feat(assembly): fixed reference planes + solver docs

Assembly Origin Planes:
- AssemblyObject::setupObject() relabels origin planes to
  Top (XY), Front (XZ), Right (YZ) on assembly creation
- CommandCreateAssembly.py makes origin planes visible by default
- AssemblyUtils.cpp getObjFromRef() resolves LocalCoordinateSystem
  to child datum elements for joint references to origin planes
- TestAssemblyOriginPlanes.py: 9 integration tests covering
  structure, labels, grounding, reference resolution, solver,
  and save/load round-trip

Solver Documentation:
- docs/src/solver/: 7 new pages covering architecture overview,
  expression DAG, constraints, solving algorithms, diagnostics,
  assembly integration, and writing custom solvers
- docs/src/SUMMARY.md: added Kindred Solver section

docs/src/solver/diagnostics.md

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+# Diagnostics
+
+The solver provides three levels of constraint analysis: system-wide DOF counting, per-entity DOF decomposition, and overconstrained/conflicting constraint detection.
+
+## DOF counting
+
+**Source:** `mods/solver/kindred_solver/dof.py`
+
+Degrees of freedom are computed from the Jacobian rank:
+
+```
+DOF = n_free_params - rank(J)
+```
+
+Where `n_free_params` is the number of non-fixed parameters and `rank(J)` is the numerical rank of the Jacobian evaluated at current parameter values (SVD with tolerance `1e-8`).
+
+A well-constrained assembly has `DOF = 0` (exactly enough constraints to determine all positions). Positive DOF means underconstrained (parts can still move). Negative DOF is not possible with this formulation -- instead, rank deficiency in an overdetermined system indicates redundant constraints.
+
+The DOF value is reported in `SolveResult.dof` after every solve.
+
+## Per-entity DOF
+
+**Source:** `mods/solver/kindred_solver/diagnostics.py`
+
+`per_entity_dof()` breaks down the DOF count per body, identifying which motions remain free for each part:
+
+1. Build the full Jacobian
+2. For each non-grounded body, extract the 7 columns corresponding to its parameters
+3. Compute SVD of the sub-matrix; rank = number of constrained directions
+4. `remaining_dof = 7 - rank` (includes the quaternion normalization constraint counted in the rank)
+5. Classify null-space vectors as free motions by analyzing their translation vs. rotation components:
+   - Pure translation: >80% of the null vector's energy is in `tx, ty, tz` components
+   - Pure rotation: >80% of the energy is in `qw, qx, qy, qz` components
+   - Helical: mixed
+
+Returns a list of `EntityDOF` dataclasses:
+
+```python
+@dataclass
+class EntityDOF:
+    entity_id: str
+    remaining_dof: int
+    free_motions: list[str]  # e.g., ["rotation about Z", "translation along X"]
+```
+
+## Overconstrained detection
+
+**Source:** `mods/solver/kindred_solver/diagnostics.py`
+
+`find_overconstrained()` identifies redundant and conflicting constraints when the system is overconstrained (Jacobian is rank-deficient). It runs automatically when `solve()` fails to converge.
+
+### Algorithm
+
+Following the approach used by SolvSpace:
+
+1. **Check rank.** Build the full Jacobian `J`, compute its rank via SVD. If `rank == n_residuals`, the system is not overconstrained -- return empty.
+
+2. **Find redundant constraints.** For each constraint, temporarily remove its rows from J and re-check rank. If the rank is preserved, the constraint is **redundant** (removing it doesn't change the system's effective equations).
+
+3. **Distinguish conflicting from merely redundant.** Compute the left null space of J (columns of U beyond the rank). Project the residual vector onto this null space:
+   ```
+   null_residual = U_null^T @ r
+   residual_projection = U_null @ null_residual
+   ```
+   If a redundant constraint's residuals have significant projection onto the null space, it is **conflicting** -- it's both redundant and unsatisfied, meaning it contradicts other constraints.
+
+### Diagnostic output
+
+Returns `ConstraintDiag` dataclasses:
+
+```python
+@dataclass
+class ConstraintDiag:
+    constraint_index: int
+    kind: str           # "redundant" or "conflicting"
+    detail: str         # Human-readable explanation
+```
+
+These are converted to `kcsolve.ConstraintDiagnostic` objects in the IKCSolver bridge:
+
+| ConstraintDiag.kind | kcsolve.DiagnosticKind |
+|---------------------|----------------------|
+| `"redundant"` | `Redundant` |
+| `"conflicting"` | `Conflicting` |
+
+### Example
+
+Two Fixed joints between the same pair of parts:
+
+- Joint A: 6 residuals (3 position + 3 orientation)
+- Joint B: 6 residuals (same as Joint A)
+
+Jacobian rank = 6 (Joint B's rows are linearly dependent on Joint A's). Both joints are detected as redundant. If the joints specify different relative positions, both are also flagged as conflicting.
+
+## Solution preferences
+
+**Source:** `mods/solver/kindred_solver/preference.py`
+
+Solution preferences guide the solver toward physically intuitive solutions when multiple valid configurations exist.
+
+### Minimum-movement weighting
+
+The weight vector scales the Newton step to prefer solutions near the initial configuration. Translation parameters get weight `1.0`, quaternion parameters get weight `(180/pi)^2 ~ 3283`. This makes a 1-radian rotation equally "expensive" as a ~57-unit translation.
+
+The weighted minimum-norm step is:
+
+```
+J_scaled = J @ diag(W^{-1/2})
+dx_scaled = lstsq(J_scaled, -r)
+dx = dx_scaled * W^{-1/2}
+```
+
+This produces the minimum-norm solution in the weighted parameter space, biasing toward small movements.
+
+### Half-space tracking
+
+Described in detail in [Solving Algorithms: Half-space tracking](solving.md#half-space-tracking). Preserves the initial configuration's "branch" for constraints with multiple valid solutions by detecting and correcting branch crossings during iteration.