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New Fig. 1 (experimental-programme schematic); Table 2 to SI; figures in citation order; Fig. 2B legible labels

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Replaces the results table with a pipeline figure: five questions x three
architecture tiers (exact Wright-Fisher simulator, trained networks, language
models), filled cells naming the experiments, dashed cells the honest gaps.
Table 1 (the dictionary) stays; Table 2 moves to SI Appendix Table S2. The
renumber surfaced a pre-existing citation-order violation (the LLM figure was
cited in the recombination section before Figs. 3-6), so figures are renumbered
to strict first-citation order (LLM tier is now Fig. 3). Fig. 2B: the montage's
baked-in raster labels are cropped away and replaced with vector row numbers
under a rotated "generation" header.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01BkRLcc18rwT2Lysu6PbG7v
This commit is contained in:
Giorgio Gilestro 2026-09-07 11:12:33 +01:00
parent 073fc33509
commit 0159e2839a
13 changed files with 208 additions and 109 deletions
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20
paper/pnas/build.py
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@ -22,6 +22,16 @@ OUT = HERE / "body.tex"
# figure name -> (single publication PDF from make_figs.py, caption)
FIGURES: dict[str, tuple[list[str], str]] = {
"fig1": (["paper/pnas/figs/fig1.pdf"],
"The experimental programme. Each population-genetic abstraction (Table 1) is tested at up "
"to three tiers of model architecture, ordered left to right by increasing realism: an exact "
"Wright--Fisher simulator over knowledge distributions (closed forms; bitwise-reproducible), "
"trained neural networks measured against exact oracles (recurrent, feedforward, and "
"variational-autoencoder generators on a synthetic mode universe, and a convolutional VAE on "
"MNIST), and language models (LoRA specialists on Qwen bases at 0.5B and 7B, scored by an "
"exact-match verifier). Rows are the paper's five questions; filled cells name the "
"experiments run at each tier; dashed cells were not tested, and the composed society at "
"language-model scale is the paper's stated gap."),
"fig2": (["paper/pnas/figs/fig2.pdf"],
"Grounding is immigration. (A) Stationary diversity against the grounding fraction in the "
"minimal inheritance model: simulation (points, 95\\% CI) matches the exact immigration--drift "
"equilibrium (dashed). The equilibrium is smooth in $g$; $g \\approx 0.05$ marks the "
@ -30,7 +40,7 @@ FIGURES: dict[str, tuple[list[str], str]] = {
"is zero). (B) The same signs on real images: samples from a convolutional VAE retrained each "
"generation on its own output (rows: generations 0--15 of an ungrounded lineage) collapse "
"toward a single blurred mode; 10\\% grounding holds all thirty modes (quantified in SI)."),
"fig2": (["paper/pnas/figs/fig2.pdf"],
"fig4": (["paper/pnas/figs/fig4.pdf"],
"Recombination in the minimal model: blending inheritance and the Fisher--Muller effect. "
"(A) Expected rare-capability survival in a child refit from $K$ uncorrelated parents: the "
"output-mean (blending) stays at the single-parent level --- the first-order cancellation --- "
@ -38,7 +48,7 @@ FIGURES: dict[str, tuple[list[str], str]] = {
"with parent count. (B) Multi-locus recombination of decorrelated specialists produces "
"offspring fitter than any parent, approaching the optimum as parents are added; the best "
"single parent and the blended average plateau below (mean $\\pm$ 95\\% CI)."),
"fig3": (["paper/pnas/figs/fig3.pdf"],
"fig5": (["paper/pnas/figs/fig5.pdf"],
"Rugged (epistatic) landscapes: risk, remedy, and population structure. (A) Outbreeding "
"depression: the mean offspring of blindly recombined specialist parents falls below the best "
"parent, more steeply the more rugged the landscape (NK ruggedness $K$) and the higher the "
@ -47,7 +57,7 @@ FIGURES: dict[str, tuple[list[str], str]] = {
"(C) Mating structure: the best champion arises at wide mate-pool breadth on smooth landscapes "
"and at intermediate breadth on rugged ones. (D) Wide breadth monotonically erodes population "
"diversity at every ruggedness (mean $\\pm$ 95\\% CI, 20 replicates)."),
"fig4": (["paper/pnas/figs/fig4.pdf"],
"fig6": (["paper/pnas/figs/fig6.pdf"],
"The tested society: grounded evaluation, recombination, and diversity preservation make "
"complementary contributions. A finite agent population on a rugged NK landscape; selection "
"weights true fitness against conformity to the population consensus. (A) Best real fitness: "
@ -55,7 +65,7 @@ FIGURES: dict[str, tuple[list[str], str]] = {
"population onto a confident, unfit consensus; removing recombination or diversity "
"preservation strands it lower. (B) Population diversity. (C) The self-consumption signature: "
"conformity minus true fitness (mean $\\pm$ 95\\% CI, 12 replicates)."),
"fig5": (["paper/pnas/figs/fig5.pdf"],
"fig7": (["paper/pnas/figs/fig7.pdf"],
"Model speciation at three tiers. (A) Analytic model: hybrid fitness tracks the parents while "
"lineages are compatible, then falls to inviability; the denser the incompatibilities, the "
"earlier the fall. (B) The isolation cliff: probability of hybrid inviability against "
@ -68,7 +78,7 @@ FIGURES: dict[str, tuple[list[str], str]] = {
"own convention while the merged model falls below both --- function-specific hybrid "
"breakdown. (F) Divergence without conflict: over-training disjoint specialists from 1 to 12 "
"epochs produces no isolation; the merged model tracks or exceeds the parents throughout."),
"fig6": (["paper/pnas/figs/fig6.pdf"],
"fig3": (["paper/pnas/figs/fig3.pdf"],
"The language-model tier. (A) Seed-replicated merging (0.5B, five seeds, fixed test sets; mean "
"$\\pm$ 95\\% CI): merged specialists exceed the best single specialist overall, and only "
"merged models are competent on every task family. (B) Hard, unsaturated tasks at 7B (single "