Giorgio Gilestro
585264d0b4
Re-ran the specialist-merge experiment at a capable base (Qwen2.5-7B-Instruct, 200 tests/family) on one L40S GPU of Imperial's CX3 HPC (8 min walltime). The two caveats the 0.5B prototype left marginal are now resolved: - "exceeds every parent overall" is clean: both merges 0.87 vs best specialist 0.77 (+10 pts), and above every specialist on every family. - dilution vanishes: at 0.5B averaging diluted the lists-specialist (0.43->0.26); at 7B the merge beats it (0.62>0.57). Dilution was a small-model artefact -- a capable base composes rather than dilutes, which softens E4's "merge, don't average" once the parents are strong. The figure title is now data-driven (reports ">" for 7B, "~" for 0.5B). Adds the hpc/ smoke job script and the llm_merge walltime trim. Results synced to results/llm_merge_hpc/ (parquet gitignored per the reproducibility contract). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> |
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| hpc | ||
| paper | ||
| results | ||
| src | ||
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| .gitignore | ||
| CLAUDE.md | ||
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| 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)