Giorgio Gilestro
e2981c845d
Two insertions answering the editor's implicit questions. Introduction gains "stated as a problem": the four recurring decisions a model- population operator cannot currently answer from principle (replay budget; compose-or-damage; pre-merge detection; separate-vs-consolidate) and the framework's counterintuitive answers — averaging cancels the multi-parent benefit exactly in the tail regime; specialisation/ divergence produced no incompatibility anywhere tested while conflicting conventions always did; weight distance carried no predictive signal while cheap behavioural disagreement did; and the theory's numbers land on the replay constants practice converged to independently (unexpectedness + problem-solved + external check, at reviewer-hardened calibration). Discussion gains a closing "Outlook: the evolution of language models": the ecosystem's trajectory is consolidating on exactly these operators, and the framework's fork is concrete and measurable — either models stay freely recombinable (one interbreeding population; levers = per-capability grounding budgets + deliberate diversity) or long-horizon specialisation at scale begins emergent speciation (a routed archipelago of diverging lineages), decidable now with the pre-merge conflict instruments tested here. Design rules tie back to the posed decisions. 5.2k words, citation invariant intact, 20 pp. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01BkRLcc18rwT2Lysu6PbG7v |
||
|---|---|---|
| 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)