MachineSex/src/neural
Giorgio Gilestro ea051a5f92 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
2026-09-06 12:35:14 +01:00
..
__init__.py Layer 1.5: architecture-general neural existence proof 2026-07-04 21:02:49 +01:00
config.py neural: real-MNIST external-validity tier (collapse + grounding) 2026-07-05 09:19:36 +01:00
evaluate.py Layer 1.5: architecture-general neural existence proof 2026-07-04 21:02:49 +01:00
experiment.py E13: real-weight model speciation — the Git Re-Basin residual confirms E12 2026-07-09 00:19:04 +01:00
generation_loop.py Rename neural experiments to descriptive paths (drop N* codes) 2026-07-04 21:21:56 +01:00
mnist_data.py neural: real-MNIST external-validity tier (collapse + grounding) 2026-07-05 09:19:36 +01:00
mnist_loop.py neural: real-MNIST external-validity tier (collapse + grounding) 2026-07-05 09:19:36 +01:00
mnist_oracle.py neural: real-MNIST external-validity tier (collapse + grounding) 2026-07-05 09:19:36 +01:00
mnist_vae.py neural: real-MNIST external-validity tier (collapse + grounding) 2026-07-05 09:19:36 +01:00
models.py Rename neural experiments to descriptive paths (drop N* codes) 2026-07-04 21:21:56 +01:00
oracle.py neural: grounding refinement + all five Layer-1.5 figures 2026-07-05 08:14:19 +01:00
rebasin.py E13b/c: harden real-weight speciation — full symmetry group + emergent-divergence null 2026-09-06 12:35:14 +01:00
recombine.py recombination: reproduce the E4 "merge, don't average" finding in real weights 2026-07-04 21:49:44 +01:00
speciation_real.py E13b/c: harden real-weight speciation — full symmetry group + emergent-divergence null 2026-09-06 12:35:14 +01:00
synthetic.py Layer 1.5: architecture-general neural existence proof 2026-07-04 21:02:49 +01:00
torch_mlp.py Rename neural experiments to descriptive paths (drop N* codes) 2026-07-04 21:21:56 +01:00
torch_models.py Rename neural experiments to descriptive paths (drop N* codes) 2026-07-04 21:21:56 +01:00
torch_vae.py Rename neural experiments to descriptive paths (drop N* codes) 2026-07-04 21:21:56 +01:00
train.py Layer 1.5: architecture-general neural existence proof 2026-07-04 21:02:49 +01:00