"""Layer 1.5 — the architecture-general neural existence proof.

Realises the Layer-1 (``knowledge``) Wright-Fisher abstractions in *real trained
generative models* on a fully-synthetic sandbox whose ground-truth ``p*`` is known
exactly. A model's knowledge is measured as its output distribution over ``K`` discrete
*modes* (via an oracle), so the same metrics (``knowledge.metrics``), the same closed
forms, and the same experiments carry over — a neural collapse curve can be overlaid on
a Layer-1 analytic curve.

The package is staged by cost: the histogram model (pure NumPy) reduces this layer
*exactly* to Layer 1 and is the validation bridge; the RNN/VAE/MLP models (torch, added
from Stage C) show that collapse is architecture-general.
"""