Giorgio Gilestro
96902e87f0
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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| hpc | ||
| paper | ||
| results | ||
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| .gitignore | ||
| CLAUDE.md | ||
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| README.md | ||
| uv.lock | ||
The Lamarckian Society — Layer 1 (analytical core)
A parametric population-genetics model of knowledge transmission across generations of
learning agents. Knowledge transmission is modelled literally as a Wright–Fisher
process (not by analogy): a model's knowledge is a distribution p_t over K discrete
items; a fixed true distribution p* has a rare tail; each generational step is
"sample from the parent (drift) + mix in fresh real samples (grounding/immigration) +
refit." Model collapse is the loss of rare alleles under drift.
See paper/blueprint.md (the normative build spec),
paper/the-lamarckian-society-v5.md (the perspective paper), and
paper/results-summary.md (a summary of all results).
Reproduce
Environment is a uv venv built from the committed, hash-pinned uv.lock — that
lockfile is the single source of truth for "it runs" (Layer 1 is pure NumPy/SciPy and
bitwise-reproducible from a seed; no container needed).
# one-time: install uv (https://astral.sh/uv)
curl -LsSf https://astral.sh/uv/install.sh | sh
uv sync # build .venv from uv.lock
make test # correctness + scientific-validation tests (the spine of trust)
make layer1 # run experiments E1–E6
make figures # regenerate figures from committed results
Layout
src/knowledge/ Layer 1 package (imported as `knowledge`)
configs/layer1/ one YAML per experiment (E1..E6)
figures/ plot_EX.py — read results.parquet only
tests/ test_correctness.py + test_scientific_validation.py (analytic checks)
paper/ blueprint.md, perspective paper, figure_manifest.md
results/ written artifacts (gitignored; hashes tracked in manifest.json)