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