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/inheritance/collapse_null.yaml

@@ -0,0 +1,32 @@
+experiment: E1_reproduce_collapse
+seed: 20260704
+n_replicates: 100
+generations: 600
+
+# Null model (blueprint 2.5-E1): no grounding, single teacher, no selection.
+# Starting from the truth makes the collapse narrative clean -- the tail is lost first,
+# support shrinks to 1, forward-KL diverges, and H decays geometrically at rate 1/n.
+# (The decay law E[H_t]=H_0(1-1/n)^t holds from any start; see the validation suite.)
+truth:
+  K: 500
+  R: 1
+  tail: zipf
+  zipf_s: 1.1
+  tail_frac: 0.5
+  tail_threshold: 1.0e-3
+  init: truth
+
+dynamics:
+  n: 100                 # distillation sample size = drift strength
+  teachers: {K_T: 1, rho: 0.0, q: 1.0}
+  grounding: {m: 0, policy: proportional}
+  selection: {mode: none, novelty_alpha: 0.0}
+  remint: {enabled: false, period: null, H_gate: null}
+
+metrics:
+  kl_floor: 1.0e-9
+  support_eps: 1.0e-9
+
+# No sweep: a single condition.
+output:
+  dir: results/collapse_null