MachineSex/results/speciation_real

E13 — Real-weight model speciation (Git Re-Basin residual)

Claim tested. E12 predicts model speciation analytically: as two lineages diverge, recombination (merging) fails, via Bateson–Dobzhansky–Muller incompatibilities. E13 confirms it in real trained weights, and — decisively — separates the part of the incompatibility that is a mere coordinate artefact (removable by permuting hidden units; Git Re-Basin, Ainsworth et al. 2022) from the residual that permutation cannot remove, which is the true reproductive-isolation signal. This is the experiment that answers the mode-connectivity reviewer: if alignment removes the barrier, it was a coordinate artefact; the barrier that survives alignment is real speciation.

Setup. Small no-BatchNorm MLPs (784–512–512–10) on MNIST — the clean Re-Basin regime. Two children are forked from a shared base and trained independently; we weight-average them and measure the linear-mode-connectivity error barrier before (naive) and after (aligned) in-house, deterministic Git Re-Basin weight-matching (neural/rebasin.py, scipy linear_sum_assignment). Statistically reproducible (seeded torch; NumPy/scipy alignment is deterministic). 3 replicates.

Results — the decomposition (mean over divergence, reps)

condition	naive barrier	removable (coordinate)	residual (isolation)
shared (same task, shared fork)	0.00	0.00	0.00
independent (same task, different init)	0.056	0.055	0.001
conflict (conflicting label maps)	0.496	0.000	0.496

• independent: two nets trained from different random inits on the same task have a real naive barrier — which alignment removes ~98% of (residual 0.001). Same species, different basis: the incompatibility is a coordinate artefact. (This reproduces the canonical Git Re-Basin result and proves our alignment works.)
• conflict: two nets that learned conflicting functions have a large barrier that alignment removes none of (residual 0.496). Different species: genuine reproductive isolation. Because alignment demonstrably works on independent, this residual cannot be dismissed as a failure to align.
• The residual after alignment is therefore the clean discriminator: ~0 for compatible models (even independently trained), large only for functionally incompatible ones.

Results — the isolation cliff (speciation_real_cliff/)

Sweeping the fraction of classes on which child B learns a conflicting label map, the residual (after-alignment) barrier rises monotonically — the real-weight image of E12's cliff:

conflict fraction	0.0	0.2	0.4	0.6	0.8	1.0
residual barrier	0.00	0.13	0.19	0.28	0.40	0.49

residual = naive at every point (alignment removes nothing in the conflict condition), so the cliff is genuinely functional isolation, not a coordinate artefact.

Positioning

The incumbents each hold one piece: Git Re-Basin / Entezari (barriers are coordinate artefacts), Frankle (the fork-instability protocol), Pari et al. 2024 (specialisation diverges representations, route don't fuse), Zhou et al. 2026 (predict mergeability from divergence metrics). E13's contribution is the synthesis they lack: a controlled decomposition where alignment cleanly partitions the merge barrier into a removable coordinate artefact and a residual reproductive-isolation term that rises with task conflict — the real-weight confirmation of E12's speciation prediction, and the direct answer to "isn't this just a loss barrier / permutation artefact?" Falsifier (not triggered): alignment failing to remove the independent-init barrier (then residual is meaningless), or conflict showing no residual — instead alignment removed 98% of the former and 0% of the latter.