Giorgio Gilestro
79bbc45f41
Confirms model collapse and its arrest by grounding on REAL images, not just the synthetic sandbox. A conv VAE (the canonical generative-collapse model) is retrained each generation on its own generated digits, with a fraction g of fresh real MNIST mixed in. Modes = digit class x stroke- thickness bin (K=30, Zipf, ~18 tail modes); the oracle is a frozen CNN + deterministic thickness at 98.5% mode accuracy (30x30 confusion matrix recorded in the manifest as the measurement-noise floor). Result (4 reps): dry (g=0) collapses to a single mode -- forward-KL 0.5->18, support 30->1, tail 1.0->0.06, H->0 -- while 10% grounding holds all 30 modes (KL~0.6, full tail, H~0.9). Signs, not magnitudes (blueprint 3.5); the exact synthetic oracle stays the quantitative anchor. The VAE needs ~10% grounding vs the synthetic histogram's ~5%, consistent with the grounding finding that trained nets need more than the exact operator. Plugs into the existing data-agnostic contract (metrics/grounding/output reused verbatim): mnist_data (thickness bins, class x thickness bijection, MnistSampler), mnist_oracle (ClassifierOracle + confusion matrix), mnist_vae (ConvVAEGenerator), mnist_loop (run_mnist_lineage), kind= mnist_lineage dispatch, MnistCfg/OracleCfg. Figures: plot_mnist (parquet- only) + mnist_montage (eyeball diagnostic showing digits degenerate to one blurry mode). make mnist / make env-mnist, kept out of the make neural loop. 99 tests green (+5 torchvision-gated). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
37 lines
1.7 KiB
Makefile
37 lines
1.7 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 test layer1 layer2 neural mnist 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
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for e in E1 E2 E3 E4 E5 E6; 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 "$$c" ;; esac; done
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mnist: ## run the real-MNIST confirmation tier (needs env-mnist; downloads MNIST)
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uv run python -m neural.experiment configs/neural/mnist_collapse.yaml
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layer2: neural ## alias: Layer 1.5 is the current Layer-2 deliverable (LLM rung deferred)
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figures: ## regenerate figures from committed results
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for e in E1 E2 E3 E4 E5 E6; do MPLBACKEND=Agg uv run python figures/plot_$$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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