Giorgio Gilestro
6bca1db61e
Rewrite the-lamarckian-society-v4.md -> v5, making sexual reproduction the central engine and lifting accessibility for a cross-disciplinary audience (geneticists, ML engineers, neuroscientists) — every field's jargon glossed inline, big picture kept legible, measured tone, honest about scope (the evidence is from minimal models, not LLMs). Core reframe (section 5): single-teacher distillation is asexual copying, which collapses (Muller's ratchet = model collapse); the cure is to ground every birth in reality and to reproduce SEXUALLY — recombining many complementary parents so offspring can exceed any parent (Fisher-Muller). Adds the honest limits (outbreeding depression on entangled skills) and the distinctly-AI advantage (directed sex: choose mates, screen offspring, unbounded parents). Turns the old "a companion paper will..." into "what we built and found," folding in the demonstrated results (E1-E11, neural + MNIST, the learning kernel) and the Riis positioning (concede collapse=drift as prior art; claim the control-theory cure). Update CLAUDE.md / README references; results-summary noted alongside. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> |
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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)