neural: real-MNIST external-validity tier (collapse + grounding)

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>

tasks/todo.md

@@ -230,7 +230,22 @@ C3 vertical claim deferred.*
   while mean-distill stays flat (analytic + trained + rho=1 control). **grounding**: the reframed
   4-panel (forward-KL phase boundary, recovery, metric-choice).
 
-## Remaining
+**2026-07-05 — real-MNIST external-validity tier (`mnist_collapse`).**
+
+- New image tier plugged into the existing contract (metrics/grounding/output are data-agnostic and
+  reused verbatim): `mnist_data.py` (load, per-class thickness bins, mode = class×thickness bijection,
+  `MnistSampler`), `mnist_oracle.py` (frozen CNN + deterministic thickness = `ClassifierOracle`,
+  confusion matrix), `mnist_vae.py` (`ConvVAEGenerator`), `mnist_loop.py` (`run_mnist_lineage`), plus
+  `kind=mnist_lineage` dispatch in `experiment.py`, `configs/neural/mnist_collapse.yaml`,
+  `figures/plot_mnist.py`, `figures/mnist_montage.py` (eyeball diagnostic), `MnistCfg`/`OracleCfg`.
+- **Gates:** CNN mode accuracy **98.5%** (30×30 confusion matrix in the manifest = noise floor);
+  VAE gen-0 recovers full 30/30 support (over-smooths freq, KL≈0.5, no prior hole).
+- **Result (4 reps):** dry (g=0) VAE **collapses to a single mode** (KL 0.5→18, support 30→1, tail
+  1.0→0.06, H→0); **g=0.1 holds all 30 modes** (KL≈0.6, full tail, H≈0.9). Collapse + grounding-rescue
+  confirmed on real images. VAE needs ~10% grounding vs synthetic ~5% (cf. the `grounding` finding).
+  **99 tests green** (+5 torchvision-gated). `make mnist` / `make env-mnist` (kept out of `make neural`).
+
+## Remaining (all optional)
 
 - [ ] **`region_matched`** grounding (R>1), **`remint`** re-mint gate (optional).
 - [ ] **VAE fidelity:** fix the prior-hole mismatch (KL-annealing / free-bits / larger latent) so it