Giorgio Gilestro
5a23ddaf2a
llm_speciation (new kind; src/llm/speciation.py): E13 in LLM weights. LoRA children share the frozen base's coordinates, so merge failure is functional by construction. CONFLICT (ambiguous sort prompts learned under opposite conventions — the BDM structure): function-specific hybrid breakdown — merged coherence 0.02-0.08 falls below BOTH parents (~0.2) on the conflicted function; and in the de-confounded `add` design (private budget fixed, conflict added on top; 3 seeds after a single-seed pilot showed one anomalous point) the merge's private-family accuracy shows NO trend with conflict — the damage is surgical, not global. DURATION (over-trained disjoint specialists, 1->12 epochs): the merge improves (0.84->0.94) and stays above the best parent — the MLP "no emergent isolation" null generalises; relevant to the expert-training-duration report (2607.11997), with the epistasis prediction left to the decisive experiment. Multi-seed firm-up (seeds threaded into specialist caches; `seeds:` list support in the runner; fixed test sets): all three recombination claims hold with CIs — merges beat every specialist (5 seeds, ties 0.647±0.027 > best spec 0.592±0.009; worst-family 0.28 vs <=0.16); union 0.274±0.026 > fusion 0.174±0.102 on hard (3 seeds); directed 0.221±0.026 > soup. NEW finding: fusion is seed-FRAGILE where headroom exists (CI ±0.10) while routing/directed selection are stable (±0.026) — the union/selection operators win on reliability, not just mean. Figures (llm_speciation 3-panel; llm_seeds 3-panel with 95% CI), READMEs, +1 convention test (150 green), make llm-speciation / llm-seeds targets. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01BkRLcc18rwT2Lysu6PbG7v |
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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),
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)