Giorgio Gilestro
d6a5c5cacd
Speciation section rewritten around the hardened results: alignment modulo the full function-preserving symmetry group (answers 2606.23607 preemptively), the hybrid-fitness cliff (0.97 -> 0.03), the mu(S)/2 floor, and the pre-registered emergent converse (no isolation without functional conflict; the merge rescues forgetting specialists) — in the abstract, §5, §13 ledger, and the accessible version. Citation refresh (author names verified via arXiv API): concede First-Extinction Law (Benati 2509.20101) and quantitative-trait collapse (Yoon 2407.17493) alongside Riis; add verifier-injection (Yi 2510.16657), Livnat & Papadimitriou (CACM 2016) as the sex-as-computation precursor, and the adjacent 2024-26 merge/LMC/multi-agent literature (Ainsworth, Pari, Zhou, Cao, Sharma, Hu, Kozodoi, Li & Shen, Harris, Chen, Tanaka). arXiv package (paper/arxiv/): md2tex.py — a small block-based Markdown->LaTeX converter keeping the Markdown as source of truth — main.tex, generated body.tex, 3 vector figures; builds clean under tectonic (20 pp; pdflatex hint guarded for arXiv); ARXIV-SUBMISSION.md carries categories, license note, and a <=1,920-char abstract. 149 tests green. 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)