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Fig. 1B: the conceptual cartoon — from a society in space to a society in time

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The paper's key move drawn as a two-panel partner to the programme grid (now
Fig. 1A): on the left, the usual picture — contemporaries exchanging messages
(multi-agent systems, one moment on the clock); on the right, the same
ecosystem seen along its time axis — a pedigree in which a rare capability
(gold dot) is lost under single-parent inheritance, reassembled by merging
complementary parents, and re-supplied by grounding from a reality that can
say no (the globe). Friendly-robot glyphs, colour-coded capability dots;
build.py now stacks multiple PDFs per figure; the Introduction cites 1A and
1B in panel order.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01BkRLcc18rwT2Lysu6PbG7v
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@ -92,9 +92,12 @@ convention and by trial-and-error search. They are also, recognisably, machine l
problem at a new scale: *continual learning*, the struggle to acquire new abilities without losing old
ones (26, 27), transposed from a single network to a population whose members inherit from one
another. Population genetics, I will argue, prices these decisions. Table 1 summarises the
correspondences on which the argument runs, and Fig. 1 maps the experimental programme built on
correspondences on which the argument runs. Fig. 1A maps the experimental programme built on
them: the same abstractions tested at three tiers — an exact population-genetic simulator,
trained neural networks, and language models — with the sections that follow climbing that ladder.
Fig. 1B draws the shift of perspective the whole transfer rests on: the model ecosystem read not as
a society in space, contemporaries exchanging messages, but as a society in time, generations
coupled by inheritance, recombination, and immigration.
*(FIG:fig1)*