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
57 lines
3 KiB
Makefile
57 lines
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 E14 kernel_sharpen kernel_smooth; do uv run python -m knowledge.experiment configs/layer1/$$e.yaml; done
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neural: ## run Layer 1.5 synthetic neural experiments (excludes the MNIST/torchvision tiers)
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for c in configs/neural/*.yaml; do case "$$c" in *mnist*|*speciation_real*) ;; \
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*) uv run python -m neural.experiment "$$c" ;; esac; done
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mnist: ## run the torchvision tiers: MNIST collapse + E13 real-weight speciation (needs env-mnist)
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uv run python -m neural.experiment configs/neural/mnist_collapse.yaml
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uv run python -m neural.experiment configs/neural/speciation_real.yaml
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uv run python -m neural.experiment configs/neural/speciation_real_cliff.yaml
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uv run python -m neural.experiment configs/neural/speciation_real_emergent.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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llm-speciation: ## LLM-tier speciation: conflict cliff (replace + de-confounded add) and duration null
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uv run python -m llm.experiment configs/llm/speciation.yaml
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uv run python -m llm.experiment configs/llm/speciation_add.yaml
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llm-seeds: ## multi-seed firm-up (heavy): merge x5, moe-hard x3, directed-hard x3
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uv run python -m llm.experiment configs/llm/merge_seeds.yaml
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uv run python -m llm.experiment configs/llm/moe_hard_seeds.yaml
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uv run python -m llm.experiment configs/llm/directed_hard_seeds.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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