Giorgio Gilestro
db9452c9d4
New analytic result for the evolution-of-sex paper: how far can two lineages diverge before recombination (model merging) stops working? Frames merge failure as biological reproductive isolation via Bateson-Dobzhansky-Muller incompatibilities. src/knowledge/speciation.py, kind: speciation, on the E7-E11 genotype machinery (pure seeded NumPy, bitwise-reproducible; no external simulator whose separate RNG would break that). - BDM construction (E12.yaml): ancestor + two lineages substituting disjoint loci (each parent adaptive, incompatibility-free), a fraction rho of cross-lineage pairs are BDMIs. Sweeping divergence d reproduces the predicted compatible -> outbreeding depression -> hybrid inviability curve; the isolation cliff moves to lower d as epistasis density rises (iso at d=20: 0.00/0.03/0.50 for rho 0.1/0.25/0.5); incompatibilities snowball ~ (d/2)^2 (Orr-Turelli). - NK variant (E12_nk.yaml): parents = hill-climbed local optima; the epistasis wedge — recombination gain flips 0 -> -0.13 and OD rate 0 -> 0.90 as ruggedness K rises. At matched divergence, mergeability is governed by epistasis, the axis no divergence-only ML merge predictor captures. plot_E12.py (3-panel), +7 tests (138 green), README with honest positioning (concedes the empirical phenomenon to Pari 2024 / Zhou 2026 + permutation artefacts to Git Re-Basin; claims the predictive theory + the epistasis wedge). Wired into make layer1. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> |
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| CLAUDE.md | ||
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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)