Giorgio Gilestro
d22dd9d535
src/neural/recombine.py mirrors Layer-1 run_coverage but trains K_T specialist RNNs on assignments from the exact shared-switch retention construction (K_T/rho/q clean; union matches the closed form), then recombines the measured teacher distributions two ways: mean (naive pooling) vs oracle-guided max-merge (per-mode strongest teacher, M2N2-style), each followed by size-n resampling. Result (8 reps): at rho=0, union rises 0.49->0.96 (supply matches closed form); analytic surviving_max rises 0.043->0.087 while surviving_mean stays flat ~0.045 — the conservation law (averaging cancels the union gain, max-merge realises it). At rho=1 (identical teachers) union and max are flat. The lesson holds in the neural setting; trained-weight columns show the same signs but noisier (smoothing inflates baseline; deep tail barely clears n=200 resampling). torch-gated test added. 93 tests green. Co-Authored-By: Claude Opus 4.8 (1M context) <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) 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)