Giorgio Gilestro
4e86d8b602
Folds the four LLM runs into both papers now that the arc is fully characterised.
results-summary.md: new section 5 "The claims tested in real LLM weights" — the
Fisher-Muller generalist (7B merge 0.87 > best specialist 0.77), union-vs-fusion
(routing beats averaging where there is headroom), directed sex (breed + select),
and the unifying HEADROOM law that resolves the earlier saturation confound.
Updates design rule 2 ("merge, don't average — where there is headroom"), adds a
plain-language point 8, refreshes the validation counts (131 tests, real-LLM tier).
the-lamarckian-society-v5.md: softens the three "not a language model yet" claims
to acknowledge the prototype; adds the real-LLM confirmation after the
Fisher-Muller and directed-sex claims (with the headroom caveat as a sharpening,
not a weakening); adds an LLM bullet to section 13; reframes the closing gap to
"the operators, checked; the living society, next."
Tone held to the accessibility/honesty bar: prototype scope flagged (signs not
magnitudes), caveats presented as sharpening the claims, nothing over-celebrated.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
|
||
|---|---|---|
| configs | ||
| figures | ||
| hpc | ||
| paper | ||
| results | ||
| src | ||
| tasks | ||
| 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)