Restructure: descriptive tier and experiment names, paper/manuscript

- paper/pnas -> paper/manuscript (venue-neutral)
- configs/layer1 -> configs/inheritance, src/knowledge -> src/inheritance
  (imported as `inheritance`), make layer1 -> make inheritance; layer2 alias dropped
- inheritance and trained-network bundles named after the manuscript figure
  they feed (fig2_grounding_sweep, figS3_rebaselining, ...), or descriptively
  where they feed none; configs keep their `experiment:` value so parquet
  hashes are unchanged, only output.dir moves
- figure scripts, SI figure sources, notebooks, REPRODUCING.md, README and the
  SI Methods/tables updated; make clean no longer deletes tracked manifests;
  reproduce.sh hashes the s{seed}/ layouts too

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Y64o8FKP7rCuXzC48pxpMm

results/fig2_mnist_collapse/README.md

@@ -0,0 +1,39 @@
+# mnist_collapse — collapse and grounding-rescue on REAL MNIST images (external validity)
+
+**Claim tested:** everything so far used a *synthetic* sandbox with a zero-error decoder oracle. Do
+model collapse and its rescue by grounding also appear on **real images** with a **classifier**
+oracle — i.e. is the effect real, not a synthetic artefact?
+
+**Setup (Layer 1.5, real-data tier).** The generative model is a **convolutional VAE** (the model
+in which generative collapse was first observed). Each generation a **fresh** VAE is trained from
+scratch on the previous VAE's own generated digits, plus a fraction `g` of fresh **real** MNIST
+images (grounding). `K = 30` **modes** = digit class × stroke-thickness bin (S=3), Zipf-resampled so
+the rarest ~18 modes form a real tail. The **oracle** is a frozen CNN (digit class) + deterministic
+thickness bin; its **mode accuracy ≈ 98.5%** (recorded in `manifest.json` with the full 30×30
+confusion matrix) is the measurement-noise floor. Two arms — dry (`g = 0`) vs grounded (`g = 0.1`) —
+`n = 6000` images/generation, 15 generations, 4 replicates.
+
+### Symbols
+- **mode** = (digit class, stroke-thickness bin); **`p*`** = Zipf truth over the 30 modes; **`p̂`** = the VAE's oracle-measured mode distribution.
+- **`g`** = grounding fraction (share of real MNIST images each generation). **forward-KL** = distance from truth; **support** = distinct modes alive; **`H`** = diversity; **tail truth-mass alive** = fraction of the rare tail retained.
+
+### The four panels (dry = red, grounded = green; band = 95% CI over 4 reps)
+1. **Forward-KL.** Dry climbs from ~0.5 to **~18** (the VAE drifts far from truth); grounded stays
+   near the floor. Collapse is real on images.
+2. **Support.** Dry collapses from all 30 modes to **~1** (the VAE ends up emitting a single blurry
+   mode); grounded holds all 30.
+3. **Tail truth-mass alive.** Dry's rare tail is wiped out (→ 0.06); grounded keeps the whole tail.
+4. **Heterozygosity.** Dry diversity → 0; grounded holds `H ≈ 0.9`.
+
+See **`mnist_montage.png`** for the eyeball version: gen-0 digits are varied and recognisable; by
+gen 12–15 the dry lineage has degenerated into one blurry blob.
+
+### Takeaway
+Model collapse and its arrest by a small dose of real data **reproduce on real MNIST images with a
+learned classifier oracle** — external validity for the whole Layer-1.5 story. Note the VAE needs
+~10% grounding here (vs ~5% for the synthetic histogram), consistent with the `grounding` finding
+that trained neural models need somewhat more grounding than the exact operator. This is
+**confirmation-only** (signs, not magnitudes; blueprint §3.5) — the exact synthetic oracle remains
+the anchor for every quantitative claim, and the oracle confusion matrix is the recorded noise floor.
+**Falsifier (not triggered):** if the dry VAE had shown no diversity loss, or grounding had failed to
+arrest it, the external-validity claim would fail.