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figures: publication-ready — unified, lettered, codename-free

Browse source 
paper/pnas/make_figs.py re-plots every panel directly from the committed
results artifacts into six single-file figures (figs/fig1..fig6.pdf):
no experiment codenames or suptitles (interpretation moved to captions),
bold panel letters, plain-language axis labels and legend entries, one
consistent style (8pt, no top/right spines). Panels: fig1 A-B (grounding
equilibrium + MNIST montage with its baked-in title cropped), fig2 A-B
(blending cancellation + Fisher-Muller), fig3 A-D (outbreeding, directed
recombination, mating breadth champion + diversity), fig4 A-C (society
ablation trajectories), fig5 A-F (speciation: analytic curve + cliff,
MLP decomposition + conflict sweep, LLM coherence + duration null),
fig6 A-D (seed-replicated merging, 7B-hard routing vs averaging,
predictive-test scatter, predictor comparison). build.py now places the
single PDFs; captions rewritten per lettered panel; in-text panel refs
updated (5B->5C-D, 5C->5E-F); stale stacked copies removed. Document
20pp -> 18pp.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01BkRLcc18rwT2Lysu6PbG7v
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Giorgio Gilestro 2026-09-07 09:09:46 +01:00
parent 6f1f8bf172
commit 96902e87f0
26 changed files with 469 additions and 96 deletions
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@ -294,7 +294,7 @@ In trained networks, the claim must survive a known alternative: merge barriers
trained networks are famously *coordinate artefacts*, removable by re-aligning hidden units (62);
richer symmetry groups remove more (63), with known failures beyond the shared-data regime (64). We therefore aligned under the composition of
permutation matching and exact per-unit rescaling (the unit symmetry group of plain ReLU MLPs, as the
search space) and decomposed the barrier (Fig. 5B): two networks trained from different
search space) and decomposed the barrier (Fig. 5 C and D): two networks trained from different
initialisations on the *same* task have a barrier that this alignment removes essentially entirely
(residual ≈ 0.001, the aligned merge performing at parent level): coordinate, not functional; two
networks trained on *conflicting* label maps have a barrier the same alignment leaves largely
@ -312,7 +312,7 @@ emergent (each lineage's changes harmless alone), so we let children diverge wit
signal anywhere*, using complementary class specialists and divergent input conventions, to 6.4× the base
training. **No isolation emerged** (residual 0.000 throughout); instead the merge *rescued* the two
catastrophically-forgetting specialists (parents ≈ 0.50, merge ≈ 0.955, a sustained Fisher–Muller
rescue). The same double result appears at the language-model tier (Fig. 5C): conflicting conventions
rescue). The same double result appears at the language-model tier (Fig. 5 E and F): conflicting conventions
produce **function-specific** hybrid breakdown (the merge scores below both parents on the conflicted
function, while a budget-controlled design shows the disjoint skills merge unharmed), and over-training
disjoint specialists 1→12 epochs (cf. the merging literature's expert-duration effect; 65) produces