E13: real-weight model speciation — the Git Re-Basin residual confirms E12

The real-weight image of E12, and the answer to the mode-connectivity reviewer.
Small no-BN MLPs on MNIST, forked from a shared base and trained independently,
are weight-averaged; we measure the linear-mode-connectivity barrier before and
after in-house deterministic Git Re-Basin permutation alignment (neural/rebasin.py,
scipy linear_sum_assignment), decomposing it into removable (coordinate artefact)
and residual (reproductive isolation). kind: speciation_real.

Result (3 reps):
- shared (same task, shared fork): no barrier — trivially mergeable.
- independent (same task, different init): naive 0.056, alignment removes 98%
  (residual 0.001) — the incompatibility is a coordinate artefact.
- conflict (conflicting label maps): naive 0.496, alignment removes 0% (residual
  0.496) — genuine reproductive isolation. Because alignment demonstrably works on
  the independent case, the conflict residual is real, not a failure to align.
- Isolation cliff (speciation_real_cliff): residual rises 0.00->0.13->0.19->0.28->
  0.40->0.49 with the fraction of conflicting classes — the real-weight mirror of
  E12's cliff; residual==naive throughout (functional, not coordinate).

rebasin.py sanity-gated (recovers a known permutation exactly). plot_speciation_real.py
(2-panel), +4 pure-NumPy tests (142 green), README with honest positioning vs
Git Re-Basin / Entezari / Frankle / Pari 2024 / Zhou 2026. Wired into make mnist
(needs torchvision).

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

src/neural/experiment.py

@@ -188,6 +188,11 @@ def run_and_save(config_path: str | Path) -> Path:
     elif kind == "mnist_lineage":
         df, extra_manifest = run_mnist_experiment(cfg)   # oracle provenance + confusion matrix
         grid = [{"label": label, "lineage_cfg": c} for label, c in _expand_mnist(cfg)]
+    elif kind == "speciation_real":
+        from .speciation_real import run_speciation_real   # E13: real-weight speciation + Git Re-Basin
+        df = run_speciation_real(cfg, int(cfg["seed"]))
+        grid = None
+        extra_manifest = {"layer": "1.5", "tier": "speciation_real"}
     else:
         raise ValueError(f"unknown neural experiment kind {kind!r}")
     save_artifacts(cfg, df, out_dir, extra_libs=_EXTRA_LIBS,