Giorgio Gilestro
48181a1c84
Deepen the sexual-reproduction frame before entering the full society, on
the two facets GG chose: landscape robustness and directed recombination.
Adds a Kauffman NK landscape (genotype.nk_fitness, tunable ruggedness),
finite n-parent crossover (genotype.crossover, per-gap recombination rate),
and hill-climb (parents = local optima = trained models).
E9 (recomb_landscape) -- the "why sex?" test: E8's dramatic super-parent
result used an ADDITIVE landscape. On rugged/epistatic landscapes, blindly
recombining local optima causes OUTBREEDING DEPRESSION -- offspring fall
below the parents, worse with both ruggedness and recombination rate (K=8,
free recomb: ~ -0.23), and the optimal recombination rate shrinks as
ruggedness grows. Design rule: merge freely when skills are complementary/
additive; sparingly (and with selection) when entangled.
E10 (directed_sex) -- directed sex beats biological sex: biology is stuck
with 2 random-mating parents and no offspring preview; an AI can choose
complementary mates, evaluate many recombinant offspring, keep the fittest,
and use unbounded parents (iterated recombine-then-select). Random
("biological") sex craters with ruggedness (0.66->0.51); directed sex
tracks/exceeds the best parent at every ruggedness -- converting the
outbreeding-depression catastrophe into a win. No biological analog.
Complete sexual-transmission picture: dramatic super-parent offspring when
skills are complementary (E8); outbreeding-depression risk when entangled
(E9); directed sex resolves the risk (E10). configs/layer1/{E9,E10}.yaml,
figures/plot_{E9,E10}.py, READMEs, +5 tests (117 green).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
37 lines
1.8 KiB
Makefile
37 lines
1.8 KiB
Makefile
# Layer 1 + Layer 1.5 automation. The uv venv (built from the committed uv.lock) is the
|
|
# reproducibility source of truth; every target runs inside it via `uv run`.
|
|
|
|
.PHONY: env env-neural env-mnist test layer1 layer2 neural mnist figures clean
|
|
|
|
env: ## build .venv from the committed lockfile
|
|
uv sync --extra dev
|
|
|
|
env-neural: ## add the Layer 1.5 torch stack (GPU; Stage C onward)
|
|
uv sync --extra dev --extra neural
|
|
|
|
env-mnist: ## add torchvision for the real-MNIST confirmation tier
|
|
uv sync --extra dev --extra neural --extra mnist
|
|
|
|
test: ## correctness tests + scientific-validation tests (the spine of trust)
|
|
uv run pytest
|
|
|
|
layer1: ## run experiments E1-E6 + the learning-kernel bridge (analytic)
|
|
for e in E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 kernel_sharpen kernel_smooth; do uv run python -m knowledge.experiment configs/layer1/$$e.yaml; done
|
|
|
|
neural: ## run Layer 1.5 synthetic neural experiments (excludes the heavy MNIST tier)
|
|
for c in configs/neural/*.yaml; do case "$$c" in *mnist*) ;; \
|
|
*) uv run python -m neural.experiment "$$c" ;; esac; done
|
|
|
|
mnist: ## run the real-MNIST confirmation tier (needs env-mnist; downloads MNIST)
|
|
uv run python -m neural.experiment configs/neural/mnist_collapse.yaml
|
|
|
|
layer2: neural ## alias: Layer 1.5 is the current Layer-2 deliverable (LLM rung deferred)
|
|
|
|
figures: ## regenerate figures from committed results
|
|
for e in E1 E2 E3 E4 E5 E6; do MPLBACKEND=Agg uv run python figures/plot_$$e.py; done
|
|
for p in figures/plot_*.py; do case "$$p" in */plot_E[1-6].py|*/_*) ;; \
|
|
*) [ -e "$$p" ] && MPLBACKEND=Agg uv run python "$$p" ;; esac; done
|
|
|
|
clean: ## remove caches and generated results (keeps committed manifests)
|
|
rm -rf .pytest_cache **/__pycache__
|
|
find results -type f ! -name '.gitkeep' -delete 2>/dev/null || true
|