recombination: reproduce the E4 "merge, don't average" finding in real weights

src/neural/recombine.py mirrors Layer-1 run_coverage but trains K_T specialist RNNs on
assignments from the exact shared-switch retention construction (K_T/rho/q clean; union
matches the closed form), then recombines the measured teacher distributions two ways:
mean (naive pooling) vs oracle-guided max-merge (per-mode strongest teacher, M2N2-style),
each followed by size-n resampling.

Result (8 reps): at rho=0, union rises 0.49->0.96 (supply matches closed form); analytic
surviving_max rises 0.043->0.087 while surviving_mean stays flat ~0.045 — the conservation
law (averaging cancels the union gain, max-merge realises it). At rho=1 (identical
teachers) union and max are flat. The lesson holds in the neural setting; trained-weight
columns show the same signs but noisier (smoothing inflates baseline; deep tail barely
clears n=200 resampling). torch-gated test added. 93 tests green.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

results/recombination/manifest.json

@@ -0,0 +1,17 @@
+{
+  "experiment": "recombination",
+  "master_seed": 20260704,
+  "git_commit": "aca7b394a3f4b1f487afdb01d70757956408d59f",
+  "python": "3.14.5",
+  "libraries": {
+    "numpy": "2.5.0",
+    "scipy": "1.18.0",
+    "pandas": "3.0.3",
+    "pyarrow": "24.0.0",
+    "torch": "2.12.1"
+  },
+  "rows": 64,
+  "results_sha256": "8e6cb83e4e5a0c711137d4f70c3d83192f845ceaa9194b3aeac427741808477b",
+  "layer": "1.5",
+  "model_kind": "rnn"
+}