Layer 1.5: architecture-general neural existence proof

Re-scopes Layer 2 into a cheaper, architecture-general neural collapse proof
before the LLM rung. Realises the same Wright–Fisher abstractions in real trained
generative models on a fully-synthetic sandbox with an exact oracle, reusing
knowledge.metrics/truth/seeding and the output contract so neural curves overlay
the Layer-1 analytic curves.

  - src/neural/: synthetic token-grammar sandbox (lossless identity + stochastic
    style), ExactOracle, HistogramModel bridge, generation loop, experiment runner
  - HARD GATE passed: histogram lineage reproduces Layer 1 exactly (neutral decay,
    exact H_eq, tracks run_lineage) — tests/test_neural_validation.py
  - torch models: autoregressive RNN + MLP (VAE implemented, not yet fidelity-
    passing); determinism seeding derived from the SeedSequence stream
  - N0 bridge (neural g*=0.047 ≈ Layer-1 0.048), N1 collapse-in-weights, N2 phase
    boundary, N5 architecture-generality (collapse + grounding-rescue in histogram
    + RNN + MLP). Manifests/configs committed; parquet gitignored, hashes tracked
  - additive backward-compatible save_artifacts extension; Makefile neural targets

Finding: neural smoothing partially resists H-collapse, so forward-KL and tail
survival are the sharp neural collapse metrics (H is smooth, per Layer 1).

92 tests green. Remaining (tasks/todo.md): N4 merge, N2 refine, N3/N6, VAE
fidelity, MNIST tier, figures. LLM/LoRA rung and C3 deferred.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

pyproject.toml

@@ -16,15 +16,22 @@ dependencies = [
 
 [project.optional-dependencies]
 dev = ["pytest>=8.0"]
+# Layer 1.5 neural existence proof. Torch is only needed from Stage C (RNN/VAE/MLP);
+# Stages A-B (synthetic sandbox + histogram bridge) are pure NumPy and run in the base env.
+# The default PyPI torch wheel is CUDA-enabled (cu13, matching the RTX A4000 driver).
+# Install with `uv sync --extra neural`.
+neural = ["torch>=2.2"]
+# The real-MNIST secondary-confirmation tier only. Install with `uv sync --extra mnist`.
+mnist = ["torchvision>=0.17"]
 
 [build-system]
 requires = ["hatchling"]
 build-backend = "hatchling.build"
 
 # src-layout: src/knowledge/ is importable as `knowledge` (the normative package
-# name the scientific-validation conformance tests import).
+# name the scientific-validation conformance tests import). src/neural/ is Layer 1.5.
 [tool.hatch.build.targets.wheel]
-packages = ["src/knowledge"]
+packages = ["src/knowledge", "src/neural"]
 
 [tool.pytest.ini_options]
 testpaths = ["tests"]