Giorgio Gilestro
db9452c9d4
New analytic result for the evolution-of-sex paper: how far can two lineages diverge before recombination (model merging) stops working? Frames merge failure as biological reproductive isolation via Bateson-Dobzhansky-Muller incompatibilities. src/knowledge/speciation.py, kind: speciation, on the E7-E11 genotype machinery (pure seeded NumPy, bitwise-reproducible; no external simulator whose separate RNG would break that). - BDM construction (E12.yaml): ancestor + two lineages substituting disjoint loci (each parent adaptive, incompatibility-free), a fraction rho of cross-lineage pairs are BDMIs. Sweeping divergence d reproduces the predicted compatible -> outbreeding depression -> hybrid inviability curve; the isolation cliff moves to lower d as epistasis density rises (iso at d=20: 0.00/0.03/0.50 for rho 0.1/0.25/0.5); incompatibilities snowball ~ (d/2)^2 (Orr-Turelli). - NK variant (E12_nk.yaml): parents = hill-climbed local optima; the epistasis wedge — recombination gain flips 0 -> -0.13 and OD rate 0 -> 0.90 as ruggedness K rises. At matched divergence, mergeability is governed by epistasis, the axis no divergence-only ML merge predictor captures. plot_E12.py (3-panel), +7 tests (138 green), README with honest positioning (concedes the empirical phenomenon to Pari 2024 / Zhou 2026 + permutation artefacts to Git Re-Basin; claims the predictive theory + the epistasis wedge). Wired into make layer1. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
45 lines
2.3 KiB
Makefile
45 lines
2.3 KiB
Makefile
# Layer 1 + Layer 1.5 automation. The uv venv (built from the committed uv.lock) is the
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# reproducibility source of truth; every target runs inside it via `uv run`.
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.PHONY: env env-neural env-mnist env-llm test layer1 layer2 neural mnist llm figures clean
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env: ## build .venv from the committed lockfile
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uv sync --extra dev
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env-neural: ## add the Layer 1.5 torch stack (GPU; Stage C onward)
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uv sync --extra dev --extra neural
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env-mnist: ## add torchvision for the real-MNIST confirmation tier
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uv sync --extra dev --extra neural --extra mnist
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test: ## correctness tests + scientific-validation tests (the spine of trust)
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uv run pytest
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layer1: ## run experiments E1-E6 + the learning-kernel bridge (analytic)
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for e in E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 E11 E12 E12_nk kernel_sharpen kernel_smooth; do uv run python -m knowledge.experiment configs/layer1/$$e.yaml; done
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neural: ## run Layer 1.5 synthetic neural experiments (excludes the heavy MNIST tier)
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for c in configs/neural/*.yaml; do case "$$c" in *mnist*) ;; \
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*) uv run python -m neural.experiment "$$c" ;; esac; done
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mnist: ## run the real-MNIST confirmation tier (needs env-mnist; downloads MNIST)
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uv run python -m neural.experiment configs/neural/mnist_collapse.yaml
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env-llm: ## add the LLM stack for the Layer-2 prototype (GPU; transformers/peft)
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uv sync --extra dev --extra neural --extra llm
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llm: ## run the LLM prototypes: merge (fusion) + moe (union) + directed (offspring select)
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uv run python -m llm.experiment configs/llm/merge.yaml
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uv run python -m llm.experiment configs/llm/moe.yaml
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uv run python -m llm.experiment configs/llm/directed.yaml
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layer2: neural ## alias: Layer 1.5 is the current Layer-2 deliverable (LLM rung deferred)
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figures: ## regenerate figures from committed results
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for e in E1 E2 E3 E4 E5 E6; do MPLBACKEND=Agg uv run python figures/plot_$$e.py; done
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for p in figures/plot_*.py; do case "$$p" in */plot_E[1-6].py|*/_*) ;; \
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*) [ -e "$$p" ] && MPLBACKEND=Agg uv run python "$$p" ;; esac; done
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clean: ## remove caches and generated results (keeps committed manifests)
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rm -rf .pytest_cache **/__pycache__
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find results -type f ! -name '.gitkeep' -delete 2>/dev/null || true
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