Record the MLX convergence-stop divergence

docs/guides/amica-differences.md

@@ -120,6 +120,39 @@ Separate from reference divergences: the optional MLX backend is a subset.
 | Outlier rejection | yes | yes | no | yes |
 | Precision | f64/f32 | f64 | f32 only | f64 |
 | Rank detection | yes | yes | yes | yes (absolute floor) |
-
-MLX limitations raise `NotImplementedError` rather than differing silently, per
-`.rules/backend_parity.md`.
+| `min_dll` stop | yes | yes | **no** | yes |
+| `min_nd` stop / gradient norm | yes | yes (as `min_grad_norm`) | **no** | yes |
+| `keep_best` best-iterate restore | yes | no | no | n/a |
+| MIR diagnostic | yes | no | no | n/a |
+| Persistence | `state_dict` | EEGLAB `amicaout` | none | EEGLAB `amicaout` |
+
+Most MLX limitations fail loudly: `do_newton=True` and non-GG `pdftype` raise
+`NotImplementedError`, and every unsupported parameter is simply absent from the
+constructor, so passing it raises `TypeError`.
+
+**The convergence stops are the exception, and the one difference to plan
+around.** `pamica/mlx_impl/core.py` implements neither stop — it has no
+`min_dll`/`use_min_dll`/`maxincs` and no `min_nd`/`use_grad_norm`/`ndtmpsum`, so
+its only outcomes are `max_iter`, `lrate_floor`, and the degenerate reasons
+(`nan_ll`, `singular_ll`, `nan_params`).
+
+The distinction is not that an MLX fit runs to `max_iter` — a PyTorch fit does
+too at its own `max_iter=100` default, because `min_dll` needs several hundred
+iterations to trigger on a recording this size. The distinction is what raising
+`max_iter` buys you. On the other backends, a larger budget lets the likelihood
+stop end the fit when it stops improving. **On MLX a larger budget is simply
+spent**, however long the fit has stopped making progress.
+
+Nothing warns you about this, because there is no parameter to reject. Two
+consequences:
+
+- Moving a working configuration from the PyTorch backend to MLX for the
+  Apple-GPU speedup does more work than the same `max_iter` implies. Size
+  `max_iter` for MLX from where the other backends actually stop improving on
+  your data.
+- MLX cannot be evaluated on gradient-norm behaviour at all, because it never
+  computes the quantity. Any statement elsewhere in these guides about whether
+  the `min_nd` threshold is reachable covers Fortran, PyTorch and NumPy only.
+  (`numpy_impl` spells that same threshold `min_grad_norm`.)
+
+Tracked in issue #248.