E13b/c: harden real-weight speciation — full symmetry group + emergent-divergence null

E13c (the symmetry defense): alignment now runs modulo the FULL
function-preserving unit symmetry group of a ReLU MLP (per-unit positive
rescaling via canonicalise_scale, composed with Re-Basin permutations;
sanity gate recovers a permuted-and-rescaled copy exactly). Verdict: the
full group removes the independent-init barrier (residual 0.001) and
essentially none of the conflict barrier (0.502 -> 0.497) — the residual
is functional, not a missed symmetry (answers arXiv:2606.23607). The
cliff gains a hybrid-fitness readout: merged accuracy 0.97 -> 0.03 with
conflict. Floor proposition drafted (paper/si-notes.md S1): endpoint
invariance + max(eps_A, eps_B) >= mu(S)/2 for any merged model under any
alignment group.

E13b (emergent divergence): pre-registered second reading — with NO
conflicting training signal (disjoint class specialists; rolled-input
conventions), residual is 0.000 at every divergence to t_div=3200, and
the merge RESCUES the forgetting specialists (parents 0.535/0.474 ->
merged 0.955; a sustained Fisher-Muller rescue at zero barrier).
Speciation in real weights requires functional conflict; it does not
emerge from compatible specialisation on shared ancestry. LLM-scale
over-specialisation (cf. 2607.11997) deferred to Phase-3 llm_speciation.

3-panel figure, READMEs, +2 tests (149 green), make mnist wired.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01BkRLcc18rwT2Lysu6PbG7v

results/speciation_real_cliff/manifest.json

@@ -1,8 +1,8 @@
 {
   "experiment": "speciation_real_cliff",
   "master_seed": 13,
-  "git_commit": "56f642e7f9ae01fe01d863bdffe98b226b9dbb7b",
-  "python": "3.14.5",
+  "git_commit": "f5f68f52498402ba7cc6a5193e5e357be6357446",
+  "python": "3.14.7",
   "libraries": {
     "numpy": "2.5.0",
     "scipy": "1.18.0",
@@ -12,7 +12,7 @@
     "torchvision": "0.27.1"
   },
   "rows": 18,
-  "results_sha256": "0252581d848376ad698f56d4e69edbcae40cf5a0f090203d6f723cfc5e25303c",
+  "results_sha256": "889945655a6efaba9d97eed409f24968c9f77764101bdfa05acdf00c8f8668b7",
   "layer": "1.5",
   "tier": "speciation_real"
 }