Giorgio Gilestro
1721d047fa
Finishes the Layer 1 analytical core. All six experiments run with honest, publication-quality figures; 71 tests green. - E3 region-matched grounding: `grounding.exercised` knob + per-region tail survival. Matched holds the exercised region's tail (0.49) where uniform spreads thin and lets it collapse (0.07). - E4 multi-teacher recombination: `run_coverage` runner. Union coverage matches U(K_T,rho,q) exactly. Finding: mean-mixture distillation shows NO surviving benefit (a conservation law — 1/K_T dilution cancels the union gain); a union-preserving max-merge (M2N2-style) does. E4 reports both operators. - E5 QD vs greedy: greedy drives fixation (H~0.01); QD holds H at 0.48-0.88, rising with the novelty exponent. - E6 re-mint gate: `arm` multi-override sweep. Re-minting a collapsed lineage locks in divergence of KL-to-original; gating on diversity prevents it. - E2 analysis add-ons (from the companion work order, numbers verified): new analysis.py (reduce_to_stationary, critical_grounding with bootstrap CI -> g*=0.048, 95% CI [0.047,0.050]); tail_band_metrics + per-band logging; the E2 figure rebuilt as a 2x2 (defined g*+CI, g=0 flagged as a finite-time artifact, tail item-vs-mass, per-rarity-band panel). Uses truth-mass-weighted tail coverage rather than the raw (martingale) tail_mass. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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| configs/layer1 | ||
| figures | ||
| paper | ||
| results | ||
| src/knowledge | ||
| 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) and
paper/the-lamarckian-society-v4.md (the perspective paper).
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)