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

configs/neural/mnist_collapse.yaml

@@ -1,59 +0,0 @@
-experiment: mnist_collapse
-kind: mnist_lineage
-seed: 20260705
-n_replicates: 4
-
-# (Layer 1.5 external-validity tier; maps to Layer-1 E1/E2 and neural collapse/grounding): does
-# model collapse — and its rescue by grounding — appear on REAL MNIST images, not just the
-# synthetic sandbox? A convolutional VAE (the canonical model in which generative collapse was
-# first observed) is retrained each generation on the previous VAE's own generated images, plus a
-# fraction g of fresh REAL MNIST images (grounding). Modes = digit class x stroke-thickness bin
-# (K=30) resampled to a Zipf p*; a frozen CNN + deterministic thickness is the oracle (its
-# confusion matrix, recorded in the manifest, is the measurement-noise floor). Expect (per E1/E2):
-# the dry arm (g=0) collapses — rare modes die, forward-KL climbs, support shrinks — while a
-# grounded arm holds the tail. This is confirmation-only: SIGNS, not magnitudes (blueprint 3.5);
-# the exact synthetic oracle remains the anchor for every quantitative comparison. Falsifier: the
-# dry VAE shows no diversity loss, or grounding fails to arrest it.
-
-mnist:
-  K: 30
-  n_classes: 10
-  style_bins: 3            # K = 10 classes x 3 stroke-thickness bins
-  R: 1
-  tail: zipf
-  zipf_s: 1.5             # the rarest ~18/30 modes form a real tail (~9% of the mass)
-  tail_threshold: 1.0e-2
-  init: truth
-  data_root: data
-
-model:
-  kind: convvae
-  latent: 32
-  epochs: 30
-  lr: 1.0e-3
-  batch_size: 256
-  beta: 1.0
-  n_eval: 10000          # generate-and-classify samples for the mode-distribution readout
-
-oracle:
-  epochs: 5              # frozen digit CNN (~98.5% mode accuracy = the noise floor)
-  lr: 1.0e-3
-  batch_size: 256
-  cache: models/mnist_cnn.pt
-
-dynamics:
-  n: 6000                # images the pupil VAE sees per generation (drift strength)
-  grounding: {m: 0, policy: proportional}   # m overwritten per g by the sweep
-
-generations: 15
-
-metrics:
-  kl_floor: 1.0e-9
-  support_eps: 1.0e-9
-
-sweep:
-  - param: g
-    values: [0.0, 0.1]   # dry vs grounded (VAE collapse is strong; needs ~10% real, cf. grounding)
-
-output:
-  dir: results/mnist_collapse