Giorgio Gilestro
56f642e7f9
Reframe of v5 into a population-genetic control theory for agent societies (leads with evolution-of-sex, concedes collapse=drift up front), positioned against the 2025-26 landscape (Multiagent-Finetuning, GENOME, M2N2, DGM, Pari 2024, Zhou 2026, Git Re-Basin) with an explicit concede/own ledger. Folds in E12 as the headline NEW modelling result: a dedicated 'The limit of sex: model speciation' section (compatible -> outbreeding depression -> hybrid inviability; the isolation cliff set by epistasis not divergence alone; the Orr-Turelli snowball; the route-don't- merge design rule), threaded through the abstract (5th load-bearing claim) and the what's-ours ledger. New draft file; v5 preserved. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> |
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| hpc | ||
| paper | ||
| results | ||
| src | ||
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| tests | ||
| .gitignore | ||
| CLAUDE.md | ||
| Makefile | ||
| 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)