Giorgio Gilestro
6f1f8bf172
All 66 references renumbered to first-appearance order (programmatically verified: in-text sequence = 1..66 = list order; ranges expanded, remapped, recompressed) and rewritten in PNAS style (initials-first authors with the >5 -> et-al rule, sentence-case titles, abbreviated italic venues, bold volumes, year-at-end, arXiv [Preprint] + 10.48550 DOIs). Correctness: 47 arXiv ids batch-verified against the arXiv API (title/first-author/year); caught and fixed an authorless GENOME entry (Y. Zhang et al.), "Sakana AI" -> J. Abrantes et al., a wrong Kotha id (2310.05719, a different paper -> 2309.10105), Nemotron's corporate author, and Liang's truncated title. Also: six load-bearing refs that lost their in-text anchors during the restructure re-anchored (NK, QD, Pari, LoRA, Sharma, Kozodoi), one real mis-citation fixed (Self-Instruct credited to Multiagent-Finetuning; new ref added), and four figure captions in build.py brought up to third-review calibration (operational grounding threshold; first-order conservation; complementary-contributions society; permutation-and-rescaling alignment). 20-pp rebuild clean. 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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| pyproject.toml | ||
| 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)