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Fig. 1 revision: pop-gen definitions as row labels, title into the caption, solid header row

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Row labels now teach the vocabulary (grounding = immigration, recombination =
sex, epistasis, the composed society, speciation = reproductive isolation),
each with a two-to-three-line definition in the left margin, replacing the
question taglines. The in-figure title/arrow is removed (the caption carries
it); the tier header row is now solid-colour with white text so the
model-nature band reads as a header rather than a content row.

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 11:21:06 +01:00
parent 0159e2839a
commit e42fe497ff
5 changed files with 39 additions and 36 deletions
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66
paper/pnas/make_figs.py
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@ -48,66 +48,69 @@ def fig1():
TIERS = [
("Exact model", "Wright\u2013Fisher simulator (NumPy)", "closed forms \u00b7 bitwise-reproducible",
"#4292c6", "#eaf2fa"),
"#3d7eb8", "#eaf2fa"),
("Trained networks", "RNN \u00b7 MLP \u00b7 VAE on a synthetic oracle;\nconvolutional VAE on MNIST",
"sign-level tests \u00b7 exact oracles", "#41ab5d", "#edf8ea"),
"sign-level tests \u00b7 exact oracles", "#3f8f4f", "#edf8ea"),
("Language models", "LoRA specialists on Qwen 0.5B & 7B;\nexact-match verifier",
"seed-replicated signs", "#e6550d", "#fdf0e6"),
"seed-replicated signs", "#d9650f", "#fdf0e6"),
]
ROWS = [
("Grounding", "how much real data?",
("Grounding = immigration",
"fresh verified samples from a\nfixed external source enter the\ntraining mix every generation",
["immigration\u2013drift equilibrium:\n$g \\approx 0.05$ retains $\\geq$95% diversity;\nobservation floor $1-e^{-mp}$",
"collapse & rescue in every\narchitecture; MNIST: dry 30$\\to$1 modes,\n10% grounding holds 30/30;\nestimator-bias learning kernel",
None]),
("Recombination", "blend or merge?",
("Recombination = sex",
"a child inherits from several\nparents, reassembling variants\nthat arose in different lineages",
["blending conservation law\n(first-order cancellation);\nunion-operator gain; Fisher\u2013Muller",
"merge rescues two forgetting\nspecialists ($\\approx$0.50 $\\to$ 0.955)",
"merged specialists beat every parent\n(5 seeds at 0.5B; 7B); routing vs\naveraging: the headroom rule"]),
("Entangled skills", "who merges with whom?",
("Epistasis (entangled skills)",
"a variant's fitness contribution\ndepends on the variants present\nat the other loci",
["NK landscapes: outbreeding\ndepression; directed sex restores\nthe gain; mate-pool breadth optimum",
None,
"bred-and-screened offspring beat\nthe blind blend in every seed\n(hard, unsaturated tasks)"]),
("The composed society", "can the loop sustain itself?",
("The composed society",
"selection, recombination,\ndiversity preservation and\ngrounding on one population",
["four-arm ablation: grounding, sex,\ndiversity each removed\n$\\to$ three distinct failures",
None,
"OPEN"]),
("Speciation & prediction", "when does merging fail?",
("Speciation",
"reproductive isolation: diverged\nlineages no longer produce\nviable (mergeable) offspring",
["BDM incompatibility model:\nisolation cliff; quadratic snowball",
"barrier decomposition under\npermutation+rescaling; conflict\nsweep 0.97$\\to$0.03; emergent null",
"convention conflict $\\to$ hybrid\nbreakdown; duration null; pre-merge\npredictive test (13 cond. $\\times$ 3 seeds)"]),
]
fig, ax = plt.subplots(figsize=(11.4, 5.4))
fig, ax = plt.subplots(figsize=(11.4, 5.3))
ax.set_axis_off()
ax.set_xlim(0, 1)
ax.set_ylim(0, 1)
x0, gap = 0.16, 0.008
x0, gap = 0.205, 0.008
cw = (1.0 - x0) / 3
row_h, row_top = 0.152, 0.79
row_h, row_top = 0.157, 0.805
ax.annotate("", xy=(0.995, 0.975), xytext=(x0 + 0.02, 0.975),
arrowprops=dict(arrowstyle="->", color="#555", lw=1.1))
ax.text(x0 + (1 - x0) / 2, 0.988, "the same population-genetic abstractions (Table 1), increasing realism",
ha="center", va="bottom", fontsize=8, style="italic", color="#333")
for j2, (name, arch, guarantee, edge, face) in enumerate(TIERS):
x = x0 + j2 * cw
ax.add_patch(FancyBboxPatch((x + gap, 0.825), cw - 2 * gap, 0.155,
boxstyle="round,pad=0.004", fc=edge, ec=edge, lw=1.4))
ax.text(x + cw / 2, 0.966, name, ha="center", va="top", fontsize=9.5,
fontweight="bold", color="white")
ax.text(x + cw / 2, 0.922, arch, ha="center", va="top", fontsize=6.8,
linespacing=1.3, color="white")
ax.text(x + cw / 2, 0.833, guarantee, ha="center", va="bottom", fontsize=6.4,
style="italic", color="white", alpha=0.92)
for j, (name, arch, guarantee, edge, face) in enumerate(TIERS):
x = x0 + j * cw
ax.add_patch(FancyBboxPatch((x + gap, 0.795), cw - 2 * gap, 0.16,
boxstyle="round,pad=0.004", fc=face, ec=edge, lw=1.4))
ax.text(x + cw / 2, 0.944, name, ha="center", va="top", fontsize=9, fontweight="bold", color=edge)
ax.text(x + cw / 2, 0.902, arch, ha="center", va="top", fontsize=6.8, linespacing=1.3)
ax.text(x + cw / 2, 0.803, guarantee, ha="center", va="bottom", fontsize=6.4,
style="italic", color="#555")
for i, (label, question, cells) in enumerate(ROWS):
y1 = row_top - i * row_h
for i2, (label, definition, cells) in enumerate(ROWS):
y1 = row_top - i2 * row_h
y0 = y1 - row_h + 2 * gap
yc = (y0 + y1) / 2
ax.text(0.0, yc + 0.012, label, ha="left", va="center", fontsize=8, fontweight="bold")
ax.text(0.0, yc - 0.022, question, ha="left", va="center", fontsize=6.8, style="italic", color="#555")
for j, cell in enumerate(cells):
x = x0 + j * cw
edge, face = TIERS[j][3], TIERS[j][4]
ax.text(0.0, y1 - 0.014, label, ha="left", va="top", fontsize=8, fontweight="bold")
ax.text(0.0, y1 - 0.054, definition, ha="left", va="top", fontsize=6.2,
style="italic", color="#555", linespacing=1.35)
for j2, cell in enumerate(cells):
x = x0 + j2 * cw
edge, face = TIERS[j2][3], TIERS[j2][4]
if cell is None:
ax.add_patch(FancyBboxPatch((x + gap, y0), cw - 2 * gap, y1 - y0,
boxstyle="round,pad=0.004", fc="white", ec="#bbbbbb",
@ -126,7 +129,6 @@ def fig1():
ax.text(x + cw / 2, yc, cell, ha="center", va="center", fontsize=6.4, linespacing=1.35)
save(fig, "fig1")
# ---------------------------------------------------------------- fig 2: grounding + MNIST
def fig2():
from knowledge.analysis import critical_grounding, reduce_to_stationary