Giorgio Gilestro
ea051a5f92
E13c (the symmetry defense): alignment now runs modulo the FULL function-preserving unit symmetry group of a ReLU MLP (per-unit positive rescaling via canonicalise_scale, composed with Re-Basin permutations; sanity gate recovers a permuted-and-rescaled copy exactly). Verdict: the full group removes the independent-init barrier (residual 0.001) and essentially none of the conflict barrier (0.502 -> 0.497) — the residual is functional, not a missed symmetry (answers arXiv:2606.23607). The cliff gains a hybrid-fitness readout: merged accuracy 0.97 -> 0.03 with conflict. Floor proposition drafted (paper/si-notes.md S1): endpoint invariance + max(eps_A, eps_B) >= mu(S)/2 for any merged model under any alignment group. E13b (emergent divergence): pre-registered second reading — with NO conflicting training signal (disjoint class specialists; rolled-input conventions), residual is 0.000 at every divergence to t_div=3200, and the merge RESCUES the forgetting specialists (parents 0.535/0.474 -> merged 0.955; a sustained Fisher-Muller rescue at zero barrier). Speciation in real weights requires functional conflict; it does not emerge from compatible specialisation on shared ancestry. LLM-scale over-specialisation (cf. 2607.11997) deferred to Phase-3 llm_speciation. 3-panel figure, READMEs, +2 tests (149 green), make mnist wired. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01BkRLcc18rwT2Lysu6PbG7v
48 lines
2.5 KiB
Makefile
48 lines
2.5 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 "$$c" ;; esac; done
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mnist: ## run the torchvision tiers: MNIST collapse + E13 real-weight speciation (needs env-mnist)
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uv run python -m neural.experiment configs/neural/mnist_collapse.yaml
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uv run python -m neural.experiment configs/neural/speciation_real.yaml
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uv run python -m neural.experiment configs/neural/speciation_real_cliff.yaml
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uv run python -m neural.experiment configs/neural/speciation_real_emergent.yaml
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env-llm: ## add the LLM stack for the Layer-2 prototype (GPU; transformers/peft)
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uv sync --extra dev --extra neural --extra llm
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llm: ## run the LLM prototypes: merge (fusion) + moe (union) + directed (offspring select)
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uv run python -m llm.experiment configs/llm/merge.yaml
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uv run python -m llm.experiment configs/llm/moe.yaml
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uv run python -m llm.experiment configs/llm/directed.yaml
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layer2: neural ## alias: Layer 1.5 is the current Layer-2 deliverable (LLM rung deferred)
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figures: ## regenerate figures from committed results
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for e in E1 E2 E3 E4 E5 E6; do MPLBACKEND=Agg uv run python figures/plot_$$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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