — extend
+  `project.display.fit.results()` to print a "Settings used" block
+  populated from `analysis.minimizer.*` above the existing result
+  tables.
+- All tutorials referencing `analysis.minimizer.<output_field>` (e.g.
+  `runtime_seconds`, `gelman_rubin_max`, `objective_value`) →
+  `analysis.fit_result.<output_field>`. List enumerated at P1.15 start
+  via `git grep`.
+- Tests reading `analysis.minimizer.<output_field>` → migrate to
+  `analysis.fit_result.<output_field>`. P2.1 enumerates.
+
+### Deleted
+
+- None. The existing `fit_result/default.py` is rewritten in place; the
+  existing `FitResult` class becomes `FitResultBase`.
+
+## Implementation steps (Phase 1)
+
+Mark `[x]` as each step lands.
+
+- [x] **P1.1 — Rename `FitResult` to `FitResultBase`; add reset hooks;
+      update every import site.** In
+      `src/easydiffraction/analysis/categories/fit_result/default.py`,
+      rename the class. The factory `@register` decorator stays on the
+      renamed class so the default-tag lookup keeps working until P1.4
+      extends the factory.
+
+      Add two class-level hooks to `FitResultBase` matching the
+      `MinimizerCategoryBase` shape introduced by the consolidation
+      work
+      ([`minimizer/base.py:69-74`](../../../src/easydiffraction/analysis/categories/minimizer/base.py)):
+
+      ```python
+      _result_descriptor_names: ClassVar[tuple[str, ...]] = (
+          'success', 'message', 'iterations',
+          'fitting_time', 'reduced_chi_square', 'result_kind',
+      )
+
+      def _reset_result_descriptors(self) -> None:
+          """Reset fit-result descriptors to declared defaults."""
+          for name in self._result_descriptor_names:
+              descriptor = getattr(self, name)
+              if isinstance(descriptor, GenericDescriptorBase):
+                  descriptor.value = descriptor._value_spec.default_value()
+      ```
+
+      `LeastSquaresFitResult` (P1.2) and `BayesianFitResult` (P1.3)
+      then add their own field names to `_result_descriptor_names`
+      so the inherited helper resets every relevant descriptor.
+
+      This must land in P1.1 because P1.6 retargets
+      `_clear_minimizer_result_projection` (renamed
+      `_clear_fit_result_projection`) to call
+      `self.fit_result._reset_result_descriptors()`, and that method
+      must exist on `FitResultBase` before the swap is wired.
+
+      Update every package-level import that referenced the old
+      name. `git grep -nP '\bFitResult\b' src/ tests/` lists the
+      sites at plan time:
+
+      - `src/easydiffraction/analysis/__init__.py` (line 14 today)
+      - `src/easydiffraction/analysis/categories/__init__.py`
+        (line 14 today)
+      - `src/easydiffraction/analysis/categories/fit_result/__init__.py`
+      - `src/easydiffraction/analysis/analysis.py` (import line 18;
+        type annotation on the `fit_result` property at line 432;
+        `self._fit_result = FitResult()` at line 483; same
+        construction at line 1208)
+
+      All four `FitResult` import/annotation/construction sites in
+      `analysis.py` become `FitResultBase` after this step. The two
+      `self._fit_result = FitResult()` construction sites (init and
+      `_clear_persisted_fit_state`) become
+      `FitResultBase()` temporarily; P1.6 retargets them to the
+      paired class.
+
+      Re-run `git grep -nP '\bFitResult\b' src/` at the end of this
+      step — every remaining hit must be the renamed class name or a
+      module path, not the old bare class. Tests are migrated by
+      P2.1.
+
+      Commit: `Rename FitResult to FitResultBase, add reset hooks`
+
+- [x] **P1.2 — Add `LeastSquaresFitResult` class.** New file
+      `src/easydiffraction/analysis/categories/fit_result/lsq.py`.
+      `LeastSquaresFitResult(FitResultBase)` declares: `objective_name`,
+      `objective_value`, `n_data_points`, `n_parameters`,
+      `n_free_parameters`, `degrees_of_freedom`, `covariance_available`,
+      `correlation_available`, `exit_reason`. **All defaults are `None`
+      with `allow_none=True`**, matching the consolidation cleanup that
+      previously moved LSQ outputs off `0` / `false` / `''` so a pre-fit
+      CIF emits `?` rather than a value that looks like a degenerate
+      result. This applies to numeric, integer-like, string, and bool
+      fields alike; the descriptor helpers in
+      `LeastSquaresMinimizerBase`
+      ([`lsq_base.py`](../../../src/easydiffraction/analysis/categories/minimizer/lsq_base.py))
+      that currently produce these descriptors are the model — they can
+      be lifted into `LeastSquaresFitResult` verbatim before being
+      removed from `lsq_base.py` at P1.9. Declare
+      `_expected_descriptor_names`, `_result_descriptor_names` for
+      parity with the minimizer hierarchy. Tests deferred to Phase 2.
+      Commit: `Add LeastSquaresFitResult class`
+
+- [x] **P1.3 — Add `BayesianFitResult` class.** New file
+      `src/easydiffraction/analysis/categories/fit_result/bayesian.py`.
+      `BayesianFitResult(FitResultBase)` declares:
+      `point_estimate_name`, `sampler_completed`,
+      `credible_interval_inner` (default `0.68`),
+      `credible_interval_outer` (default `0.95`),
+      `acceptance_rate_mean`, `gelman_rubin_max`,
+      `effective_sample_size_min`, `best_log_posterior`. Declare
+      `_expected_descriptor_names`, `_result_descriptor_names`. Tests
+      deferred to Phase 2. Commit: `Add BayesianFitResult class`
+
+- [x] **P1.4 — Register fit-result classes with the existing
+      `FitResultFactory`.** The factory already exists at
+      [`src/easydiffraction/analysis/categories/fit_result/factory.py`](../../../src/easydiffraction/analysis/categories/fit_result/factory.py)
+      and currently registers only the default common class. Update it
+      to also register `LeastSquaresFitResult` and `BayesianFitResult`
+      with their family tags. Update
+      `src/easydiffraction/analysis/categories/fit_result/__init__.py`
+      to explicitly import every concrete class (so registration fires
+      on package import, per the repo's standard pattern).
+
+      **Authoritative mechanism:** `Analysis._swap_minimizer`
+      constructs the paired fit-result via the minimizer's
+      `_fit_result_class` attribute (P1.5), not via a factory
+      lookup. The factory is kept as a registration helper for
+      introspection and testing; do not add a public selector surface
+      (`type`, `show_supported`) since `fit_result` is internally
+      paired, per ADR §1. Commit:
+      `Register fit-result family classes with factory`
+
+- [x] **P1.5 — Declare `_fit_result_class` on minimizer bases.** In
+      `src/easydiffraction/analysis/categories/minimizer/lsq_base.py`,
+      add `_fit_result_class: ClassVar[type] = LeastSquaresFitResult`.
+      In
+      `src/easydiffraction/analysis/categories/minimizer/bayesian_base.py`,
+      add `_fit_result_class: ClassVar[type] = BayesianFitResult`. Add
+      the matching declaration to
+      `src/easydiffraction/analysis/categories/minimizer/base.py` with
+      `_fit_result_class: ClassVar[type] = FitResultBase` as a safety
+      fallback (no concrete minimizer instantiates the bare base, but
+      `_swap_minimizer` reads through this attribute). Commit:
+      `Declare paired _fit_result_class on minimizer bases`
+
+- [x] **P1.6 — Wire `Analysis._swap_minimizer` to install both
+      instances, and update every `_fit_result` reset path.** In
+      `src/easydiffraction/analysis/analysis.py`:
+  - `__init__` constructs the initial `_fit_result` from the default
+    minimizer's `_fit_result_class`:
+    `self._fit_result = self._minimizer._fit_result_class()`. The line
+    483 `self._fit_result = FitResultBase()` (after P1.1) is replaced.
+  - `_replace_minimizer` constructs
+    `self._fit_result = new_minimizer._fit_result_class()` after the new
+    minimizer is created. The old `fit_result` is detached
+    (`_parent = None`) before being replaced.
+  - `_clear_persisted_fit_state` (line 1204 today) currently calls
+    `self._clear_minimizer_result_projection()` and then
+    `self._fit_result = FitResult()`. After P1.1 + the split, both lines
+    must change:
+    - `self._fit_result = self.minimizer._fit_result_class()` replaces
+      the bare `FitResultBase()` construction. This keeps the paired
+      class invariant whenever the persisted state is reset.
+    - `self._clear_minimizer_result_projection()` currently calls
+      `self.minimizer._reset_result_descriptors()`. After P1.9/P1.10
+      remove the result descriptors from the minimizer, this method
+      becomes a no-op. **Retarget it to
+      `self.fit_result._reset_result_descriptors()`** and rename it to
+      `_clear_fit_result_projection`. Update the call sites (line 1204;
+      potentially others — `git grep` confirms).
+  - Add `analysis.fit_result` read-only property
+    (`return self._fit_result`). Type annotation: `FitResultBase` (the
+    family classes inherit from it).
+  - Wire `self._fit_result._parent = self` in
+    `_attach_category_parents`. Every `_fit_result` reassignment in the
+    methods above must also set `_parent` on the new instance.
+
+  Verification at the end of this step:
+
+  ```
+  git grep -nE 'self\._fit_result\s*=' src/easydiffraction/analysis/analysis.py
+  ```
+
+  Every match must construct via `self.minimizer._fit_result_class()`
+  (or `new_minimizer._fit_result_class()` in `_replace_minimizer`), not
+  a bare class name. There must be no remaining
+  `self._fit_result = FitResultBase()` after this step.
+
+  Commit: `Wire fit_result swap and reset paths to paired class`
+
+- [x] **P1.7 — Route LSQ result writers to `fit_result`.** In
+      `src/easydiffraction/analysis/analysis.py`,
+      `_store_least_squares_result_projection` currently writes to
+      `self.minimizer._set_objective_name(...)` etc. Reroute every such
+      call to `self.fit_result._set_*`. Same for
+      `_restore_fit_results_from_projection`'s LSQ branch (it reads
+      `self.minimizer.objective_name.value` etc. — change to
+      `self.fit_result.<name>.value`). Commit:
+      `Route LSQ result writers to fit_result`
+
+- [x] **P1.8 — Route Bayesian result writers to `fit_result`.** Same
+      treatment for `_store_posterior_fit_projection` and the Bayesian
+      branch of `_restore_fit_results_from_projection`. Includes the
+      `_set_credible_interval_*` calls — they now target
+      `self.fit_result._set_credible_interval_*`. Commit:
+      `Route Bayesian result writers to fit_result`
+
+- [x] **P1.9 — Remove output fields from LSQ minimizer base.** In
+      `src/easydiffraction/analysis/categories/minimizer/lsq_base.py`,
+      delete the descriptor declarations and properties for
+      `objective_name`, `objective_value`, `n_data_points`,
+      `n_parameters`, `n_free_parameters`, `degrees_of_freedom`,
+      `covariance_available`, `correlation_available`,
+      `runtime_seconds`, `iterations_performed`, `exit_reason`. Remove
+      these names from `_expected_descriptor_names` and
+      `_result_descriptor_names`. `_setting_descriptor_names` stays
+      `('max_iterations',)`. After this step, `_result_descriptor_names`
+      on `LeastSquaresMinimizerBase` is `()` and
+      `_reset_result_descriptors()` is a no-op on every LSQ minimizer —
+      confirming the P1.6 retarget of
+      `_clear_minimizer_result_projection` to operate on
+      `self.fit_result` is the correct call site.
+
+      Note: `optimizer_name` and `method_name` were already removed
+      by the consolidation work (`_engine_metadata` dict replaces
+      them); this step is the bulk removal of the remaining LSQ
+      outputs.
+
+      Commit: `Remove LSQ output descriptors from minimizer base`
+
+- [x] **P1.10 — Remove duplicate fields from Bayesian minimizer base.**
+      In
+      `src/easydiffraction/analysis/categories/minimizer/bayesian_base.py`,
+      delete the descriptor declarations and properties for
+      `runtime_seconds`, `point_estimate_name`, `sampler_completed`,
+      `credible_interval_inner`, `credible_interval_outer`,
+      `acceptance_rate_mean`, `gelman_rubin_max`,
+      `effective_sample_size_min`, `best_log_posterior`. Remove these
+      names from `_expected_descriptor_names` and
+      `_result_descriptor_names`. The `_setting_descriptor_names` tuple
+      keeps the seven Bayesian inputs.
+
+      Commit: `Remove Bayesian output descriptors from minimizer base`
+
+- [x] **P1.11 — Update CIF emit/read for the split.** In
+      `src/easydiffraction/io/cif/serialize.py`:
+
+  **No category-list reordering is performed in this step.** Neither
+  `Analysis._serializable_categories()` nor
+  `Analysis._fit_state_categories()` is restructured. `fit_result` stays
+  conditionally included by `_fit_state_categories()` only when
+  `self._has_persisted_fit_state()` is true — exactly as today. Pre-fit
+  projects continue to emit no `_fit_result.*` block.
+
+  The only changes in this step are content updates inside the existing
+  emit/read flow:
+  - `_minimizer.*` emit/read continues to handle settings only (the
+    minimizer category's `from_cif` walks its remaining descriptors
+    after P1.9 / P1.10 removed the output descriptors).
+  - `_fit_result.*` emit/read picks up the new family-specific
+    descriptors automatically because P1.6 wires the paired class
+    (`LeastSquaresFitResult` or `BayesianFitResult`) onto
+    `self._fit_result`. The existing
+    `analysis.fit_result.from_cif(block)` call inside
+    `_restore_common_fit_state`
+    ([`serialize.py:590`](../../../src/easydiffraction/io/cif/serialize.py))
+    reads `_fit_result.*` tags into the already-paired class — no
+    reordering, no new call.
+  - The read-side already restores `minimizer.type` first
+    ([`serialize.py:553-555`](../../../src/easydiffraction/io/cif/serialize.py)),
+    so the paired-class swap fires before `fit_result.from_cif` runs. No
+    code change is required here.
+  - Update the legacy-tag rejection message in
+    `_raise_for_legacy_analysis_tags` to include the now-removed
+    `_minimizer.<output_name>` tags (e.g. `_minimizer.runtime_seconds`,
+    `_minimizer.gelman_rubin_max`) as legacy markers that should raise a
+    clear error rather than load silently.
+
+  Commit: `Serialize fit outputs to _fit_result.* tags`
+
+- [x] **P1.12 — Confirm `_fit_state_categories` returns the paired
+      `fit_result`.** In `src/easydiffraction/analysis/analysis.py`,
+      `_fit_state_categories()` already returns
+      `[self.fit_parameters,     self.fit_result, self.fit_parameter_correlations]`
+      when persisted fit state exists. After P1.6 wires the paired-class
+      construction, `self.fit_result` is automatically the paired
+      `LeastSquaresFitResult` / `BayesianFitResult` instance — no method
+      body change is needed. The dead branch in `_fit_state_categories`
+      (review-9 finding F4, open issue #101) can be cleaned up here
+      since this step is already reading the function. The plan does not
+      require the cleanup; if taken, mention "closes #101" in the commit
+      message.
+
+  Commit: `Confirm fit_result paired instance flows through serializer`
+
+- [x] **P1.13 — Update `project.display.fit.results()` to add a
+      "Settings used" block.** In
+      `src/easydiffraction/project/display.py`, extend the existing
+      results-display method to print, above the current tables, a
+      one-section table titled "Settings used" populated from
+      `analysis.minimizer.*`. Use the same `render_table` machinery the
+      rest of the display facade uses. Commit:
+      `Add settings-used block to fit.results display`
+
+- [x] **P1.14 — Amend the five accepted ADRs listed in §"ADR".** For
+      each, apply the matching paragraph from the ADR's §"ADRs amended"
+      section:
+  - `minimizer-category-consolidation.md` — §1 partial-rule
+    qualification; §"Alternatives Considered → D" reversal record.
+  - `analysis-cif-fit-state.md` — §"Minimizer fit projection" rewrite.
+  - `runtime-fit-results.md` — closing-paragraph reference.
+  - `switchable-category-owned-selectors.md` — §1 paired-category
+    exception paragraph.
+  - `display-ux.md` — `project.display.fit.results()` settings-block
+    note.
+  - Update `docs/dev/adrs/index.md` to add the new ADR row under
+    "Accepted" (per P1.16 promotion).
+
+  Commit: `Amend affected ADRs for minimizer input/output split`
+
+- [x] **P1.15 — Update tutorials.** `git grep` `docs/docs/tutorials/`
+      for `analysis.minimizer.<output_field>` references and rewrite
+      each per the migration table below. The two **collapsed** rows
+      target existing common fields on `FitResultBase` (already written
+      by the existing common projection writer); they are not 1:1
+      renames of the old setter/getter name. The other rows are
+      moved-but-keep-the-name relocations.
+
+      | Old (removed at P1.9 / P1.10) | New | Notes |
+      | --- | --- | --- |
+      | `analysis.minimizer.runtime_seconds` | `analysis.fit_result.fitting_time` | Collapsed onto existing common field; setter remains `fit_result._set_fitting_time(...)` (already in `FitResultBase`). |
+      | `analysis.minimizer.iterations_performed` | `analysis.fit_result.iterations` | Collapsed onto existing common field; setter remains `fit_result._set_iterations(...)`. |
+      | `analysis.minimizer.objective_name` | `analysis.fit_result.objective_name` | Moved to `LeastSquaresFitResult`. |
+      | `analysis.minimizer.objective_value` | `analysis.fit_result.objective_value` | Moved to `LeastSquaresFitResult`. |
+      | `analysis.minimizer.n_data_points` | `analysis.fit_result.n_data_points` | Moved to `LeastSquaresFitResult`. |
+      | `analysis.minimizer.n_parameters` | `analysis.fit_result.n_parameters` | Moved to `LeastSquaresFitResult`. |
+      | `analysis.minimizer.n_free_parameters` | `analysis.fit_result.n_free_parameters` | Moved to `LeastSquaresFitResult`. |
+      | `analysis.minimizer.degrees_of_freedom` | `analysis.fit_result.degrees_of_freedom` | Moved to `LeastSquaresFitResult`. |
+      | `analysis.minimizer.covariance_available` | `analysis.fit_result.covariance_available` | Moved to `LeastSquaresFitResult`. |
+      | `analysis.minimizer.correlation_available` | `analysis.fit_result.correlation_available` | Moved to `LeastSquaresFitResult`. |
+      | `analysis.minimizer.exit_reason` | `analysis.fit_result.exit_reason` | Moved to `LeastSquaresFitResult`. |
+      | `analysis.minimizer.point_estimate_name` | `analysis.fit_result.point_estimate_name` | Moved to `BayesianFitResult`. |
+      | `analysis.minimizer.sampler_completed` | `analysis.fit_result.sampler_completed` | Moved to `BayesianFitResult`. |
+      | `analysis.minimizer.credible_interval_inner` | `analysis.fit_result.credible_interval_inner` | Moved to `BayesianFitResult`. |
+      | `analysis.minimizer.credible_interval_outer` | `analysis.fit_result.credible_interval_outer` | Moved to `BayesianFitResult`. |
+      | `analysis.minimizer.acceptance_rate_mean` | `analysis.fit_result.acceptance_rate_mean` | Moved to `BayesianFitResult`. |
+      | `analysis.minimizer.gelman_rubin_max` | `analysis.fit_result.gelman_rubin_max` | Moved to `BayesianFitResult`. |
+      | `analysis.minimizer.effective_sample_size_min` | `analysis.fit_result.effective_sample_size_min` | Moved to `BayesianFitResult`. |
+      | `analysis.minimizer.best_log_posterior` | `analysis.fit_result.best_log_posterior` | Moved to `BayesianFitResult`. |
+
+      Run `pixi run notebook-prepare` to regenerate the `.ipynb`
+      files.
+
+      Verification grep (must return empty against
+      `docs/docs/tutorials/`):
+
+      ```
+      git grep -nE 'analysis\.minimizer\.(runtime_seconds|iterations_performed|objective_value|objective_name|n_data_points|n_parameters|n_free_parameters|degrees_of_freedom|covariance_available|correlation_available|exit_reason|point_estimate_name|sampler_completed|credible_interval_inner|credible_interval_outer|acceptance_rate_mean|gelman_rubin_max|effective_sample_size_min|best_log_posterior)' docs/docs/tutorials/
+      ```
+
+      Commit: `Update tutorials to read outputs from fit_result`
+
+- [x] **P1.16 — Promote ADR + update index.**
+  - `git mv docs/dev/adrs/suggestions/minimizer-input-output-split.md docs/dev/adrs/accepted/minimizer-input-output-split.md`.
+    Flip the Status header to `Accepted`.
+  - Move the seven `_reply-N.md` and seven `_review-N.md` siblings: keep
+    them next to the ADR if the project convention preserves history
+    under `accepted/`; delete them if the convention is to drop the
+    deliberation artefacts on promotion (the
+    `switchable-category-owned-selectors` precedent deleted them). Per
+    the precedent, delete on promotion.
+  - Update `docs/dev/adrs/index.md` — move the row for this ADR from
+    Suggestion → Accepted.
+
+  Commit: `Promote minimizer-input-output-split ADR`
+
+- [x] **P1.17 — Phase 1 review gate.** No code change. Re-run the P1.15
+      tutorial grep against `src/`, `docs/docs/tutorials/`, and
+      `tests/`. The `src/` and `docs/docs/tutorials/` scopes must return
+      empty. The `tests/` sweep is deferred to P2.1, which migrates the
+      tests. Then stop and request user review. After approval, proceed
+      to Phase 2.
+
+## Verification (Phase 2)
+
+Each command captures its log with a zsh-safe exit-code variable as
+required by `.github/copilot-instructions.md` → **Workflow**.
+
+- [x] **P2.1 — Migrate existing tests off the removed minimizer output
+      fields.** `git grep` `tests/` for the same patterns as P1.15.
+      Apply the same migration table from P1.15 — including the two
+      collapsed rows (`runtime_seconds` → `fitting_time`,
+      `iterations_performed` → `iterations`) where the setter name also
+      changes. Examples:
+
+      - Reader rewrite:
+        `analysis.minimizer.gelman_rubin_max` →
+        `analysis.fit_result.gelman_rubin_max`.
+      - Reader rewrite with collapse:
+        `analysis.minimizer.runtime_seconds` →
+        `analysis.fit_result.fitting_time`.
+      - Setter rewrite (moved-but-kept name):
+        `analysis.minimizer._set_gelman_rubin_max(...)` →
+        `analysis.fit_result._set_gelman_rubin_max(...)`.
+      - Setter rewrite (collapsed name):
+        `analysis.minimizer._set_runtime_seconds(...)` →
+        `analysis.fit_result._set_fitting_time(...)`.
+
+      Layout check:
+
+      ```
+      pixi run test-structure-check > /tmp/easydiffraction-test-structure-check.log 2>&1; \
+        test_structure_check_exit_code=$?; \
+        tail -n 200 /tmp/easydiffraction-test-structure-check.log; \
+        exit $test_structure_check_exit_code
+      ```
+
+      Final stale-reference grep (all four must return empty):
+
+      ```
+      git grep -nE 'analysis\.minimizer\.(runtime_seconds|iterations_performed|objective_value|objective_name|n_data_points|n_parameters|n_free_parameters|degrees_of_freedom|covariance_available|correlation_available|exit_reason|point_estimate_name|sampler_completed|credible_interval_inner|credible_interval_outer|acceptance_rate_mean|gelman_rubin_max|effective_sample_size_min|best_log_posterior)' src/ docs/docs/tutorials/ tests/
+      git grep -nE '_minimizer\.(runtime_seconds|iterations_performed|objective_value|point_estimate_name|sampler_completed|credible_interval_inner|credible_interval_outer|acceptance_rate_mean|gelman_rubin_max|effective_sample_size_min|best_log_posterior)' src/ docs/docs/tutorials/ tests/
+      ```
+
+- [x] **P2.2 — Add unit tests for new modules.** New tests under
+      `tests/unit/easydiffraction/analysis/categories/fit_result/`:
+  - `test_base.py` — `FitResultBase` defaults,
+    `_reset_result_descriptors`.
+  - `test_lsq.py` — `LeastSquaresFitResult` defaults; CIF round-trip of
+    LSQ outputs.
+  - `test_bayesian.py` — `BayesianFitResult` defaults including the
+    fixed credible interval levels; CIF round-trip.
+  - `test_factory.py` — pairing rule via
+    `LeastSquaresMinimizerBase._fit_result_class` and
+    `BayesianMinimizerBase._fit_result_class`.
+
+    Layout check:
+
+    ```
+    pixi run test-structure-check > /tmp/easydiffraction-test-structure-check.log 2>&1; \
+      test_structure_check_exit_code=$?; \
+      tail -n 200 /tmp/easydiffraction-test-structure-check.log; \
+      exit $test_structure_check_exit_code
+    ```
+
+- [x] **P2.3 — Auto-fixes and static checks.**
+
+      ```
+      pixi run fix > /tmp/easydiffraction-fix.log 2>&1; \
+        fix_exit_code=$?; \
+        tail -n 200 /tmp/easydiffraction-fix.log; \
+        exit $fix_exit_code
+      ```
+
+      Then:
+
+      ```
+      pixi run check > /tmp/easydiffraction-check.log 2>&1; \
+        check_exit_code=$?; \
+        tail -n 200 /tmp/easydiffraction-check.log; \
+        exit $check_exit_code
+      ```
+
+      Iterate `pixi run check` until clean. Do not raise lint
+      thresholds — refactor instead.
+
+- [x] **P2.4 — Unit tests.**
+
+      ```
+      pixi run unit-tests > /tmp/easydiffraction-unit-tests.log 2>&1; \
+        unit_tests_exit_code=$?; \
+        tail -n 200 /tmp/easydiffraction-unit-tests.log; \
+        exit $unit_tests_exit_code
+      ```
+
+- [x] **P2.5 — Integration tests.**
+
+      ```
+      pixi run integration-tests > /tmp/easydiffraction-integration-tests.log 2>&1; \
+        integration_tests_exit_code=$?; \
+        tail -n 200 /tmp/easydiffraction-integration-tests.log; \
+        exit $integration_tests_exit_code
+      ```
+
+- [x] **P2.6 — Script tests.**
+
+      ```
+      pixi run script-tests > /tmp/easydiffraction-script-tests.log 2>&1; \
+        script_tests_exit_code=$?; \
+        tail -n 200 /tmp/easydiffraction-script-tests.log; \
+        exit $script_tests_exit_code
+      ```
+
+      This regenerates `tmp/tutorials/projects/*` fixtures with the
+      new CIF layout (`_minimizer.*` settings-only,
+      `_fit_result.*` outputs).
+
+## Suggested Pull Request
+
+**Title:** Split minimizer settings from fit-result outputs
+
+**Description (user-facing):**
+
+`analysis.minimizer` now holds only the settings you can change —
+`sampling_steps`, `max_iterations`, and the other input knobs. Once a
+fit completes, every output the project records (wall time, χ², the
+Bayesian diagnostics, the LSQ counters) lives on a paired
+`analysis.fit_result`. The pairing happens automatically when you change
+minimizer type, so users never see a separate result selector.
+
+`project.display.fit.results()` now prints a "Settings used" block above
+the existing result tables, so the settings that produced the fit and
+the outputs the fit produced are visible side-by-side without having to
+open two namespaces.
+
+The CIF layout follows the same split: `_minimizer.*` holds settings
+only, `_fit_result.*` holds outputs. Saved projects from the previous
+layout do not load unchanged (the project is in beta; no legacy shims).
+Tutorials and saved-fixture regeneration land in this PR.
+
+Incidental cleanup also bundled in this branch: the unused
+`essdiffraction` development dependency is removed, and a handful of
+unrelated functions are split into helpers to satisfy the project's
+complexity thresholds during Phase 2 verification:
+`singleton.ConstraintsHandler.apply_constraints`,
+`analysis.sequential._fit_worker`,
+`display.plotting._posterior_predictive_*`, and
+`calculators.{crysfml,pdffit}._calculate_pattern`.
diff --git a/docs/docs/tutorials/ed-24.ipynb b/docs/docs/tutorials/ed-24.ipynb
index 869a30919..4e77ed733 100644
--- a/docs/docs/tutorials/ed-24.ipynb
+++ b/docs/docs/tutorials/ed-24.ipynb
@@ -46,9 +46,7 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "3",
-   "metadata": {
-    "lines_to_next_cell": 1
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from pathlib import Path\n",
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   sha256: 607c53218732647dff4acdfcd50cb62615cedf612e72d1724fb1a0cc6405b378
   requires_python: '>=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*'
-- pypi: https://files.pythonhosted.org/packages/f7/00/bbca25f8a2372465cdf93138c1e1e38dff045fb0afef1488f395d0afcb3b/ncrystal-4.4.2-py3-none-any.whl
-  name: ncrystal
-  version: 4.4.2
-  sha256: e02fa7d743addc3fbea23287737a88b8d01192450fdca51554d3f9032fe4617c
-  requires_dist:
-  - ncrystal-core==4.4.2
-  - ncrystal-python==4.4.2
-  - spglib>=2.1.0 ; extra == 'composer'
-  - ase>=3.23.0 ; extra == 'cif'
-  - gemmi>=0.6.1 ; extra == 'cif'
-  - spglib>=2.1.0 ; extra == 'cif'
-  - endf-parserpy>=0.14.3 ; extra == 'endf'
-  - matplotlib>=3.6.0 ; extra == 'plot'
-  - ase>=3.23.0 ; extra == 'all'
-  - endf-parserpy>=0.14.3 ; extra == 'all'
-  - gemmi>=0.6.1 ; extra == 'all'
-  - matplotlib>=3.6.0 ; extra == 'all'
-  - spglib>=2.1.0 ; extra == 'all'
-  - pyyaml>=6.0.0 ; extra == 'devel'
-  - ase>=3.23.0 ; extra == 'devel'
-  - cppcheck ; extra == 'devel'
-  - endf-parserpy>=0.14.3 ; extra == 'devel'
-  - gemmi>=0.6.1 ; extra == 'devel'
-  - matplotlib>=3.6.0 ; extra == 'devel'
-  - mpmath>=1.3.0 ; extra == 'devel'
-  - numpy>=1.22 ; extra == 'devel'
-  - pybind11>=2.11.0 ; extra == 'devel'
-  - ruff>=0.8.1 ; extra == 'devel'
-  - simple-build-system>=1.6.0 ; extra == 'devel'
-  - spglib>=2.1.0 ; extra == 'devel'
-  - tomli>=2.0.0 ; python_full_version < '3.11' and extra == 'devel'
-  requires_python: '>=3.8'
 - pypi: https://files.pythonhosted.org/packages/f7/ec/67fbef5d497f86283db54c22eec6f6140243aae73265799baaaa19cd17fb/ghp_import-2.1.0-py3-none-any.whl
   name: ghp-import
   version: 2.1.0
@@ -12934,11 +11815,6 @@ packages:
   - multidict>=4.0
   - propcache>=0.2.1
   requires_python: '>=3.10'
-- pypi: https://files.pythonhosted.org/packages/fb/12/5911ae3eeec47800503a238d971e51722ccea5feb8569b735184d5fcdbc0/toolz-1.1.0-py3-none-any.whl
-  name: toolz
-  version: 1.1.0
-  sha256: 15ccc861ac51c53696de0a5d6d4607f99c210739caf987b5d2054f3efed429d8
-  requires_python: '>=3.9'
 - pypi: https://files.pythonhosted.org/packages/fb/76/641ae371508676492379f16e2fa48f4e2c11741bd63c48be4b12a6b09cba/aiosignal-1.4.0-py3-none-any.whl
   name: aiosignal
   version: 1.4.0
diff --git a/pyproject.toml b/pyproject.toml
index 507a069d1..d953cfa19 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -52,7 +52,6 @@ dependencies = [
 
 [project.optional-dependencies]
 dev = [
-  'essdiffraction',                  # ESS-specific diffraction library
   'GitPython',                       # Interact with Git repositories
   'build',                           # Building the package
   'pre-commit',                      # Pre-commit hooks
diff --git a/src/easydiffraction/analysis/__init__.py b/src/easydiffraction/analysis/__init__.py
index 186c375c8..325c9882a 100644
--- a/src/easydiffraction/analysis/__init__.py
+++ b/src/easydiffraction/analysis/__init__.py
@@ -11,7 +11,7 @@
 from easydiffraction.analysis.categories.fit_parameters import FitParameterItem
 from easydiffraction.analysis.categories.fit_parameters import FitParameters
 from easydiffraction.analysis.categories.fit_parameters import FitParametersFactory
-from easydiffraction.analysis.categories.fit_result import FitResult
+from easydiffraction.analysis.categories.fit_result import FitResultBase
 from easydiffraction.analysis.categories.fit_result import FitResultFactory
 from easydiffraction.analysis.categories.joint_fit import JointFitCollection
 from easydiffraction.analysis.categories.joint_fit import JointFitFactory
diff --git a/src/easydiffraction/analysis/analysis.py b/src/easydiffraction/analysis/analysis.py
index 87676646e..9a01d8385 100644
--- a/src/easydiffraction/analysis/analysis.py
+++ b/src/easydiffraction/analysis/analysis.py
@@ -15,7 +15,6 @@
 from easydiffraction.analysis.categories.constraints.factory import ConstraintsFactory
 from easydiffraction.analysis.categories.fit_parameter_correlations import FitParameterCorrelations
 from easydiffraction.analysis.categories.fit_parameters import FitParameters
-from easydiffraction.analysis.categories.fit_result import FitResult
 from easydiffraction.analysis.categories.fitting_mode import FittingMode
 from easydiffraction.analysis.categories.fitting_mode import FittingModeFactory
 from easydiffraction.analysis.categories.joint_fit import JointFitCollection
@@ -56,6 +55,7 @@
 from easydiffraction.utils.utils import render_table
 
 if TYPE_CHECKING:
+    from easydiffraction.analysis.categories.fit_result import FitResultBase
     from easydiffraction.analysis.categories.minimizer.base import MinimizerCategoryBase
     from easydiffraction.core.posterior import PosteriorParameterSummary
 
@@ -429,7 +429,7 @@ def fit_parameters(self) -> FitParameters:
         return self._fit_parameters
 
     @property
-    def fit_result(self) -> FitResult:
+    def fit_result(self) -> FitResultBase:
         """Persisted common fit-result status metadata."""
         return self._fit_result
 
@@ -480,7 +480,7 @@ def __init__(self, project: object) -> None:
         )
         self._sequential_fit_extract = SequentialFitExtractCollection()
         self._fit_parameters = FitParameters()
-        self._fit_result = FitResult()
+        self._fit_result = self._minimizer._fit_result_class()
         self._fit_parameter_correlations = FitParameterCorrelations()
         self._has_persisted_fit_state_data = False
         self._persisted_fit_state_sidecar: dict[str, object] = {}
@@ -736,13 +736,13 @@ def _restore_fit_results_from_projection(self) -> object | None:
                 starting_parameters=list(restored_parameters),
                 fitting_time=fitting_time,
                 sampler_name=sampler_name,
-                point_estimate_name=self.minimizer.point_estimate_name.value,
+                point_estimate_name=self.fit_result.point_estimate_name.value or 'best_sample',
                 posterior_samples=posterior_samples,
                 posterior_parameter_summaries=self._restored_posterior_summaries(),
                 posterior_predictive=self._restored_predictive_summaries(),
                 credible_interval_levels=(
-                    float(self.minimizer.credible_interval_inner.value),
-                    float(self.minimizer.credible_interval_outer.value),
+                    float(self.fit_result.credible_interval_inner.value),
+                    float(self.fit_result.credible_interval_outer.value),
                 ),
                 sampler_settings={
                     'steps': int(sampler_settings.get('steps', 0)),
@@ -755,17 +755,17 @@ def _restore_fit_results_from_projection(self) -> object | None:
                 },
                 convergence_diagnostics={
                     'converged': False,
-                    'max_r_hat': self.minimizer.gelman_rubin_max.value,
-                    'min_ess_bulk': self.minimizer.effective_sample_size_min.value,
+                    'max_r_hat': self.fit_result.gelman_rubin_max.value,
+                    'min_ess_bulk': self.fit_result.effective_sample_size_min.value,
                     'n_draws': int(sample_shape[0]),
                     'n_chains': int(sample_shape[1]),
                     'n_parameters': int(sample_shape[2]),
                 },
-                sampler_completed=bool(self.minimizer.sampler_completed.value),
-                best_log_posterior=self.minimizer.best_log_posterior.value,
+                sampler_completed=bool(self.fit_result.sampler_completed.value),
+                best_log_posterior=self.fit_result.best_log_posterior.value,
             )
-            restored_results.message = self.fit_result.message.value
-            restored_results.iterations = int(self.fit_result.iterations.value)
+            restored_results.message = self.fit_result.message.value or ''
+            restored_results.iterations = _int_or_none(self.fit_result.iterations.value) or 0
             self.fit_results = restored_results
             return restored_results
 
@@ -778,21 +778,21 @@ def _restore_fit_results_from_projection(self) -> object | None:
             fitting_time=fitting_time,
             optimizer_name=engine_metadata['optimizer_name'],
             method_name=engine_metadata['method_name'],
-            objective_name=self.minimizer.objective_name.value,
-            objective_value=self.minimizer.objective_value.value,
-            n_data_points=_int_or_none(self.minimizer.n_data_points.value),
-            n_parameters=_int_or_none(self.minimizer.n_parameters.value),
-            n_free_parameters=_int_or_none(self.minimizer.n_free_parameters.value),
-            degrees_of_freedom=_int_or_none(self.minimizer.degrees_of_freedom.value),
-            covariance_available=self.minimizer.covariance_available.value,
-            correlation_available=self.minimizer.correlation_available.value,
-            runtime_seconds=self.minimizer.runtime_seconds.value,
-            iterations_performed=_int_or_none(self.minimizer.iterations_performed.value),
-            exit_reason=self.minimizer.exit_reason.value,
+            objective_name=self.fit_result.objective_name.value,
+            objective_value=self.fit_result.objective_value.value,
+            n_data_points=_int_or_none(self.fit_result.n_data_points.value),
+            n_parameters=_int_or_none(self.fit_result.n_parameters.value),
+            n_free_parameters=_int_or_none(self.fit_result.n_free_parameters.value),
+            degrees_of_freedom=_int_or_none(self.fit_result.degrees_of_freedom.value),
+            covariance_available=self.fit_result.covariance_available.value,
+            correlation_available=self.fit_result.correlation_available.value,
+            runtime_seconds=fitting_time,
+            iterations_performed=_int_or_none(self.fit_result.iterations.value),
+            exit_reason=self.fit_result.exit_reason.value,
         )
-        restored_results.message = self.fit_result.message.value
-        restored_results.iterations = int(self.fit_result.iterations.value)
-        restored_results.chi_square = self.minimizer.objective_value.value
+        restored_results.message = self.fit_result.message.value or ''
+        restored_results.iterations = _int_or_none(self.fit_result.iterations.value) or 0
+        restored_results.chi_square = self.fit_result.objective_value.value
         self.fit_results = restored_results
         return restored_results
 
@@ -1062,8 +1062,11 @@ def _replace_minimizer(
         self._warn_about_minimizer_swap_defaults(old_defaults, new_minimizer)
 
         old_minimizer._parent = None
+        self._fit_result._parent = None
         self._minimizer = new_minimizer
+        self._fit_result = new_minimizer._fit_result_class()
         self._minimizer._parent = self
+        self._fit_result._parent = self
         self._fitter = Fitter(value)
         if announce:
             console.paragraph('Current minimizer changed to')
@@ -1203,16 +1206,19 @@ def _fit_state_categories(self) -> list[object]:
 
     def _clear_persisted_fit_state(self) -> None:
         """Reset all persisted fit-state categories before a new fit."""
-        self._clear_minimizer_result_projection()
         self._fit_parameters = FitParameters()
-        self._fit_result = FitResult()
+        self._fit_result._parent = None
+        self._fit_result = self.minimizer._fit_result_class()
+        self._fit_result._parent = self
         self._fit_parameter_correlations = FitParameterCorrelations()
         self._set_has_persisted_fit_state(value=False)
         self._persisted_fit_state_sidecar = {}
 
-    def _clear_minimizer_result_projection(self) -> None:
-        """Reset result-only fields on the active minimizer category."""
-        self.minimizer._reset_result_descriptors()
+    def _clear_fit_result_projection(self) -> None:
+        """
+        Reset result-only fields on the active fit-result category.
+        """
+        self.fit_result._reset_result_descriptors()
 
     def _capture_fit_parameter_state(self, parameters: list[Parameter]) -> None:
         """Capture pre-fit parameter state."""
@@ -1372,17 +1378,15 @@ def _store_least_squares_result_projection(
             else None
         )
 
-        self.minimizer._set_objective_name('chi_square')
-        self.minimizer._set_objective_value(self._resolve_objective_value(results))
-        self.minimizer._set_n_data_points(n_data_points)
-        self.minimizer._set_n_parameters(n_parameters)
-        self.minimizer._set_n_free_parameters(n_free_parameters)
-        self.minimizer._set_degrees_of_freedom(degrees_of_freedom)
-        self.minimizer._set_covariance_available(value=covariance is not None)
-        self.minimizer._set_correlation_available(value=correlation_matrix is not None)
-        self.minimizer._set_runtime_seconds(results.fitting_time)
-        self.minimizer._set_iterations_performed(results.iterations)
-        self.minimizer._set_exit_reason(results.message)
+        self.fit_result._set_objective_name('chi_square')
+        self.fit_result._set_objective_value(self._resolve_objective_value(results))
+        self.fit_result._set_n_data_points(n_data_points)
+        self.fit_result._set_n_parameters(n_parameters)
+        self.fit_result._set_n_free_parameters(n_free_parameters)
+        self.fit_result._set_degrees_of_freedom(degrees_of_freedom)
+        self.fit_result._set_covariance_available(value=covariance is not None)
+        self.fit_result._set_correlation_available(value=correlation_matrix is not None)
+        self.fit_result._set_exit_reason(results.message)
 
         if correlation_matrix is not None:
             self._store_correlation_projection(
@@ -1679,15 +1683,14 @@ def _store_posterior_fit_projection(self, results: BayesianFitResults) -> None:
         point_estimate_name = results.point_estimate_name or 'best_sample'
         convergence = results.convergence_diagnostics
 
-        self.minimizer._set_runtime_seconds(results.fitting_time)
-        self.minimizer._set_point_estimate_name(point_estimate_name)
-        self.minimizer._set_sampler_completed(value=results.sampler_completed)
-        self.minimizer._set_best_log_posterior(results.best_log_posterior)
-        self.minimizer._set_credible_interval_inner(credible_interval_inner)
-        self.minimizer._set_credible_interval_outer(credible_interval_outer)
-        self.minimizer._set_gelman_rubin_max(convergence.get('max_r_hat'))
-        self.minimizer._set_effective_sample_size_min(convergence.get('min_ess_bulk'))
-        self.minimizer._set_acceptance_rate_mean(convergence.get('acceptance_rate_mean'))
+        self.fit_result._set_point_estimate_name(point_estimate_name)
+        self.fit_result._set_sampler_completed(value=results.sampler_completed)
+        self.fit_result._set_best_log_posterior(results.best_log_posterior)
+        self.fit_result._set_credible_interval_inner(credible_interval_inner)
+        self.fit_result._set_credible_interval_outer(credible_interval_outer)
+        self.fit_result._set_gelman_rubin_max(convergence.get('max_r_hat'))
+        self.fit_result._set_effective_sample_size_min(convergence.get('min_ess_bulk'))
+        self.fit_result._set_acceptance_rate_mean(convergence.get('acceptance_rate_mean'))
         self._store_posterior_samples_sidecar_projection(results)
 
         live_parameters = {
@@ -1945,33 +1948,14 @@ def _fit_single(
         self.fitter.minimizer.tracker._set_shared_display_handle(short_display_handle)
 
         try:
-            for expt_name in expt_names:
-                if verb is VerbosityEnum.FULL:
-                    console.print(
-                        f"📋 Using experiment 🔬 '{expt_name}' for '{mode.value}' fitting"
-                    )
-
-                experiment = experiments[expt_name]
-                self.fitter.fit(
-                    structures,
-                    [experiment],
-                    analysis=self,
-                    verbosity=verb,
-                    use_physical_limits=use_physical_limits,
-                    random_seed=self._resolved_fit_random_seed(random_seed),
-                )
-
-                # After fitting, snapshot parameter values before
-                # they get overwritten by the next experiment's fit
-                results = self.fitter.results
-                self._snapshot_params(expt_name, results)
-                self.fit_results = results
-
-                # Short mode: append one summary row and update in-place
-                if verb is VerbosityEnum.SHORT:
-                    self._fit_single_update_short_table(
-                        short_rows, expt_name, results, short_display_handle
-                    )
+            self._fit_single_experiments(
+                verb,
+                structures,
+                experiments,
+                use_physical_limits=use_physical_limits,
+                random_seed=random_seed,
+                short_state=(short_rows, short_display_handle),
+            )
         finally:
             self.fitter.minimizer.tracker._set_shared_display_handle(None)
 
@@ -1980,6 +1964,47 @@ def _fit_single(
                 with suppress(Exception):
                     short_display_handle.close()
 
+    def _fit_single_experiments(
+        self,
+        verb: VerbosityEnum,
+        structures: object,
+        experiments: object,
+        *,
+        use_physical_limits: bool,
+        random_seed: int | None,
+        short_state: tuple[list[list[str]], object],
+    ) -> None:
+        """Run the per-experiment loop for single-fit mode."""
+        short_rows, short_display_handle = short_state
+        for expt_name in experiments.names:
+            if verb is VerbosityEnum.FULL:
+                console.print(
+                    f"📋 Using experiment 🔬 '{expt_name}' for "
+                    f"'{FitModeEnum.SINGLE.value}' fitting"
+                )
+
+            experiment = experiments[expt_name]
+            self.fitter.fit(
+                structures,
+                [experiment],
+                analysis=self,
+                verbosity=verb,
+                use_physical_limits=use_physical_limits,
+                random_seed=self._resolved_fit_random_seed(random_seed),
+            )
+
+            results = self.fitter.results
+            self._snapshot_params(expt_name, results)
+            self.fit_results = results
+
+            if verb is VerbosityEnum.SHORT:
+                self._fit_single_update_short_table(
+                    short_rows,
+                    expt_name,
+                    results,
+                    short_display_handle,
+                )
+
     @staticmethod
     def _fit_single_print_header(
         verb: VerbosityEnum,
diff --git a/src/easydiffraction/analysis/calculators/crysfml.py b/src/easydiffraction/analysis/calculators/crysfml.py
index 85ff5779f..70231b02f 100644
--- a/src/easydiffraction/analysis/calculators/crysfml.py
+++ b/src/easydiffraction/analysis/calculators/crysfml.py
@@ -129,22 +129,40 @@ def calculate_pattern(
 
         crysfml_dict = self._crysfml_dict(structure, experiment)
         try:
-            if experiment.type.beam_mode.value == BeamModeEnum.CONSTANT_WAVELENGTH:
-                _, y = cfml_py_utilities.cw_powder_pattern_from_dict(crysfml_dict)
-            elif experiment.type.beam_mode.value == BeamModeEnum.TIME_OF_FLIGHT:
-                _, y = cfml_py_utilities.tof_powder_pattern_from_dict(crysfml_dict)
-            else:
-                print(
-                    f'[CrysfmlCalculator] Error: '
-                    f'Unsupported beam mode {experiment.type.beam_mode.value}'
-                )
-                return np.array([])
-            y = self._adjust_pattern_length(y, len(experiment.data.x))
+            y = self._calculate_adjusted_pattern(crysfml_dict, experiment)
         except KeyError:
             print('[CrysfmlCalculator] Error: No calculated data')
             y = []
         return np.asarray(y)
 
+    def _calculate_adjusted_pattern(
+        self,
+        crysfml_dict: dict[str, object],
+        experiment: ExperimentBase,
+    ) -> list[float]:
+        """Calculate a Crysfml pattern and match experiment length."""
+        y = self._calculate_raw_pattern(crysfml_dict, experiment)
+        if y is None:
+            return []
+        return self._adjust_pattern_length(y, len(experiment.data.x))
+
+    @staticmethod
+    def _calculate_raw_pattern(
+        crysfml_dict: dict[str, object],
+        experiment: ExperimentBase,
+    ) -> list[float] | None:
+        """Calculate a Crysfml pattern without length adjustment."""
+        if experiment.type.beam_mode.value == BeamModeEnum.CONSTANT_WAVELENGTH:
+            _, y = cfml_py_utilities.cw_powder_pattern_from_dict(crysfml_dict)
+            return y
+        if experiment.type.beam_mode.value == BeamModeEnum.TIME_OF_FLIGHT:
+            _, y = cfml_py_utilities.tof_powder_pattern_from_dict(crysfml_dict)
+            return y
+        print(
+            f'[CrysfmlCalculator] Error: Unsupported beam mode {experiment.type.beam_mode.value}'
+        )
+        return None
+
     def _adjust_pattern_length(  # noqa: PLR6301
         self,
         pattern: list[float],
diff --git a/src/easydiffraction/analysis/calculators/pdffit.py b/src/easydiffraction/analysis/calculators/pdffit.py
index ed0717536..a9c3b3697 100644
--- a/src/easydiffraction/analysis/calculators/pdffit.py
+++ b/src/easydiffraction/analysis/calculators/pdffit.py
@@ -19,17 +19,19 @@
 from easydiffraction.datablocks.experiment.item.base import ExperimentBase
 from easydiffraction.datablocks.structure.item.base import Structure
 
+
+def _open_pdffit_devnull() -> object:
+    """Open a durable devnull handle for PDFfit stdout redirection."""
+    with Path(os.devnull).open('w', encoding='utf-8') as tmp_devnull:
+        return os.fdopen(os.dup(tmp_devnull.fileno()), 'w')
+
+
 try:
     from diffpy.pdffit2 import PdfFit
     from diffpy.pdffit2 import redirect_stdout
     from diffpy.structure.parsers.p_cif import P_cif as pdffit_cif_parser
 
-    # Silence the C++ engine output while keeping the handle open
-    _pdffit_devnull: object | None
-    with Path(os.devnull).open('w', encoding='utf-8') as _tmp_devnull:
-        # Duplicate file descriptor so the handle remains
-        # valid after the context
-        _pdffit_devnull = os.fdopen(os.dup(_tmp_devnull.fileno()), 'w')
+    _pdffit_devnull = _open_pdffit_devnull()
     redirect_stdout(_pdffit_devnull)
     # TODO: Add the following print to debug mode
     # print("✅ 'pdffit' calculation engine is successfully imported.")
diff --git a/src/easydiffraction/analysis/categories/__init__.py b/src/easydiffraction/analysis/categories/__init__.py
index d6dcda46b..54fea54e5 100644
--- a/src/easydiffraction/analysis/categories/__init__.py
+++ b/src/easydiffraction/analysis/categories/__init__.py
@@ -11,7 +11,7 @@
 from easydiffraction.analysis.categories.fit_parameter_correlations import FitParameterCorrelations
 from easydiffraction.analysis.categories.fit_parameters import FitParameterItem
 from easydiffraction.analysis.categories.fit_parameters import FitParameters
-from easydiffraction.analysis.categories.fit_result import FitResult
+from easydiffraction.analysis.categories.fit_result import FitResultBase
 from easydiffraction.analysis.categories.fitting_mode import FittingMode
 from easydiffraction.analysis.categories.fitting_mode import FittingModeFactory
 from easydiffraction.analysis.categories.joint_fit import JointFitCollection
diff --git a/src/easydiffraction/analysis/categories/fit_result/__init__.py b/src/easydiffraction/analysis/categories/fit_result/__init__.py
index 8578ab47b..6bc137ee8 100644
--- a/src/easydiffraction/analysis/categories/fit_result/__init__.py
+++ b/src/easydiffraction/analysis/categories/fit_result/__init__.py
@@ -1,5 +1,7 @@
 # SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
 # SPDX-License-Identifier: BSD-3-Clause
 
-from easydiffraction.analysis.categories.fit_result.default import FitResult
+from easydiffraction.analysis.categories.fit_result.base import FitResultBase
+from easydiffraction.analysis.categories.fit_result.bayesian import BayesianFitResult
 from easydiffraction.analysis.categories.fit_result.factory import FitResultFactory
+from easydiffraction.analysis.categories.fit_result.lsq import LeastSquaresFitResult
diff --git a/src/easydiffraction/analysis/categories/fit_result/base.py b/src/easydiffraction/analysis/categories/fit_result/base.py
new file mode 100644
index 000000000..c94a35b09
--- /dev/null
+++ b/src/easydiffraction/analysis/categories/fit_result/base.py
@@ -0,0 +1,153 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Common fit-result status category."""
+
+from __future__ import annotations
+
+from typing import ClassVar
+
+from easydiffraction.analysis.categories.fit_result.factory import FitResultFactory
+from easydiffraction.analysis.enums import FitResultKindEnum
+from easydiffraction.core.category import CategoryItem
+from easydiffraction.core.metadata import TypeInfo
+from easydiffraction.core.validation import AttributeSpec
+from easydiffraction.core.validation import MembershipValidator
+from easydiffraction.core.variable import BoolDescriptor
+from easydiffraction.core.variable import GenericDescriptorBase
+from easydiffraction.core.variable import IntegerDescriptor
+from easydiffraction.core.variable import NumericDescriptor
+from easydiffraction.core.variable import StringDescriptor
+from easydiffraction.io.cif.handler import CifHandler
+
+
+@FitResultFactory.register
+class FitResultBase(CategoryItem):
+    """Common persisted fit-result status metadata."""
+
+    _category_code = 'fit_result'
+    _result_descriptor_names: ClassVar[tuple[str, ...]] = (
+        'success',
+        'message',
+        'iterations',
+        'fitting_time',
+        'reduced_chi_square',
+        'result_kind',
+    )
+
+    type_info = TypeInfo(
+        tag='default',
+        description='Common persisted fit-result status metadata',
+    )
+
+    def __init__(self) -> None:
+        super().__init__()
+        self._result_kind = StringDescriptor(
+            name='result_kind',
+            description='Kind of the latest persisted fit-result projection.',
+            value_spec=AttributeSpec(
+                default=FitResultKindEnum.default().value,
+                validator=MembershipValidator(
+                    allowed=[member.value for member in FitResultKindEnum]
+                ),
+            ),
+            cif_handler=CifHandler(names=['_fit_result.result_kind']),
+        )
+        self._success = BoolDescriptor(
+            name='success',
+            description='Whether the latest persisted fit-result projection succeeded.',
+            value_spec=AttributeSpec(default=None, allow_none=True),
+            cif_handler=CifHandler(names=['_fit_result.success']),
+        )
+        self._message = StringDescriptor(
+            name='message',
+            description='Status message for the latest persisted fit-result projection.',
+            value_spec=AttributeSpec(default=None, allow_none=True),
+            cif_handler=CifHandler(names=['_fit_result.message']),
+        )
+        self._iterations = IntegerDescriptor(
+            name='iterations',
+            description='Iteration count for the latest persisted fit-result projection.',
+            value_spec=AttributeSpec(default=None, allow_none=True),
+            cif_handler=CifHandler(names=['_fit_result.iterations']),
+        )
+        self._fitting_time = NumericDescriptor(
+            name='fitting_time',
+            description='Fitting time in seconds for the latest persisted projection.',
+            value_spec=AttributeSpec(default=None, allow_none=True),
+            cif_handler=CifHandler(names=['_fit_result.fitting_time']),
+        )
+        self._reduced_chi_square = NumericDescriptor(
+            name='reduced_chi_square',
+            description='Reduced chi-square for the latest persisted projection.',
+            value_spec=AttributeSpec(default=None, allow_none=True),
+            cif_handler=CifHandler(names=['_fit_result.reduced_chi_square']),
+        )
+
+    @property
+    def result_kind(self) -> StringDescriptor:
+        """Kind of the latest persisted fit-result projection."""
+        return self._result_kind
+
+    def _set_result_kind(self, value: str) -> None:
+        """Set the result kind for internal callers."""
+        self._result_kind.value = value
+
+    @property
+    def success(self) -> BoolDescriptor:
+        """
+        Whether the latest persisted fit-result projection succeeded.
+        """
+        return self._success
+
+    def _set_success(self, *, value: bool | None) -> None:
+        """Set the success flag for internal callers."""
+        self._success.value = value
+
+    @property
+    def message(self) -> StringDescriptor:
+        """
+        Status message for the latest persisted fit-result projection.
+        """
+        return self._message
+
+    def _set_message(self, value: str | None) -> None:
+        """Set the fit-result message for internal callers."""
+        self._message.value = value
+
+    @property
+    def iterations(self) -> IntegerDescriptor:
+        """
+        Iteration count for the latest persisted fit-result projection.
+        """
+        return self._iterations
+
+    def _set_iterations(self, value: int | None) -> None:
+        """Set the iteration count for internal callers."""
+        self._iterations.value = value
+
+    @property
+    def fitting_time(self) -> NumericDescriptor:
+        """
+        Fitting time in seconds for the latest persisted projection.
+        """
+        return self._fitting_time
+
+    def _set_fitting_time(self, value: float | None) -> None:
+        """Set the fitting time for internal callers."""
+        self._fitting_time.value = value
+
+    @property
+    def reduced_chi_square(self) -> NumericDescriptor:
+        """Reduced chi-square for the latest persisted projection."""
+        return self._reduced_chi_square
+
+    def _set_reduced_chi_square(self, value: float | None) -> None:
+        """Set the reduced chi-square for internal callers."""
+        self._reduced_chi_square.value = value
+
+    def _reset_result_descriptors(self) -> None:
+        """Reset fit-result descriptors to declared defaults."""
+        for name in self._result_descriptor_names:
+            descriptor = getattr(self, name)
+            if isinstance(descriptor, GenericDescriptorBase):
+                descriptor.value = descriptor._value_spec.default_value()
diff --git a/src/easydiffraction/analysis/categories/fit_result/bayesian.py b/src/easydiffraction/analysis/categories/fit_result/bayesian.py
new file mode 100644
index 000000000..91e1310f1
--- /dev/null
+++ b/src/easydiffraction/analysis/categories/fit_result/bayesian.py
@@ -0,0 +1,207 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Bayesian fit-result category."""
+
+from __future__ import annotations
+
+from typing import ClassVar
+
+from easydiffraction.analysis.categories.fit_result.base import FitResultBase
+from easydiffraction.analysis.categories.fit_result.factory import FitResultFactory
+from easydiffraction.core.metadata import TypeInfo
+from easydiffraction.core.validation import AttributeSpec
+from easydiffraction.core.variable import BoolDescriptor
+from easydiffraction.core.variable import NumericDescriptor
+from easydiffraction.core.variable import StringDescriptor
+from easydiffraction.io.cif.handler import CifHandler
+
+
+@FitResultFactory.register
+class BayesianFitResult(FitResultBase):
+    """Persisted Bayesian fit-result metadata."""
+
+    type_info = TypeInfo(
+        tag='bayesian',
+        description='Persisted Bayesian fit-result metadata',
+    )
+    _result_descriptor_names: ClassVar[tuple[str, ...]] = (
+        *FitResultBase._result_descriptor_names,
+        'point_estimate_name',
+        'sampler_completed',
+        'credible_interval_inner',
+        'credible_interval_outer',
+        'acceptance_rate_mean',
+        'gelman_rubin_max',
+        'effective_sample_size_min',
+        'best_log_posterior',
+    )
+    _expected_descriptor_names: ClassVar[tuple[str, ...]] = _result_descriptor_names
+
+    def __init__(self) -> None:
+        super().__init__()
+        self._point_estimate_name = self._point_estimate_name_descriptor()
+        self._sampler_completed = self._sampler_completed_descriptor()
+        self._credible_interval_inner = self._credible_interval_inner_descriptor()
+        self._credible_interval_outer = self._credible_interval_outer_descriptor()
+        self._acceptance_rate_mean = self._acceptance_rate_mean_descriptor()
+        self._gelman_rubin_max = self._gelman_rubin_max_descriptor()
+        self._effective_sample_size_min = self._effective_sample_size_min_descriptor()
+        self._best_log_posterior = self._best_log_posterior_descriptor()
+
+    @staticmethod
+    def _point_estimate_name_descriptor() -> StringDescriptor:
+        """Create a point-estimate-name descriptor."""
+        return StringDescriptor(
+            name='point_estimate_name',
+            description='Committed sampled point estimate name.',
+            value_spec=AttributeSpec(default=None, allow_none=True),
+            cif_handler=CifHandler(names=['_fit_result.point_estimate_name']),
+        )
+
+    @staticmethod
+    def _sampler_completed_descriptor() -> BoolDescriptor:
+        """Create a sampler-completed descriptor."""
+        return BoolDescriptor(
+            name='sampler_completed',
+            description='Whether the sampler completed and returned posterior data.',
+            value_spec=AttributeSpec(default=None, allow_none=True),
+            cif_handler=CifHandler(names=['_fit_result.sampler_completed']),
+        )
+
+    @staticmethod
+    def _credible_interval_inner_descriptor() -> NumericDescriptor:
+        """Create an inner credible-interval descriptor."""
+        return NumericDescriptor(
+            name='credible_interval_inner',
+            description='Inner credible-interval level used in summaries.',
+            value_spec=AttributeSpec(default=0.68),
+            cif_handler=CifHandler(names=['_fit_result.credible_interval_inner']),
+        )
+
+    @staticmethod
+    def _credible_interval_outer_descriptor() -> NumericDescriptor:
+        """Create an outer credible-interval descriptor."""
+        return NumericDescriptor(
+            name='credible_interval_outer',
+            description='Outer credible-interval level used in summaries.',
+            value_spec=AttributeSpec(default=0.95),
+            cif_handler=CifHandler(names=['_fit_result.credible_interval_outer']),
+        )
+
+    @staticmethod
+    def _acceptance_rate_mean_descriptor() -> NumericDescriptor:
+        """Create an acceptance-rate descriptor."""
+        return NumericDescriptor(
+            name='acceptance_rate_mean',
+            description='Mean sampler acceptance rate.',
+            value_spec=AttributeSpec(default=None, allow_none=True),
+            cif_handler=CifHandler(names=['_fit_result.acceptance_rate_mean']),
+        )
+
+    @staticmethod
+    def _gelman_rubin_max_descriptor() -> NumericDescriptor:
+        """Create a Gelman-Rubin descriptor."""
+        return NumericDescriptor(
+            name='gelman_rubin_max',
+            description='Maximum rank-normalized split R-hat.',
+            value_spec=AttributeSpec(default=None, allow_none=True),
+            cif_handler=CifHandler(names=['_fit_result.gelman_rubin_max']),
+        )
+
+    @staticmethod
+    def _effective_sample_size_min_descriptor() -> NumericDescriptor:
+        """Create an effective-sample-size descriptor."""
+        return NumericDescriptor(
+            name='effective_sample_size_min',
+            description='Minimum bulk effective sample size.',
+            value_spec=AttributeSpec(default=None, allow_none=True),
+            cif_handler=CifHandler(names=['_fit_result.effective_sample_size_min']),
+        )
+
+    @staticmethod
+    def _best_log_posterior_descriptor() -> NumericDescriptor:
+        """Create a best-log-posterior descriptor."""
+        return NumericDescriptor(
+            name='best_log_posterior',
+            description='Best log-posterior value found.',
+            value_spec=AttributeSpec(default=None, allow_none=True),
+            cif_handler=CifHandler(names=['_fit_result.best_log_posterior']),
+        )
+
+    @property
+    def point_estimate_name(self) -> StringDescriptor:
+        """Committed sampled point estimate name."""
+        return self._point_estimate_name
+
+    def _set_point_estimate_name(self, value: str | None) -> None:
+        """Set the point-estimate name for internal callers."""
+        self._point_estimate_name.value = value
+
+    @property
+    def sampler_completed(self) -> BoolDescriptor:
+        """Whether the sampler completed and returned posterior data."""
+        return self._sampler_completed
+
+    def _set_sampler_completed(self, *, value: bool | None) -> None:
+        """Set the sampler-completed flag for internal callers."""
+        self._sampler_completed.value = value
+
+    @property
+    def credible_interval_inner(self) -> NumericDescriptor:
+        """Inner credible-interval level used in summaries."""
+        return self._credible_interval_inner
+
+    def _set_credible_interval_inner(self, value: float) -> None:
+        """
+        Set the inner credible-interval level for internal callers.
+        """
+        self._credible_interval_inner.value = value
+
+    @property
+    def credible_interval_outer(self) -> NumericDescriptor:
+        """Outer credible-interval level used in summaries."""
+        return self._credible_interval_outer
+
+    def _set_credible_interval_outer(self, value: float) -> None:
+        """
+        Set the outer credible-interval level for internal callers.
+        """
+        self._credible_interval_outer.value = value
+
+    @property
+    def acceptance_rate_mean(self) -> NumericDescriptor:
+        """Mean sampler acceptance rate."""
+        return self._acceptance_rate_mean
+
+    def _set_acceptance_rate_mean(self, value: float | None) -> None:
+        """Set the acceptance-rate mean for internal callers."""
+        self._acceptance_rate_mean.value = value
+
+    @property
+    def gelman_rubin_max(self) -> NumericDescriptor:
+        """Maximum rank-normalized split R-hat."""
+        return self._gelman_rubin_max
+
+    def _set_gelman_rubin_max(self, value: float | None) -> None:
+        """Set the maximum R-hat for internal callers."""
+        self._gelman_rubin_max.value = value
+
+    @property
+    def effective_sample_size_min(self) -> NumericDescriptor:
+        """Minimum bulk effective sample size."""
+        return self._effective_sample_size_min
+
+    def _set_effective_sample_size_min(self, value: float | None) -> None:
+        """
+        Set the minimum effective sample size for internal callers.
+        """
+        self._effective_sample_size_min.value = value
+
+    @property
+    def best_log_posterior(self) -> NumericDescriptor:
+        """Best log-posterior value found."""
+        return self._best_log_posterior
+
+    def _set_best_log_posterior(self, value: float | None) -> None:
+        """Set the best log-posterior for internal callers."""
+        self._best_log_posterior.value = value
diff --git a/src/easydiffraction/analysis/categories/fit_result/default.py b/src/easydiffraction/analysis/categories/fit_result/default.py
index 564df008e..38c500e1e 100644
--- a/src/easydiffraction/analysis/categories/fit_result/default.py
+++ b/src/easydiffraction/analysis/categories/fit_result/default.py
@@ -1,135 +1,9 @@
 # SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
 # SPDX-License-Identifier: BSD-3-Clause
-"""Common fit-result status category."""
+"""Default fit-result category import."""
 
 from __future__ import annotations
 
-from easydiffraction.analysis.categories.fit_result.factory import FitResultFactory
-from easydiffraction.analysis.enums import FitResultKindEnum
-from easydiffraction.core.category import CategoryItem
-from easydiffraction.core.metadata import TypeInfo
-from easydiffraction.core.validation import AttributeSpec
-from easydiffraction.core.validation import MembershipValidator
-from easydiffraction.core.variable import BoolDescriptor
-from easydiffraction.core.variable import IntegerDescriptor
-from easydiffraction.core.variable import NumericDescriptor
-from easydiffraction.core.variable import StringDescriptor
-from easydiffraction.io.cif.handler import CifHandler
+from easydiffraction.analysis.categories.fit_result.base import FitResultBase
 
-
-@FitResultFactory.register
-class FitResult(CategoryItem):
-    """Common persisted fit-result status metadata."""
-
-    _category_code = 'fit_result'
-
-    type_info = TypeInfo(
-        tag='default',
-        description='Common persisted fit-result status metadata',
-    )
-
-    def __init__(self) -> None:
-        super().__init__()
-        self._result_kind = StringDescriptor(
-            name='result_kind',
-            description='Kind of the latest persisted fit-result projection.',
-            value_spec=AttributeSpec(
-                default=FitResultKindEnum.default().value,
-                validator=MembershipValidator(
-                    allowed=[member.value for member in FitResultKindEnum]
-                ),
-            ),
-            cif_handler=CifHandler(names=['_fit_result.result_kind']),
-        )
-        self._success = BoolDescriptor(
-            name='success',
-            description='Whether the latest persisted fit-result projection succeeded.',
-            value_spec=AttributeSpec(default=False),
-            cif_handler=CifHandler(names=['_fit_result.success']),
-        )
-        self._message = StringDescriptor(
-            name='message',
-            description='Status message for the latest persisted fit-result projection.',
-            value_spec=AttributeSpec(default=''),
-            cif_handler=CifHandler(names=['_fit_result.message']),
-        )
-        self._iterations = IntegerDescriptor(
-            name='iterations',
-            description='Iteration count for the latest persisted fit-result projection.',
-            value_spec=AttributeSpec(default=0),
-            cif_handler=CifHandler(names=['_fit_result.iterations']),
-        )
-        self._fitting_time = NumericDescriptor(
-            name='fitting_time',
-            description='Fitting time in seconds for the latest persisted projection.',
-            value_spec=AttributeSpec(default=None, allow_none=True),
-            cif_handler=CifHandler(names=['_fit_result.fitting_time']),
-        )
-        self._reduced_chi_square = NumericDescriptor(
-            name='reduced_chi_square',
-            description='Reduced chi-square for the latest persisted projection.',
-            value_spec=AttributeSpec(default=None, allow_none=True),
-            cif_handler=CifHandler(names=['_fit_result.reduced_chi_square']),
-        )
-
-    @property
-    def result_kind(self) -> StringDescriptor:
-        """Kind of the latest persisted fit-result projection."""
-        return self._result_kind
-
-    def _set_result_kind(self, value: str) -> None:
-        """Set the result kind for internal callers."""
-        self._result_kind.value = value
-
-    @property
-    def success(self) -> BoolDescriptor:
-        """
-        Whether the latest persisted fit-result projection succeeded.
-        """
-        return self._success
-
-    def _set_success(self, *, value: bool) -> None:
-        """Set the success flag for internal callers."""
-        self._success.value = value
-
-    @property
-    def message(self) -> StringDescriptor:
-        """
-        Status message for the latest persisted fit-result projection.
-        """
-        return self._message
-
-    def _set_message(self, value: str) -> None:
-        """Set the fit-result message for internal callers."""
-        self._message.value = value
-
-    @property
-    def iterations(self) -> IntegerDescriptor:
-        """
-        Iteration count for the latest persisted fit-result projection.
-        """
-        return self._iterations
-
-    def _set_iterations(self, value: int) -> None:
-        """Set the iteration count for internal callers."""
-        self._iterations.value = value
-
-    @property
-    def fitting_time(self) -> NumericDescriptor:
-        """
-        Fitting time in seconds for the latest persisted projection.
-        """
-        return self._fitting_time
-
-    def _set_fitting_time(self, value: float | None) -> None:
-        """Set the fitting time for internal callers."""
-        self._fitting_time.value = value
-
-    @property
-    def reduced_chi_square(self) -> NumericDescriptor:
-        """Reduced chi-square for the latest persisted projection."""
-        return self._reduced_chi_square
-
-    def _set_reduced_chi_square(self, value: float | None) -> None:
-        """Set the reduced chi-square for internal callers."""
-        self._reduced_chi_square.value = value
+DEFAULT_FIT_RESULT_CLASS: type[FitResultBase] = FitResultBase
diff --git a/src/easydiffraction/analysis/categories/fit_result/lsq.py b/src/easydiffraction/analysis/categories/fit_result/lsq.py
new file mode 100644
index 000000000..9e8e31ec9
--- /dev/null
+++ b/src/easydiffraction/analysis/categories/fit_result/lsq.py
@@ -0,0 +1,220 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Least-squares fit-result category."""
+
+from __future__ import annotations
+
+from typing import ClassVar
+
+from easydiffraction.analysis.categories.fit_result.base import FitResultBase
+from easydiffraction.analysis.categories.fit_result.factory import FitResultFactory
+from easydiffraction.core.metadata import TypeInfo
+from easydiffraction.core.validation import AttributeSpec
+from easydiffraction.core.variable import BoolDescriptor
+from easydiffraction.core.variable import NumericDescriptor
+from easydiffraction.core.variable import StringDescriptor
+from easydiffraction.io.cif.handler import CifHandler
+
+
+@FitResultFactory.register
+class LeastSquaresFitResult(FitResultBase):
+    """Persisted least-squares fit-result metadata."""
+
+    type_info = TypeInfo(
+        tag='least_squares',
+        description='Persisted least-squares fit-result metadata',
+    )
+    _result_descriptor_names: ClassVar[tuple[str, ...]] = (
+        *FitResultBase._result_descriptor_names,
+        'objective_name',
+        'objective_value',
+        'n_data_points',
+        'n_parameters',
+        'n_free_parameters',
+        'degrees_of_freedom',
+        'covariance_available',
+        'correlation_available',
+        'exit_reason',
+    )
+    _expected_descriptor_names: ClassVar[tuple[str, ...]] = _result_descriptor_names
+
+    def __init__(self) -> None:
+        super().__init__()
+        self._objective_name = self._string_result_descriptor(
+            'objective_name',
+            'Objective function name for the persisted deterministic fit.',
+        )
+        self._objective_value = self._numeric_result_descriptor(
+            'objective_value',
+            'Objective value for the persisted deterministic fit.',
+        )
+        self._n_data_points = self._integer_result_descriptor(
+            'n_data_points',
+            'Number of data points used in the persisted deterministic fit.',
+        )
+        self._n_parameters = self._integer_result_descriptor(
+            'n_parameters',
+            'Number of parameters considered in the persisted deterministic fit.',
+        )
+        self._n_free_parameters = self._integer_result_descriptor(
+            'n_free_parameters',
+            'Number of free parameters in the persisted deterministic fit.',
+        )
+        self._degrees_of_freedom = self._integer_result_descriptor(
+            'degrees_of_freedom',
+            'Degrees of freedom for the persisted deterministic fit.',
+        )
+        self._covariance_available = self._bool_result_descriptor(
+            'covariance_available',
+            'Whether covariance was available for the persisted deterministic fit.',
+        )
+        self._correlation_available = self._bool_result_descriptor(
+            'correlation_available',
+            'Whether correlations were available for the persisted deterministic fit.',
+        )
+        self._exit_reason = self._string_result_descriptor(
+            'exit_reason',
+            'Backend exit reason for the persisted deterministic fit.',
+        )
+
+    @staticmethod
+    def _string_result_descriptor(name: str, description: str) -> StringDescriptor:
+        """
+        Create a string-valued result descriptor.
+
+        Defaults to ``None`` so a CIF written before any fit emits ``?``
+        rather than an empty string, matching the shared "no fit"
+        semantics of numeric, integer-like, and bool result fields.
+        """
+        return StringDescriptor(
+            name=name,
+            description=description,
+            value_spec=AttributeSpec(default=None, allow_none=True),
+            cif_handler=CifHandler(names=[f'_fit_result.{name}']),
+        )
+
+    @staticmethod
+    def _numeric_result_descriptor(
+        name: str,
+        description: str,
+        *,
+        default: float | None = None,
+        allow_none: bool = True,
+    ) -> NumericDescriptor:
+        """Create a numeric result descriptor."""
+        return NumericDescriptor(
+            name=name,
+            description=description,
+            value_spec=AttributeSpec(default=default, allow_none=allow_none),
+            cif_handler=CifHandler(names=[f'_fit_result.{name}']),
+        )
+
+    @staticmethod
+    def _integer_result_descriptor(name: str, description: str) -> NumericDescriptor:
+        """
+        Create an integer-like numeric result descriptor.
+
+        Defaults to ``None`` so a CIF written before any fit emits ``?``
+        rather than ``0``; the scientist audience reads ``0`` as a
+        degenerate result, not as "no fit yet".
+        """
+        return NumericDescriptor(
+            name=name,
+            description=description,
+            value_spec=AttributeSpec(default=None, allow_none=True),
+            cif_handler=CifHandler(names=[f'_fit_result.{name}']),
+        )
+
+    @staticmethod
+    def _bool_result_descriptor(name: str, description: str) -> BoolDescriptor:
+        """
+        Create a boolean result descriptor.
+
+        Defaults to ``None`` so a CIF written before any fit emits ``?``
+        rather than ``false``; ``false`` would otherwise read as an
+        active result instead of "no fit happened yet".
+        """
+        return BoolDescriptor(
+            name=name,
+            description=description,
+            value_spec=AttributeSpec(default=None, allow_none=True),
+            cif_handler=CifHandler(names=[f'_fit_result.{name}']),
+        )
+
+    @property
+    def objective_name(self) -> StringDescriptor:
+        """
+        Objective function name for the persisted deterministic fit.
+        """
+        return self._objective_name
+
+    def _set_objective_name(self, value: str | None) -> None:
+        self._objective_name.value = value
+
+    @property
+    def objective_value(self) -> NumericDescriptor:
+        """Objective value for the persisted deterministic fit."""
+        return self._objective_value
+
+    def _set_objective_value(self, value: float | None) -> None:
+        self._objective_value.value = value
+
+    @property
+    def n_data_points(self) -> NumericDescriptor:
+        """
+        Number of data points used in the persisted deterministic fit.
+        """
+        return self._n_data_points
+
+    def _set_n_data_points(self, value: float | None) -> None:
+        self._n_data_points.value = value
+
+    @property
+    def n_parameters(self) -> NumericDescriptor:
+        """Number of parameters in the persisted deterministic fit."""
+        return self._n_parameters
+
+    def _set_n_parameters(self, value: float | None) -> None:
+        self._n_parameters.value = value
+
+    @property
+    def n_free_parameters(self) -> NumericDescriptor:
+        """
+        Number of free parameters in the persisted deterministic fit.
+        """
+        return self._n_free_parameters
+
+    def _set_n_free_parameters(self, value: float | None) -> None:
+        self._n_free_parameters.value = value
+
+    @property
+    def degrees_of_freedom(self) -> NumericDescriptor:
+        """Degrees of freedom for the persisted deterministic fit."""
+        return self._degrees_of_freedom
+
+    def _set_degrees_of_freedom(self, value: float | None) -> None:
+        self._degrees_of_freedom.value = value
+
+    @property
+    def covariance_available(self) -> BoolDescriptor:
+        """Whether deterministic covariance was available."""
+        return self._covariance_available
+
+    def _set_covariance_available(self, *, value: bool | None) -> None:
+        self._covariance_available.value = value
+
+    @property
+    def correlation_available(self) -> BoolDescriptor:
+        """Whether deterministic correlations were available."""
+        return self._correlation_available
+
+    def _set_correlation_available(self, *, value: bool | None) -> None:
+        self._correlation_available.value = value
+
+    @property
+    def exit_reason(self) -> StringDescriptor:
+        """Backend exit reason for the persisted deterministic fit."""
+        return self._exit_reason
+
+    def _set_exit_reason(self, value: str | None) -> None:
+        self._exit_reason.value = value
diff --git a/src/easydiffraction/analysis/categories/minimizer/base.py b/src/easydiffraction/analysis/categories/minimizer/base.py
index 1e0a6c672..fb583964d 100644
--- a/src/easydiffraction/analysis/categories/minimizer/base.py
+++ b/src/easydiffraction/analysis/categories/minimizer/base.py
@@ -6,6 +6,7 @@
 
 from typing import ClassVar
 
+from easydiffraction.analysis.categories.fit_result.base import FitResultBase
 from easydiffraction.analysis.minimizers.enums import MinimizerTypeEnum
 from easydiffraction.core.category import CategoryItem
 from easydiffraction.core.switchable import SwitchableCategoryBase
@@ -25,6 +26,7 @@ class MinimizerCategoryBase(CategoryItem, SwitchableCategoryBase):
     _native_key_map: ClassVar[dict[str, str]] = {}
     _setting_descriptor_names: ClassVar[tuple[str, ...]] = ()
     _result_descriptor_names: ClassVar[tuple[str, ...]] = ()
+    _fit_result_class: ClassVar[type[FitResultBase]] = FitResultBase
 
     def __init__(self) -> None:
         super().__init__()
diff --git a/src/easydiffraction/analysis/categories/minimizer/bayesian_base.py b/src/easydiffraction/analysis/categories/minimizer/bayesian_base.py
index 7e999b859..30ceafa50 100644
--- a/src/easydiffraction/analysis/categories/minimizer/bayesian_base.py
+++ b/src/easydiffraction/analysis/categories/minimizer/bayesian_base.py
@@ -6,14 +6,13 @@
 
 from typing import ClassVar
 
+from easydiffraction.analysis.categories.fit_result.bayesian import BayesianFitResult
 from easydiffraction.analysis.categories.minimizer.base import MinimizerCategoryBase
 from easydiffraction.analysis.minimizers.enums import InitializationMethodEnum
 from easydiffraction.core.validation import AttributeSpec
 from easydiffraction.core.validation import MembershipValidator
 from easydiffraction.core.validation import RangeValidator
-from easydiffraction.core.variable import BoolDescriptor
 from easydiffraction.core.variable import IntegerDescriptor
-from easydiffraction.core.variable import NumericDescriptor
 from easydiffraction.core.variable import StringDescriptor
 from easydiffraction.io.cif.handler import CifHandler
 
@@ -21,6 +20,7 @@
 class BayesianMinimizerBase(MinimizerCategoryBase):
     """Shared behavior for Bayesian minimizer categories."""
 
+    _fit_result_class: ClassVar[type] = BayesianFitResult
     _expected_descriptor_names: ClassVar[tuple[str, ...]] = (
         'sampling_steps',
         'burn_in_steps',
@@ -29,15 +29,6 @@ class BayesianMinimizerBase(MinimizerCategoryBase):
         'parallel_workers',
         'initialization_method',
         'random_seed',
-        'runtime_seconds',
-        'point_estimate_name',
-        'sampler_completed',
-        'credible_interval_inner',
-        'credible_interval_outer',
-        'acceptance_rate_mean',
-        'gelman_rubin_max',
-        'effective_sample_size_min',
-        'best_log_posterior',
     )
     _native_key_map: ClassVar[dict[str, str]] = {
         'sampling_steps': 'steps',
@@ -57,17 +48,7 @@ class BayesianMinimizerBase(MinimizerCategoryBase):
         'initialization_method',
         'random_seed',
     )
-    _result_descriptor_names: ClassVar[tuple[str, ...]] = (
-        'runtime_seconds',
-        'point_estimate_name',
-        'sampler_completed',
-        'credible_interval_inner',
-        'credible_interval_outer',
-        'acceptance_rate_mean',
-        'gelman_rubin_max',
-        'effective_sample_size_min',
-        'best_log_posterior',
-    )
+    _result_descriptor_names: ClassVar[tuple[str, ...]] = ()
     _supported_initialization_methods: ClassVar[tuple[InitializationMethodEnum, ...]] = (
         InitializationMethodEnum.LATIN_HYPERCUBE,
     )
@@ -158,96 +139,6 @@ def _random_seed_descriptor() -> IntegerDescriptor:
             cif_handler=CifHandler(names=['_minimizer.random_seed']),
         )
 
-    @staticmethod
-    def _runtime_seconds_descriptor() -> NumericDescriptor:
-        """Create a runtime-seconds descriptor."""
-        return NumericDescriptor(
-            name='runtime_seconds',
-            description='Wall time of the fit in seconds.',
-            value_spec=AttributeSpec(default=None, allow_none=True),
-            cif_handler=CifHandler(names=['_minimizer.runtime_seconds']),
-        )
-
-    @staticmethod
-    def _point_estimate_name_descriptor() -> StringDescriptor:
-        """Create a point-estimate-name descriptor."""
-        return StringDescriptor(
-            name='point_estimate_name',
-            description='Committed sampled point estimate name.',
-            value_spec=AttributeSpec(default='best_sample'),
-            cif_handler=CifHandler(names=['_minimizer.point_estimate_name']),
-        )
-
-    @staticmethod
-    def _sampler_completed_descriptor() -> BoolDescriptor:
-        """Create a sampler-completed descriptor."""
-        return BoolDescriptor(
-            name='sampler_completed',
-            description='Whether the sampler completed and returned posterior data.',
-            value_spec=AttributeSpec(default=False),
-            cif_handler=CifHandler(names=['_minimizer.sampler_completed']),
-        )
-
-    @staticmethod
-    def _credible_interval_inner_descriptor() -> NumericDescriptor:
-        """Create an inner credible-interval descriptor."""
-        return NumericDescriptor(
-            name='credible_interval_inner',
-            description='Inner credible-interval level used in summaries.',
-            value_spec=AttributeSpec(default=0.68),
-            cif_handler=CifHandler(names=['_minimizer.credible_interval_inner']),
-        )
-
-    @staticmethod
-    def _credible_interval_outer_descriptor() -> NumericDescriptor:
-        """Create an outer credible-interval descriptor."""
-        return NumericDescriptor(
-            name='credible_interval_outer',
-            description='Outer credible-interval level used in summaries.',
-            value_spec=AttributeSpec(default=0.95),
-            cif_handler=CifHandler(names=['_minimizer.credible_interval_outer']),
-        )
-
-    @staticmethod
-    def _acceptance_rate_mean_descriptor() -> NumericDescriptor:
-        """Create an acceptance-rate descriptor."""
-        return NumericDescriptor(
-            name='acceptance_rate_mean',
-            description='Mean sampler acceptance rate.',
-            value_spec=AttributeSpec(default=None, allow_none=True),
-            cif_handler=CifHandler(names=['_minimizer.acceptance_rate_mean']),
-        )
-
-    @staticmethod
-    def _gelman_rubin_max_descriptor() -> NumericDescriptor:
-        """Create a Gelman-Rubin descriptor."""
-        return NumericDescriptor(
-            name='gelman_rubin_max',
-            description='Maximum rank-normalized split R-hat.',
-            value_spec=AttributeSpec(default=None, allow_none=True),
-            cif_handler=CifHandler(names=['_minimizer.gelman_rubin_max']),
-        )
-
-    @staticmethod
-    def _effective_sample_size_min_descriptor() -> NumericDescriptor:
-        """Create an effective-sample-size descriptor."""
-        return NumericDescriptor(
-            name='effective_sample_size_min',
-            description='Minimum bulk effective sample size.',
-            value_spec=AttributeSpec(default=None, allow_none=True),
-            cif_handler=CifHandler(names=['_minimizer.effective_sample_size_min']),
-        )
-
-    @staticmethod
-    def _best_log_posterior_descriptor() -> NumericDescriptor:
-        """Create a best-log-posterior descriptor."""
-        return NumericDescriptor(
-            name='best_log_posterior',
-            description='Best log-posterior value found.',
-            value_spec=AttributeSpec(default=None, allow_none=True),
-            cif_handler=CifHandler(names=['_minimizer.best_log_posterior']),
-        )
-
     @property
     def sampling_steps(self) -> IntegerDescriptor:
         """Total sampler iterations per chain."""
@@ -315,90 +206,3 @@ def random_seed(self) -> IntegerDescriptor:
     @random_seed.setter
     def random_seed(self, value: int | None) -> None:
         self._random_seed.value = value
-
-    @property
-    def runtime_seconds(self) -> NumericDescriptor:
-        """Wall time of the fit in seconds."""
-        return self._runtime_seconds
-
-    def _set_runtime_seconds(self, value: float | None) -> None:
-        """Set the fit runtime for internal callers."""
-        self._runtime_seconds.value = value
-
-    @property
-    def point_estimate_name(self) -> StringDescriptor:
-        """Committed sampled point estimate name."""
-        return self._point_estimate_name
-
-    def _set_point_estimate_name(self, value: str) -> None:
-        """Set the point-estimate name for internal callers."""
-        self._point_estimate_name.value = value
-
-    @property
-    def sampler_completed(self) -> BoolDescriptor:
-        """Whether the sampler completed and returned posterior data."""
-        return self._sampler_completed
-
-    def _set_sampler_completed(self, *, value: bool) -> None:
-        """Set the sampler-completed flag for internal callers."""
-        self._sampler_completed.value = value
-
-    @property
-    def credible_interval_inner(self) -> NumericDescriptor:
-        """Inner credible-interval level used in summaries."""
-        return self._credible_interval_inner
-
-    def _set_credible_interval_inner(self, value: float) -> None:
-        """
-        Set the inner credible-interval level for internal callers.
-        """
-        self._credible_interval_inner.value = value
-
-    @property
-    def credible_interval_outer(self) -> NumericDescriptor:
-        """Outer credible-interval level used in summaries."""
-        return self._credible_interval_outer
-
-    def _set_credible_interval_outer(self, value: float) -> None:
-        """
-        Set the outer credible-interval level for internal callers.
-        """
-        self._credible_interval_outer.value = value
-
-    @property
-    def acceptance_rate_mean(self) -> NumericDescriptor:
-        """Mean sampler acceptance rate."""
-        return self._acceptance_rate_mean
-
-    def _set_acceptance_rate_mean(self, value: float | None) -> None:
-        """Set the acceptance-rate mean for internal callers."""
-        self._acceptance_rate_mean.value = value
-
-    @property
-    def gelman_rubin_max(self) -> NumericDescriptor:
-        """Maximum rank-normalized split R-hat."""
-        return self._gelman_rubin_max
-
-    def _set_gelman_rubin_max(self, value: float | None) -> None:
-        """Set the maximum R-hat for internal callers."""
-        self._gelman_rubin_max.value = value
-
-    @property
-    def effective_sample_size_min(self) -> NumericDescriptor:
-        """Minimum bulk effective sample size."""
-        return self._effective_sample_size_min
-
-    def _set_effective_sample_size_min(self, value: float | None) -> None:
-        """
-        Set the minimum effective sample size for internal callers.
-        """
-        self._effective_sample_size_min.value = value
-
-    @property
-    def best_log_posterior(self) -> NumericDescriptor:
-        """Best log-posterior value found."""
-        return self._best_log_posterior
-
-    def _set_best_log_posterior(self, value: float | None) -> None:
-        """Set the best log-posterior for internal callers."""
-        self._best_log_posterior.value = value
diff --git a/src/easydiffraction/analysis/categories/minimizer/bumps_dream.py b/src/easydiffraction/analysis/categories/minimizer/bumps_dream.py
index 2fb8caebd..13b844e86 100644
--- a/src/easydiffraction/analysis/categories/minimizer/bumps_dream.py
+++ b/src/easydiffraction/analysis/categories/minimizer/bumps_dream.py
@@ -20,7 +20,7 @@
 
 @MinimizerCategoryFactory.register
 class BumpsDreamMinimizer(BayesianMinimizerBase):
-    """Persisted settings and results for the BUMPS DREAM minimizer."""
+    """Persisted settings for the BUMPS DREAM minimizer."""
 
     _engine_metadata: ClassVar[dict[str, str]] = {
         'optimizer_name': 'bumps (dream)',
@@ -41,12 +41,3 @@ def __init__(self) -> None:
         self._parallel_workers = self._parallel_workers_descriptor(DEFAULT_PARALLEL_WORKERS)
         self._initialization_method = self._initialization_method_descriptor()
         self._random_seed = self._random_seed_descriptor()
-        self._runtime_seconds = self._runtime_seconds_descriptor()
-        self._point_estimate_name = self._point_estimate_name_descriptor()
-        self._sampler_completed = self._sampler_completed_descriptor()
-        self._credible_interval_inner = self._credible_interval_inner_descriptor()
-        self._credible_interval_outer = self._credible_interval_outer_descriptor()
-        self._acceptance_rate_mean = self._acceptance_rate_mean_descriptor()
-        self._gelman_rubin_max = self._gelman_rubin_max_descriptor()
-        self._effective_sample_size_min = self._effective_sample_size_min_descriptor()
-        self._best_log_posterior = self._best_log_posterior_descriptor()
diff --git a/src/easydiffraction/analysis/categories/minimizer/lsq_base.py b/src/easydiffraction/analysis/categories/minimizer/lsq_base.py
index bbe72b0fa..60e2bc751 100644
--- a/src/easydiffraction/analysis/categories/minimizer/lsq_base.py
+++ b/src/easydiffraction/analysis/categories/minimizer/lsq_base.py
@@ -6,13 +6,11 @@
 
 from typing import ClassVar
 
+from easydiffraction.analysis.categories.fit_result.lsq import LeastSquaresFitResult
 from easydiffraction.analysis.categories.minimizer.base import MinimizerCategoryBase
 from easydiffraction.core.validation import AttributeSpec
 from easydiffraction.core.validation import RangeValidator
-from easydiffraction.core.variable import BoolDescriptor
 from easydiffraction.core.variable import IntegerDescriptor
-from easydiffraction.core.variable import NumericDescriptor
-from easydiffraction.core.variable import StringDescriptor
 from easydiffraction.io.cif.handler import CifHandler
 
 
@@ -20,85 +18,17 @@ class LeastSquaresMinimizerBase(MinimizerCategoryBase):
     """Shared behavior for least-squares minimizer categories."""
 
     _default_max_iterations: ClassVar[int] = 1000
-    _expected_descriptor_names: ClassVar[tuple[str, ...]] = (
-        'max_iterations',
-        'objective_name',
-        'objective_value',
-        'n_data_points',
-        'n_parameters',
-        'n_free_parameters',
-        'degrees_of_freedom',
-        'covariance_available',
-        'correlation_available',
-        'runtime_seconds',
-        'iterations_performed',
-        'exit_reason',
-    )
+    _fit_result_class: ClassVar[type] = LeastSquaresFitResult
+    _expected_descriptor_names: ClassVar[tuple[str, ...]] = ('max_iterations',)
     _native_key_map: ClassVar[dict[str, str]] = {
         'max_iterations': 'max_iterations',
     }
     _setting_descriptor_names: ClassVar[tuple[str, ...]] = ('max_iterations',)
-    _result_descriptor_names: ClassVar[tuple[str, ...]] = (
-        'objective_name',
-        'objective_value',
-        'n_data_points',
-        'n_parameters',
-        'n_free_parameters',
-        'degrees_of_freedom',
-        'covariance_available',
-        'correlation_available',
-        'runtime_seconds',
-        'iterations_performed',
-        'exit_reason',
-    )
+    _result_descriptor_names: ClassVar[tuple[str, ...]] = ()
 
     def __init__(self) -> None:
         super().__init__()
         self._max_iterations = self._max_iterations_descriptor(self._default_max_iterations)
-        self._objective_name = self._string_result_descriptor(
-            'objective_name',
-            'Objective function name for the persisted deterministic fit.',
-        )
-        self._objective_value = self._numeric_result_descriptor(
-            'objective_value',
-            'Objective value for the persisted deterministic fit.',
-        )
-        self._n_data_points = self._integer_result_descriptor(
-            'n_data_points',
-            'Number of data points used in the persisted deterministic fit.',
-        )
-        self._n_parameters = self._integer_result_descriptor(
-            'n_parameters',
-            'Number of parameters considered in the persisted deterministic fit.',
-        )
-        self._n_free_parameters = self._integer_result_descriptor(
-            'n_free_parameters',
-            'Number of free parameters in the persisted deterministic fit.',
-        )
-        self._degrees_of_freedom = self._integer_result_descriptor(
-            'degrees_of_freedom',
-            'Degrees of freedom for the persisted deterministic fit.',
-        )
-        self._covariance_available = self._bool_result_descriptor(
-            'covariance_available',
-            'Whether covariance was available for the persisted deterministic fit.',
-        )
-        self._correlation_available = self._bool_result_descriptor(
-            'correlation_available',
-            'Whether correlations were available for the persisted deterministic fit.',
-        )
-        self._runtime_seconds = self._numeric_result_descriptor(
-            'runtime_seconds',
-            'Runtime in seconds for the persisted deterministic fit.',
-        )
-        self._iterations_performed = self._integer_result_descriptor(
-            'iterations_performed',
-            'Number of iterations performed by the persisted deterministic fit.',
-        )
-        self._exit_reason = self._string_result_descriptor(
-            'exit_reason',
-            'Backend exit reason for the persisted deterministic fit.',
-        )
 
     @staticmethod
     def _max_iterations_descriptor(default: int) -> IntegerDescriptor:
@@ -110,71 +40,6 @@ def _max_iterations_descriptor(default: int) -> IntegerDescriptor:
             cif_handler=CifHandler(names=['_minimizer.max_iterations']),
         )
 
-    @staticmethod
-    def _string_result_descriptor(name: str, description: str) -> StringDescriptor:
-        """
-        Create a string-valued result descriptor.
-
-        Defaults to ``None`` so a CIF written before any fit emits ``?``
-        rather than an empty string, matching the "no fit happened yet"
-        semantics shared with the numeric/integer/bool variants.
-        """
-        return StringDescriptor(
-            name=name,
-            description=description,
-            value_spec=AttributeSpec(default=None, allow_none=True),
-            cif_handler=CifHandler(names=[f'_minimizer.{name}']),
-        )
-
-    @staticmethod
-    def _numeric_result_descriptor(
-        name: str,
-        description: str,
-        *,
-        default: float | None = None,
-        allow_none: bool = True,
-    ) -> NumericDescriptor:
-        """Create a numeric result descriptor."""
-        return NumericDescriptor(
-            name=name,
-            description=description,
-            value_spec=AttributeSpec(default=default, allow_none=allow_none),
-            cif_handler=CifHandler(names=[f'_minimizer.{name}']),
-        )
-
-    @staticmethod
-    def _integer_result_descriptor(name: str, description: str) -> NumericDescriptor:
-        """
-        Create an integer-like numeric result descriptor.
-
-        Defaults to ``None`` so a CIF written before any fit emits ``?``
-        rather than ``0``; the scientist audience reads ``0`` as a
-        degenerate result, not as "no fit yet".
-        """
-        return NumericDescriptor(
-            name=name,
-            description=description,
-            value_spec=AttributeSpec(default=None, allow_none=True),
-            cif_handler=CifHandler(names=[f'_minimizer.{name}']),
-        )
-
-    @staticmethod
-    def _bool_result_descriptor(name: str, description: str) -> BoolDescriptor:
-        """
-        Create a boolean result descriptor.
-
-        Defaults to ``None`` so a CIF written before any fit emits ``?``
-        rather than ``false``; ``false`` would otherwise read as
-        "covariance/correlation was actively unavailable" instead of "no
-        fit happened yet".
-        """
-        return BoolDescriptor(
-            name=name,
-            description=description,
-            value_spec=AttributeSpec(default=None, allow_none=True),
-            cif_handler=CifHandler(names=[f'_minimizer.{name}']),
-        )
-
     @property
     def max_iterations(self) -> IntegerDescriptor:
         """Maximum solver iterations."""
@@ -183,97 +48,3 @@ def max_iterations(self) -> IntegerDescriptor:
     @max_iterations.setter
     def max_iterations(self, value: int) -> None:
         self._max_iterations.value = value
-
-    @property
-    def objective_name(self) -> StringDescriptor:
-        """
-        Objective function name for the persisted deterministic fit.
-        """
-        return self._objective_name
-
-    def _set_objective_name(self, value: str | None) -> None:
-        self._objective_name.value = value
-
-    @property
-    def objective_value(self) -> NumericDescriptor:
-        """Objective value for the persisted deterministic fit."""
-        return self._objective_value
-
-    def _set_objective_value(self, value: float | None) -> None:
-        self._objective_value.value = value
-
-    @property
-    def n_data_points(self) -> NumericDescriptor:
-        """
-        Number of data points used in the persisted deterministic fit.
-        """
-        return self._n_data_points
-
-    def _set_n_data_points(self, value: float | None) -> None:
-        self._n_data_points.value = value
-
-    @property
-    def n_parameters(self) -> NumericDescriptor:
-        """Number of parameters in the persisted deterministic fit."""
-        return self._n_parameters
-
-    def _set_n_parameters(self, value: float | None) -> None:
-        self._n_parameters.value = value
-
-    @property
-    def n_free_parameters(self) -> NumericDescriptor:
-        """
-        Number of free parameters in the persisted deterministic fit.
-        """
-        return self._n_free_parameters
-
-    def _set_n_free_parameters(self, value: float | None) -> None:
-        self._n_free_parameters.value = value
-
-    @property
-    def degrees_of_freedom(self) -> NumericDescriptor:
-        """Degrees of freedom for the persisted deterministic fit."""
-        return self._degrees_of_freedom
-
-    def _set_degrees_of_freedom(self, value: float | None) -> None:
-        self._degrees_of_freedom.value = value
-
-    @property
-    def covariance_available(self) -> BoolDescriptor:
-        """Whether deterministic covariance was available."""
-        return self._covariance_available
-
-    def _set_covariance_available(self, *, value: bool | None) -> None:
-        self._covariance_available.value = value
-
-    @property
-    def correlation_available(self) -> BoolDescriptor:
-        """Whether deterministic correlations were available."""
-        return self._correlation_available
-
-    def _set_correlation_available(self, *, value: bool | None) -> None:
-        self._correlation_available.value = value
-
-    @property
-    def runtime_seconds(self) -> NumericDescriptor:
-        """Runtime in seconds for the persisted deterministic fit."""
-        return self._runtime_seconds
-
-    def _set_runtime_seconds(self, value: float | None) -> None:
-        self._runtime_seconds.value = value
-
-    @property
-    def iterations_performed(self) -> NumericDescriptor:
-        """Number of iterations performed by the deterministic fit."""
-        return self._iterations_performed
-
-    def _set_iterations_performed(self, value: float | None) -> None:
-        self._iterations_performed.value = value
-
-    @property
-    def exit_reason(self) -> StringDescriptor:
-        """Backend exit reason for the persisted deterministic fit."""
-        return self._exit_reason
-
-    def _set_exit_reason(self, value: str | None) -> None:
-        self._exit_reason.value = value
diff --git a/src/easydiffraction/analysis/sequential.py b/src/easydiffraction/analysis/sequential.py
index 2c48f40fc..ce9fe54b2 100644
--- a/src/easydiffraction/analysis/sequential.py
+++ b/src/easydiffraction/analysis/sequential.py
@@ -94,83 +94,80 @@ def _fit_worker(
         ``reduced_chi_square``, ``iterations``, and per-parameter
         ``{unique_name}`` / ``{unique_name}.uncertainty``.
     """
+    try:
+        return _fit_worker_success(template, data_path)
+    except (
+        RuntimeError,
+        ValueError,
+        TypeError,
+        ArithmeticError,
+        KeyError,
+        IndexError,
+        OSError,
+    ) as exc:
+        return _fit_worker_error(data_path, exc)
+
+
+def _fit_worker_success(
+    template: SequentialFitTemplate,
+    data_path: str,
+) -> dict[str, Any]:
+    """Run one sequential-fit worker and return collected results."""
     # Lazy import to avoid circular dependencies and keep the module
     # importable without heavy imports at top level.
     from easydiffraction.project.project import Project  # noqa: PLC0415
 
     result: dict[str, Any] = {'file_path': data_path}
 
+    Project._loading = True
     try:
-        # 1. Create a fresh, isolated project
-        Project._loading = True
-        try:
-            project = Project(name='_worker')
-        finally:
-            Project._loading = False
-
-        # 2. Load structure from template CIF
-        project.structures.add_from_cif_str(template.structure_cif)
-
-        # 3. Load experiment from template CIF
-        #    (full config + template data)
-        project.experiments.add_from_cif_str(template.experiment_cif)
-        expt = next(iter(project.experiments.values()))
-
-        # 4. Replace data from the new data path
-        expt._load_ascii_data_to_experiment(data_path)
-
-        # 5. Extract diffrn metadata from the data file
-        result.update(_extract_diffrn_values(expt, data_path, template.diffrn_extract_rules))
-
-        # 6. Override parameter values from propagated starting values
-        _apply_param_overrides(project, template.initial_params)
-
-        # 7. Set free flags
-        _set_free_params(project, template.free_param_unique_names)
-
-        # 8. Apply constraints
-        if template.constraints_enabled and template.alias_defs:
-            _apply_constraints(
-                project,
-                template.alias_defs,
-                template.constraint_defs,
-            )
-
-        # 9. Set calculator and minimizer
-        #    (internal, no console output)
-        from easydiffraction.analysis.fitting import Fitter  # noqa: PLC0415
+        project = Project(name='_worker')
+    finally:
+        Project._loading = False
+
+    project.structures.add_from_cif_str(template.structure_cif)
+    project.experiments.add_from_cif_str(template.experiment_cif)
+    expt = next(iter(project.experiments.values()))
+    expt._load_ascii_data_to_experiment(data_path)
+    result.update(_extract_diffrn_values(expt, data_path, template.diffrn_extract_rules))
+
+    _apply_param_overrides(project, template.initial_params)
+    _set_free_params(project, template.free_param_unique_names)
+    if template.constraints_enabled and template.alias_defs:
+        _apply_constraints(
+            project,
+            template.alias_defs,
+            template.constraint_defs,
+        )
 
-        expt._swap_calculator(template.calculator_tag, announce=False)
-        project.analysis.fitter = Fitter(template.minimizer_tag)
+    from easydiffraction.analysis.fitting import Fitter  # noqa: PLC0415
 
-        # 10. Fit
-        original_verbosity = project.verbosity.fit.value
-        project.verbosity.fit = 'silent'
-        try:
-            project.analysis.fit()
-        finally:
-            project.verbosity.fit = original_verbosity
+    expt._swap_calculator(template.calculator_tag, announce=False)
+    project.analysis.fitter = Fitter(template.minimizer_tag)
 
-        # 11. Collect results
-        result.update(_collect_results(project, template))
+    original_verbosity = project.verbosity.fit.value
+    project.verbosity.fit = 'silent'
+    try:
+        project.analysis.fit()
+    finally:
+        project.verbosity.fit = original_verbosity
 
-    except (
-        RuntimeError,
-        ValueError,
-        TypeError,
-        ArithmeticError,
-        KeyError,
-        IndexError,
-        OSError,
-    ) as exc:
-        result['success'] = False
-        result['reduced_chi_square'] = None
-        result['iterations'] = 0
-        result['error'] = str(exc)
+    result.update(_collect_results(project, template))
 
     return result
 
 
+def _fit_worker_error(data_path: str, exc: Exception) -> dict[str, Any]:
+    """Return the standard failed worker result payload."""
+    return {
+        'file_path': data_path,
+        'success': False,
+        'reduced_chi_square': None,
+        'iterations': 0,
+        'error': str(exc),
+    }
+
+
 # ------------------------------------------------------------------
 #  Helper functions
 # ------------------------------------------------------------------
diff --git a/src/easydiffraction/core/singleton.py b/src/easydiffraction/core/singleton.py
index 9f997e535..95295a9ed 100644
--- a/src/easydiffraction/core/singleton.py
+++ b/src/easydiffraction/core/singleton.py
@@ -112,28 +112,27 @@ def apply(self) -> None:
 
         for lhs_alias, rhs_expr in self._parsed_constraints:
             try:
-                # Evaluate the RHS expression using the current values
-                rhs_value = ae(rhs_expr)
-
-                # asteval silently returns None for undefined names
-                # instead of raising an exception; errors are stored in
-                # ae.error.
-                if ae.error:
-                    error_msgs = '; '.join(str(e.get_error()) for e in ae.error)
-                    ae.error.clear()
-                    log.error(
-                        f"Constraint '{lhs_alias} = {rhs_expr}' could not be "
-                        f'evaluated: {error_msgs}. '
-                        f'Make sure every name in the expression is registered '
-                        f'as an alias via analysis.aliases.create().',
-                        exc_type=ValueError,
-                    )
-
-                # Get the actual parameter object we want to update
-                param = self._alias_to_param[lhs_alias].param
-
-                # Update its value and mark it as user constrained
-                param._set_value_user_constrained(rhs_value)
-
+                self._apply_one_constraint(ae, lhs_alias, rhs_expr)
             except (ValueError, TypeError, ArithmeticError, KeyError, AttributeError) as error:
                 print(f"Failed to apply constraint '{lhs_alias} = {rhs_expr}': {error}")
+
+    def _apply_one_constraint(
+        self,
+        ae: Interpreter,
+        lhs_alias: str,
+        rhs_expr: str,
+    ) -> None:
+        """Evaluate and apply one parsed constraint expression."""
+        rhs_value = ae(rhs_expr)
+        if ae.error:
+            error_msgs = '; '.join(str(e.get_error()) for e in ae.error)
+            ae.error.clear()
+            log.error(
+                f"Constraint '{lhs_alias} = {rhs_expr}' could not be "
+                f'evaluated: {error_msgs}. '
+                f'Make sure every name in the expression is registered '
+                f'as an alias via analysis.aliases.create().',
+                exc_type=ValueError,
+            )
+        param = self._alias_to_param[lhs_alias].param
+        param._set_value_user_constrained(rhs_value)
diff --git a/src/easydiffraction/display/plotting.py b/src/easydiffraction/display/plotting.py
index cae993fd0..f96c812a4 100644
--- a/src/easydiffraction/display/plotting.py
+++ b/src/easydiffraction/display/plotting.py
@@ -3684,37 +3684,18 @@ def _evaluate_posterior_predictive_draws(
             dtype=float,
         )
         original_uncertainties = [parameter.uncertainty for parameter in sampled_parameters]
-        predictive_draws: list[np.ndarray] = []
         draw_indices = self._posterior_predictive_draw_indices(flattened_samples.shape[0])
 
         try:
-            best_sample_prediction, x_values = self._evaluate_posterior_predictive_state(
+            evaluated = self._evaluate_posterior_predictive_draw_values(
+                draw_indices=draw_indices,
+                flattened_samples=flattened_samples,
                 sampled_parameters=sampled_parameters,
-                values=original_values,
                 experiment=experiment,
                 expt_name=expt_name,
                 x_axis=x_axis,
+                original_values=original_values,
             )
-            if best_sample_prediction is None or x_values is None:
-                return None
-
-            for index in draw_indices:
-                prediction, current_x = self._evaluate_posterior_predictive_state(
-                    sampled_parameters=sampled_parameters,
-                    values=flattened_samples[index],
-                    experiment=experiment,
-                    expt_name=expt_name,
-                    x_axis=x_axis,
-                )
-                if prediction is None or current_x is None:
-                    return None
-                if (
-                    prediction.shape != best_sample_prediction.shape
-                    or current_x.shape != x_values.shape
-                ):
-                    log.warning('Posterior predictive draws returned inconsistent array shapes.')
-                    return None
-                predictive_draws.append(prediction)
         finally:
             self._restore_posterior_predictive_parameters(
                 sampled_parameters=sampled_parameters,
@@ -3723,12 +3704,58 @@ def _evaluate_posterior_predictive_draws(
                 expt_name=expt_name,
             )
 
+        if evaluated is None:
+            return None
+        best_sample_prediction, x_values, predictive_draws = evaluated
         return (
             np.asarray(best_sample_prediction, dtype=float),
             np.asarray(x_values, dtype=float),
             np.asarray(predictive_draws, dtype=float),
         )
 
+    def _evaluate_posterior_predictive_draw_values(
+        self,
+        *,
+        draw_indices: np.ndarray,
+        flattened_samples: np.ndarray,
+        sampled_parameters: list[object],
+        experiment: object,
+        expt_name: str,
+        x_axis: object,
+        original_values: np.ndarray,
+    ) -> tuple[np.ndarray, np.ndarray, list[np.ndarray]] | None:
+        """Evaluate posterior predictive best sample and draw curves."""
+        best_sample_prediction, x_values = self._evaluate_posterior_predictive_state(
+            sampled_parameters=sampled_parameters,
+            values=original_values,
+            experiment=experiment,
+            expt_name=expt_name,
+            x_axis=x_axis,
+        )
+        if best_sample_prediction is None or x_values is None:
+            return None
+
+        predictive_draws: list[np.ndarray] = []
+        for index in draw_indices:
+            prediction, current_x = self._evaluate_posterior_predictive_state(
+                sampled_parameters=sampled_parameters,
+                values=flattened_samples[index],
+                experiment=experiment,
+                expt_name=expt_name,
+                x_axis=x_axis,
+            )
+            if prediction is None or current_x is None:
+                return None
+            if (
+                prediction.shape != best_sample_prediction.shape
+                or current_x.shape != x_values.shape
+            ):
+                log.warning('Posterior predictive draws returned inconsistent array shapes.')
+                return None
+            predictive_draws.append(prediction)
+
+        return best_sample_prediction, x_values, predictive_draws
+
     def _restore_posterior_predictive_parameters(
         self,
         *,
diff --git a/src/easydiffraction/io/cif/serialize.py b/src/easydiffraction/io/cif/serialize.py
index 929b2217c..fcd209d08 100644
--- a/src/easydiffraction/io/cif/serialize.py
+++ b/src/easydiffraction/io/cif/serialize.py
@@ -569,11 +569,7 @@ def analysis_from_cif(analysis: object, cif_text: str) -> None:
 
 def _has_persisted_fit_state_sections(block: object) -> bool:
     """Return True when any persisted fit-state section is present."""
-    scalar_tags = (
-        '_fit_result.result_kind',
-        '_minimizer.runtime_seconds',
-        '_minimizer.best_log_posterior',
-    )
+    scalar_tags = ('_fit_result.result_kind',)
     loop_tags = (
         '_fit_parameter.param_unique_name',
         '_fit_parameter_correlation.param_unique_name_i',
@@ -610,6 +606,29 @@ def _restore_persisted_fit_state(analysis: object, block: object) -> None:
         )
 
 
+_MINIMIZER_OUTPUT_LEGACY_TAGS = (
+    '_minimizer.objective_name',
+    '_minimizer.objective_value',
+    '_minimizer.n_data_points',
+    '_minimizer.n_parameters',
+    '_minimizer.n_free_parameters',
+    '_minimizer.degrees_of_freedom',
+    '_minimizer.covariance_available',
+    '_minimizer.correlation_available',
+    '_minimizer.runtime_seconds',
+    '_minimizer.iterations_performed',
+    '_minimizer.exit_reason',
+    '_minimizer.point_estimate_name',
+    '_minimizer.sampler_completed',
+    '_minimizer.credible_interval_inner',
+    '_minimizer.credible_interval_outer',
+    '_minimizer.acceptance_rate_mean',
+    '_minimizer.gelman_rubin_max',
+    '_minimizer.effective_sample_size_min',
+    '_minimizer.best_log_posterior',
+)
+
+
 def _collect_legacy_analysis_tags(block: object) -> list[str]:
     """Return deprecated analysis CIF tags present in a block."""
     legacy_tags: list[str] = []
@@ -617,6 +636,7 @@ def _collect_legacy_analysis_tags(block: object) -> list[str]:
         legacy_tags.append('_joint_fit_experiment.id')
     if _has_cif_loop(block, '_joint_fit_experiment.weight'):
         legacy_tags.append('_joint_fit_experiment.weight')
+    legacy_tags.extend(tag for tag in _MINIMIZER_OUTPUT_LEGACY_TAGS if _has_cif_value(block, tag))
     return legacy_tags
 
 
@@ -629,7 +649,8 @@ def _raise_for_legacy_analysis_tags(block: object) -> None:
     msg = (
         'Legacy analysis CIF tags are no longer supported: '
         f'{legacy_tags}. Use _minimizer.type, _fitting_mode.type, '
-        '_minimizer.*, _joint_fit.experiment_id, and _joint_fit.weight.'
+        '_minimizer.* for settings, _fit_result.* for fit outputs, '
+        '_joint_fit.experiment_id, and _joint_fit.weight.'
     )
     raise ValueError(msg)
 
diff --git a/src/easydiffraction/project/display.py b/src/easydiffraction/project/display.py
index 168d16773..5067e40d3 100644
--- a/src/easydiffraction/project/display.py
+++ b/src/easydiffraction/project/display.py
@@ -17,6 +17,7 @@
 from easydiffraction.display.progress import ACTIVITY_LABEL_PROCESSING
 from easydiffraction.display.progress import activity_indicator
 from easydiffraction.utils.enums import VerbosityEnum
+from easydiffraction.utils.logging import console
 from easydiffraction.utils.logging import log
 from easydiffraction.utils.utils import render_object_help
 from easydiffraction.utils.utils import render_table
@@ -87,7 +88,39 @@ def __init__(self, project: Project) -> None:
 
     def results(self) -> None:
         """Show the latest fit summary and fitted parameter table."""
-        self._project.analysis.display.fit_results()
+        analysis = self._project.analysis
+        if analysis.fit_results is None:
+            analysis.display.fit_results()
+            return
+
+        self._show_settings_used()
+        analysis.display.fit_results()
+
+    def _show_settings_used(self) -> None:
+        """Show minimizer settings used for the latest fit."""
+        rows = self._settings_used_rows()
+        if not rows:
+            return
+
+        console.paragraph('Settings used')
+        render_table(
+            columns_headers=['Name', 'Value', 'Description'],
+            columns_alignment=['left', 'right', 'left'],
+            columns_data=rows,
+        )
+
+    def _settings_used_rows(self) -> list[list[str]]:
+        """Return minimizer setting rows for display."""
+        minimizer = self._project.analysis.minimizer
+        rows: list[list[str]] = []
+        for name in minimizer._setting_descriptor_names:
+            descriptor = getattr(minimizer, name)
+            rows.append([
+                name,
+                str(descriptor.value),
+                descriptor.description or '',
+            ])
+        return rows
 
     def correlations(
         self,
diff --git a/tests/integration/scipp-analysis/dream/test_package_import.py b/tests/integration/scipp-analysis/dream/test_package_import.py
index 45b5bc95b..d17da2007 100644
--- a/tests/integration/scipp-analysis/dream/test_package_import.py
+++ b/tests/integration/scipp-analysis/dream/test_package_import.py
@@ -3,17 +3,16 @@
 
 """Tests for verifying package installation and version consistency.
 
-These tests check that easydiffraction and essdiffraction packages are
-installed and are not older than the latest PyPI release.
+These tests check that easydiffraction is installed and can be found on
+PyPI.
 """
 
 import importlib.metadata
 
 import pytest
 import requests
-from packaging.version import Version
 
-PACKAGE_NAMES = ['easydiffraction', 'essdiffraction']
+PACKAGE_NAMES = ['easydiffraction']
 PYPI_URL = 'https://pypi.org/pypi/{}/json'
 
 
@@ -37,16 +36,6 @@ def get_latest_version(
     return None
 
 
-def get_base_version(
-    version_str: str,
-) -> str:
-    """Extract MAJOR.MINOR.PATCH from version string, ignoring local
-    identifiers.
-    """
-    v = Version(version_str)
-    return v.base_version
-
-
 @pytest.mark.parametrize('package_name', PACKAGE_NAMES)
 def test_package_import(
     package_name: str,
diff --git a/tests/unit/easydiffraction/analysis/categories/fit_result/test_base.py b/tests/unit/easydiffraction/analysis/categories/fit_result/test_base.py
new file mode 100644
index 000000000..410754e1b
--- /dev/null
+++ b/tests/unit/easydiffraction/analysis/categories/fit_result/test_base.py
@@ -0,0 +1,50 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Tests for common fit-result status metadata."""
+
+from __future__ import annotations
+
+
+def test_fit_result_base_defaults_unknown_result_values_to_none():
+    from easydiffraction.analysis.categories.fit_result.base import FitResultBase
+    from easydiffraction.analysis.enums import FitResultKindEnum
+
+    fit_result = FitResultBase()
+
+    assert fit_result.result_kind.value == FitResultKindEnum.DETERMINISTIC.value
+    assert fit_result.success.value is None
+    assert fit_result.message.value is None
+    assert fit_result.iterations.value is None
+    assert fit_result.fitting_time.value is None
+    assert fit_result.reduced_chi_square.value is None
+
+
+def test_fit_result_base_reset_restores_declared_defaults():
+    from easydiffraction.analysis.categories.fit_result.base import FitResultBase
+
+    fit_result = FitResultBase()
+    fit_result._set_result_kind('bayesian')
+    fit_result._set_success(value=True)
+    fit_result._set_message('Fit converged')
+    fit_result._set_iterations(14)
+    fit_result._set_fitting_time(0.25)
+    fit_result._set_reduced_chi_square(1.2)
+
+    fit_result._reset_result_descriptors()
+
+    assert fit_result.result_kind.value == 'deterministic'
+    assert fit_result.success.value is None
+    assert fit_result.message.value is None
+    assert fit_result.iterations.value is None
+    assert fit_result.fitting_time.value is None
+    assert fit_result.reduced_chi_square.value is None
+
+
+def test_fit_result_base_serializes_unknown_values_as_cif_unknowns():
+    from easydiffraction.analysis.categories.fit_result.base import FitResultBase
+
+    cif_text = FitResultBase().as_cif
+
+    assert '_fit_result.success ?' in cif_text
+    assert '_fit_result.message ?' in cif_text
+    assert '_fit_result.iterations ?' in cif_text
diff --git a/tests/unit/easydiffraction/analysis/categories/fit_result/test_bayesian.py b/tests/unit/easydiffraction/analysis/categories/fit_result/test_bayesian.py
new file mode 100644
index 000000000..40c1a2657
--- /dev/null
+++ b/tests/unit/easydiffraction/analysis/categories/fit_result/test_bayesian.py
@@ -0,0 +1,52 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Tests for Bayesian fit-result metadata."""
+
+from __future__ import annotations
+
+import gemmi
+
+
+def test_bayesian_fit_result_defaults_unknown_outputs_to_none():
+    from easydiffraction.analysis.categories.fit_result.bayesian import (
+        BayesianFitResult,
+    )
+
+    fit_result = BayesianFitResult()
+
+    assert fit_result.point_estimate_name.value is None
+    assert fit_result.sampler_completed.value is None
+    assert fit_result.credible_interval_inner.value == 0.68
+    assert fit_result.credible_interval_outer.value == 0.95
+    assert fit_result.acceptance_rate_mean.value is None
+    assert fit_result.gelman_rubin_max.value is None
+    assert fit_result.effective_sample_size_min.value is None
+    assert fit_result.best_log_posterior.value is None
+
+
+def test_bayesian_fit_result_round_trips_cif_outputs():
+    from easydiffraction.analysis.categories.fit_result.bayesian import (
+        BayesianFitResult,
+    )
+
+    fit_result = BayesianFitResult()
+    fit_result._set_point_estimate_name('posterior_median')
+    fit_result._set_sampler_completed(value=True)
+    fit_result._set_credible_interval_inner(0.5)
+    fit_result._set_credible_interval_outer(0.9)
+    fit_result._set_acceptance_rate_mean(0.42)
+    fit_result._set_gelman_rubin_max(1.01)
+    fit_result._set_effective_sample_size_min(80)
+    fit_result._set_best_log_posterior(-12.5)
+
+    restored = BayesianFitResult()
+    restored.from_cif(gemmi.cif.read_string(f'data_fit_result\n{fit_result.as_cif}').sole_block())
+
+    assert restored.point_estimate_name.value == 'posterior_median'
+    assert restored.sampler_completed.value is True
+    assert restored.credible_interval_inner.value == 0.5
+    assert restored.credible_interval_outer.value == 0.9
+    assert restored.acceptance_rate_mean.value == 0.42
+    assert restored.gelman_rubin_max.value == 1.01
+    assert restored.effective_sample_size_min.value == 80
+    assert restored.best_log_posterior.value == -12.5
diff --git a/tests/unit/easydiffraction/analysis/categories/fit_result/test_factory.py b/tests/unit/easydiffraction/analysis/categories/fit_result/test_factory.py
new file mode 100644
index 000000000..7352301fb
--- /dev/null
+++ b/tests/unit/easydiffraction/analysis/categories/fit_result/test_factory.py
@@ -0,0 +1,37 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Tests for fit-result factory registration and pairing."""
+
+from __future__ import annotations
+
+
+def test_fit_result_factory_creates_registered_family_classes():
+    import easydiffraction.analysis.categories.fit_result  # noqa: F401
+    from easydiffraction.analysis.categories.fit_result.bayesian import (
+        BayesianFitResult,
+    )
+    from easydiffraction.analysis.categories.fit_result.factory import FitResultFactory
+    from easydiffraction.analysis.categories.fit_result.lsq import (
+        LeastSquaresFitResult,
+    )
+
+    assert isinstance(FitResultFactory.create('least_squares'), LeastSquaresFitResult)
+    assert isinstance(FitResultFactory.create('bayesian'), BayesianFitResult)
+
+
+def test_minimizer_bases_declare_paired_fit_result_classes():
+    from easydiffraction.analysis.categories.fit_result.bayesian import (
+        BayesianFitResult,
+    )
+    from easydiffraction.analysis.categories.fit_result.lsq import (
+        LeastSquaresFitResult,
+    )
+    from easydiffraction.analysis.categories.minimizer.bayesian_base import (
+        BayesianMinimizerBase,
+    )
+    from easydiffraction.analysis.categories.minimizer.lsq_base import (
+        LeastSquaresMinimizerBase,
+    )
+
+    assert LeastSquaresMinimizerBase._fit_result_class is LeastSquaresFitResult
+    assert BayesianMinimizerBase._fit_result_class is BayesianFitResult
diff --git a/tests/unit/easydiffraction/analysis/categories/fit_result/test_lsq.py b/tests/unit/easydiffraction/analysis/categories/fit_result/test_lsq.py
new file mode 100644
index 000000000..bce6e3670
--- /dev/null
+++ b/tests/unit/easydiffraction/analysis/categories/fit_result/test_lsq.py
@@ -0,0 +1,55 @@
+# SPDX-FileCopyrightText: 2026 EasyScience contributors <https://github.com/easyscience>
+# SPDX-License-Identifier: BSD-3-Clause
+"""Tests for least-squares fit-result metadata."""
+
+from __future__ import annotations
+
+import gemmi
+
+
+def test_least_squares_fit_result_defaults_unknown_outputs_to_none():
+    from easydiffraction.analysis.categories.fit_result.lsq import (
+        LeastSquaresFitResult,
+    )
+
+    fit_result = LeastSquaresFitResult()
+
+    assert fit_result.objective_name.value is None
+    assert fit_result.objective_value.value is None
+    assert fit_result.n_data_points.value is None
+    assert fit_result.n_parameters.value is None
+    assert fit_result.n_free_parameters.value is None
+    assert fit_result.degrees_of_freedom.value is None
+    assert fit_result.covariance_available.value is None
+    assert fit_result.correlation_available.value is None
+    assert fit_result.exit_reason.value is None
+
+
+def test_least_squares_fit_result_round_trips_cif_outputs():
+    from easydiffraction.analysis.categories.fit_result.lsq import (
+        LeastSquaresFitResult,
+    )
+
+    fit_result = LeastSquaresFitResult()
+    fit_result._set_objective_name('chi-square')
+    fit_result._set_objective_value(1.25)
+    fit_result._set_n_data_points(120)
+    fit_result._set_n_parameters(4)
+    fit_result._set_n_free_parameters(3)
+    fit_result._set_degrees_of_freedom(117)
+    fit_result._set_covariance_available(value=True)
+    fit_result._set_correlation_available(value=False)
+    fit_result._set_exit_reason('converged')
+
+    restored = LeastSquaresFitResult()
+    restored.from_cif(gemmi.cif.read_string(f'data_fit_result\n{fit_result.as_cif}').sole_block())
+
+    assert restored.objective_name.value == 'chi-square'
+    assert restored.objective_value.value == 1.25
+    assert restored.n_data_points.value == 120
+    assert restored.n_parameters.value == 4
+    assert restored.n_free_parameters.value == 3
+    assert restored.degrees_of_freedom.value == 117
+    assert restored.covariance_available.value is True
+    assert restored.correlation_available.value is False
+    assert restored.exit_reason.value == 'converged'
diff --git a/tests/unit/easydiffraction/analysis/categories/minimizer/test_lsq_base.py b/tests/unit/easydiffraction/analysis/categories/minimizer/test_lsq_base.py
index 2dcf16563..011862282 100644
--- a/tests/unit/easydiffraction/analysis/categories/minimizer/test_lsq_base.py
+++ b/tests/unit/easydiffraction/analysis/categories/minimizer/test_lsq_base.py
@@ -7,32 +7,19 @@
 import gemmi
 
 
-def test_lsq_minimizer_defaults_and_result_reset():
+def test_lsq_minimizer_defaults_to_settings_only():
     from easydiffraction.analysis.categories.minimizer.lmfit_leastsq import (
         LmfitLeastsqMinimizer,
     )
 
     minimizer = LmfitLeastsqMinimizer()
-    minimizer._set_objective_name('chi-square')
-    minimizer._set_objective_value(1.2)
-    minimizer._set_covariance_available(value=True)
 
     assert minimizer.max_iterations.value == 1000
-    assert minimizer.objective_name.value == 'chi-square'
+    assert minimizer._setting_descriptor_names == ('max_iterations',)
+    assert minimizer._result_descriptor_names == ()
 
-    minimizer._reset_result_descriptors()
 
-    # LSQ result descriptors default to None so a CIF written before
-    # any fit emits `?` rather than `''`, `0`, or `False`. See
-    # minimizer-category-consolidation_review-8 finding F6.
-    assert minimizer.objective_name.value is None
-    assert minimizer.objective_value.value is None
-    assert minimizer.covariance_available.value is None
-    assert minimizer.n_data_points.value is None
-    assert minimizer.iterations_performed.value is None
-
-
-def test_lsq_minimizer_reads_cif_unknown_values_as_defaults():
+def test_lsq_minimizer_reads_cif_settings():
     from easydiffraction.analysis.categories.minimizer.lmfit_leastsq import (
         LmfitLeastsqMinimizer,
     )
@@ -40,13 +27,9 @@ def test_lsq_minimizer_reads_cif_unknown_values_as_defaults():
     document = gemmi.cif.read_string(
         """data_minimizer
 _minimizer.max_iterations 42
-_minimizer.objective_name chi-square
-_minimizer.objective_value ?
 """
     )
     minimizer = LmfitLeastsqMinimizer()
     minimizer.from_cif(document.sole_block())
 
     assert minimizer.max_iterations.value == 42
-    assert minimizer.objective_name.value == 'chi-square'
-    assert minimizer.objective_value.value is None
diff --git a/tests/unit/easydiffraction/analysis/categories/test_fit_result.py b/tests/unit/easydiffraction/analysis/categories/test_fit_result.py
index 4c9d869dc..580635c8c 100644
--- a/tests/unit/easydiffraction/analysis/categories/test_fit_result.py
+++ b/tests/unit/easydiffraction/analysis/categories/test_fit_result.py
@@ -4,10 +4,10 @@
 
 
 def test_fit_result_factory_create():
-    from easydiffraction.analysis.categories.fit_result.default import FitResult
+    from easydiffraction.analysis.categories.fit_result.base import FitResultBase
     from easydiffraction.analysis.categories.fit_result.factory import FitResultFactory
 
     fit_result = FitResultFactory.create('default')
 
     assert FitResultFactory.default_tag() == 'default'
-    assert isinstance(fit_result, FitResult)
+    assert isinstance(fit_result, FitResultBase)
diff --git a/tests/unit/easydiffraction/analysis/categories/test_fit_state.py b/tests/unit/easydiffraction/analysis/categories/test_fit_state.py
index a26d4435b..a3b4fd7cc 100644
--- a/tests/unit/easydiffraction/analysis/categories/test_fit_state.py
+++ b/tests/unit/easydiffraction/analysis/categories/test_fit_state.py
@@ -36,9 +36,9 @@ def test_fit_parameter_collection_serializes_expected_tags_and_values():
 
 
 def test_fit_result_serializes_expected_tags_and_enum_value():
-    from easydiffraction.analysis.categories.fit_result.default import FitResult
+    from easydiffraction.analysis.categories.fit_result.base import FitResultBase
 
-    fit_result = FitResult()
+    fit_result = FitResultBase()
     fit_result._set_result_kind('bayesian')
     fit_result._set_success(value=True)
     fit_result._set_message('Sampler completed')
@@ -138,7 +138,10 @@ def test_fit_parameter_posterior_summary_serializes_expected_tags():
     assert summary.ess_bulk == 120
 
 
-def test_dream_minimizer_sampler_and_diagnostics_use_cif_fields():
+def test_dream_sampler_settings_and_diagnostics_use_split_cif_fields():
+    from easydiffraction.analysis.categories.fit_result.bayesian import (
+        BayesianFitResult,
+    )
     from easydiffraction.analysis.categories.minimizer.bumps_dream import (
         BumpsDreamMinimizer,
     )
@@ -148,15 +151,17 @@ def test_dream_minimizer_sampler_and_diagnostics_use_cif_fields():
     minimizer.burn_in_steps = 20
     minimizer.parallel_workers = 0
     minimizer.random_seed = 123
-    minimizer._set_gelman_rubin_max(1.01)
-    minimizer._set_effective_sample_size_min(80)
+    fit_result = BayesianFitResult()
+    fit_result._set_gelman_rubin_max(1.01)
+    fit_result._set_effective_sample_size_min(80)
 
-    cif_text = minimizer.as_cif
+    minimizer_cif_text = minimizer.as_cif
+    fit_result_cif_text = fit_result.as_cif
 
-    assert '_minimizer.sampling_steps 100' in cif_text
-    assert '_minimizer.parallel_workers 0' in cif_text
-    assert '_minimizer.random_seed 123' in cif_text
-    assert '_minimizer.effective_sample_size_min' in cif_text
+    assert '_minimizer.sampling_steps 100' in minimizer_cif_text
+    assert '_minimizer.parallel_workers 0' in minimizer_cif_text
+    assert '_minimizer.random_seed 123' in minimizer_cif_text
+    assert '_fit_result.effective_sample_size_min' in fit_result_cif_text
 
 
 def test_fit_parameter_posteriors_preserve_row_order_from_cif():
diff --git a/tests/unit/easydiffraction/io/test_results_sidecar.py b/tests/unit/easydiffraction/io/test_results_sidecar.py
index 70dbef7d6..75dbe571e 100644
--- a/tests/unit/easydiffraction/io/test_results_sidecar.py
+++ b/tests/unit/easydiffraction/io/test_results_sidecar.py
@@ -17,9 +17,11 @@ def _analysis_with_sidecar_payload(
     include_pair: bool = True,
     include_predictive: bool = True,
 ) -> object:
-    from easydiffraction.analysis.categories.fit_result.default import FitResult
+    from easydiffraction.analysis.categories.fit_result.bayesian import (
+        BayesianFitResult,
+    )
 
-    fit_result = FitResult()
+    fit_result = BayesianFitResult()
     fit_result._set_result_kind('bayesian')
 
     posterior_samples = None
diff --git a/tests/unit/easydiffraction/project/test_display.py b/tests/unit/easydiffraction/project/test_display.py
index 23de62edb..f80d243e4 100644
--- a/tests/unit/easydiffraction/project/test_display.py
+++ b/tests/unit/easydiffraction/project/test_display.py
@@ -54,6 +54,7 @@ def _recorder(*args, **kwargs):
         analysis=SimpleNamespace(
             display=analysis_display,
             fit_results=SimpleNamespace(posterior_predictive={}),
+            minimizer=SimpleNamespace(_setting_descriptor_names=()),
             bayesian_result=SimpleNamespace(
                 has_pair_cache=SimpleNamespace(value=False),
                 has_posterior_predictive=SimpleNamespace(value=False),
diff --git a/tests/unit/easydiffraction/project/test_project_load.py b/tests/unit/easydiffraction/project/test_project_load.py
index c726172f4..cfbd7652d 100644
--- a/tests/unit/easydiffraction/project/test_project_load.py
+++ b/tests/unit/easydiffraction/project/test_project_load.py
@@ -211,14 +211,14 @@ def test_round_trips_persisted_deterministic_correlation_summary_for_reloaded_di
         original.analysis.fit_result._set_iterations(21)
         original.analysis.fit_result._set_fitting_time(0.74)
         original.analysis.fit_result._set_reduced_chi_square(1.031)
-        original.analysis.minimizer._set_objective_name('chi-square')
-        original.analysis.minimizer._set_objective_value(1.031)
-        original.analysis.minimizer._set_n_data_points(120)
-        original.analysis.minimizer._set_n_parameters(2)
-        original.analysis.minimizer._set_n_free_parameters(2)
-        original.analysis.minimizer._set_degrees_of_freedom(118)
-        original.analysis.minimizer._set_covariance_available(value=False)
-        original.analysis.minimizer._set_correlation_available(value=True)
+        original.analysis.fit_result._set_objective_name('chi-square')
+        original.analysis.fit_result._set_objective_value(1.031)
+        original.analysis.fit_result._set_n_data_points(120)
+        original.analysis.fit_result._set_n_parameters(2)
+        original.analysis.fit_result._set_n_free_parameters(2)
+        original.analysis.fit_result._set_degrees_of_freedom(118)
+        original.analysis.fit_result._set_covariance_available(value=False)
+        original.analysis.fit_result._set_correlation_available(value=True)
         original.analysis.fit_parameter_correlations.create(
             source_kind='deterministic',
             param_unique_name_i=parameter_b.unique_name,