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Fig. 1A: pastel palette (green biology, two blues for AI) + Mendel peas and robot icons

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Per GG: the biological model goes pastel green (nature), the two AI tiers
light and darker pastel blue; header text switches from white to the dark
shade of each hue (white is unreadable on pastel). A green-pea icon honours
Mendel in the biology header; a friendly robot marks the two AI headers.
Icons are Flaticon #2347052 and #10479785, used under GG's paid Flaticon
licence (noted in make_figs.py); header text is centred slightly left of a
dedicated icon slot so nothing collides. Caption colour sentence updated
(green vs blues).

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 13:23:35 +01:00
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paper/pnas/body.tex
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@ -21,7 +21,7 @@ An operator of a model population faces recurring decisions for which there is n
\begin{figure*}[p]\centering % fig1
\includegraphics[width=\textwidth]{figs/fig1a.pdf}\\[6pt]
\includegraphics[width=\textwidth]{figs/fig1b.pdf}
\caption{(A) The experimental programme. Each population-genetic abstraction (Table 1) is tested at up to three tiers, ordered left to right by increasing realism: a biological model (a 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). Colour separates the two categories: the biological model in blue, the two AI-model tiers in oranges. The same abstractions are carried across all three. Rows are the framework's mechanisms, each defined at the left margin; filled cells name the experiments run at each tier, and each carries, in its corner, the figure or table where that result is reported, so this figure doubles as a map of the paper. Each claim is tested at the cheapest tier that can falsify it, and a costlier tier is entered only where it adds a discriminating test rather than a replication: grounding at language-model scale is established in prior work (21, 30) and is not re-run; epistasis and the society skip the middle tier, whose distinctive value (exact oracles) does not bear on those operator-level questions; and the society at language-model scale is the integrative experiment this paper specifies but does not run --- its stated gap. (B) The conceptual basis of the transfer. A population of models is usually pictured as a society in space: contemporaries exchanging messages. The couplings this paper studies run instead between generations --- training on model output (inheritance), weight-space merging (recombination), verified real data entering each generation (immigration from reality) --- a society in time, the object population genetics was built to describe. Dots mark capabilities: the rare one (gold) is lost under single-parent inheritance, reassembled by merging complementary parents, and re-supplied by grounding.}\label{fig1}
\caption{(A) The experimental programme. Each population-genetic abstraction (Table 1) is tested at up to three tiers, ordered left to right by increasing realism: a biological model (a 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). Colour separates the two categories: the biological model in green, the two AI-model tiers in blues. The same abstractions are carried across all three. Rows are the framework's mechanisms, each defined at the left margin; filled cells name the experiments run at each tier, and each carries, in its corner, the figure or table where that result is reported, so this figure doubles as a map of the paper. Each claim is tested at the cheapest tier that can falsify it, and a costlier tier is entered only where it adds a discriminating test rather than a replication: grounding at language-model scale is established in prior work (21, 30) and is not re-run; epistasis and the society skip the middle tier, whose distinctive value (exact oracles) does not bear on those operator-level questions; and the society at language-model scale is the integrative experiment this paper specifies but does not run --- its stated gap. (B) The conceptual basis of the transfer. A population of models is usually pictured as a society in space: contemporaries exchanging messages. The couplings this paper studies run instead between generations --- training on model output (inheritance), weight-space merging (recombination), verified real data entering each generation (immigration from reality) --- a society in time, the object population genetics was built to describe. Dots mark capabilities: the rare one (gold) is lost under single-parent inheritance, reassembled by merging complementary parents, and re-supplied by grounding.}\label{fig1}
\end{figure*}
\section*{The biological model, and where trained learners depart from it}