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/architectures.yaml

@@ -1,54 +0,0 @@
-experiment: architecture_generality
-kind: gen_lineage
-seed: 20260704
-n_replicates: 5
-
-# (a new Layer-1.5 axis, no Layer-1 counterpart): is collapse ARCHITECTURE-GENERAL?
-# Run the same dry-vs-grounded protocol across three genuinely different learners that
-# share only the generative-collapse operator: the exact histogram (= Wright-Fisher, the
-# analytic anchor), an autoregressive GRU (recurrent), and an autoregressive MLP (feed-
-# forward). Expect the same SIGN in all: dry (g=0) loses diversity / forgets the tail;
-# grounding arrests it. Falsifier: the signs appear only for the histogram -> real neural
-# inductive biases break the Wright-Fisher mapping. (The sequence VAE is implemented but
-# excluded here: it does not clear the gen-0 fidelity gate on the Zipf-codeword task, so
-# its collapse would be confounded with underfitting; see tasks/todo.md.)
-generations: 22
-
-synthetic:
-  K: 256
-  R: 1
-  tail: zipf
-  zipf_s: 1.3
-  tail_frac: 0.5
-  tail_threshold: 1.0e-3
-  init: truth
-  style_len: 3
-  style_vocab: 5
-  id_base: 2
-
-model:
-  kind: rnn          # overwritten per arm by the model.kind sweep
-  hidden: 192
-  embed: 24
-  epochs: 25
-  lr: 2.0e-3
-  batch_size: 256
-  n_eval: 12000
-
-dynamics:
-  n: 200
-  grounding: {m: 0, policy: proportional}   # m overwritten per g by the sweep
-  remint: {enabled: false, period: null, H_gate: null}
-
-metrics:
-  kl_floor: 1.0e-9
-  support_eps: 1.0e-9
-
-sweep:
-  - param: model.kind
-    values: [histogram, rnn, mlp]
-  - param: g
-    values: [0.0, 0.05]
-
-output:
-  dir: results/architectures