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

@@ -29,4 +29,4 @@ metrics:
 
 # No sweep: a single condition.
 output:
-  dir: results/E1
+  dir: results/collapse_null

configs/inheritance/fig2_grounding_sweep.yaml

@@ -33,4 +33,4 @@ sweep:
     values: [0.0, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.4]
 
 output:
-  dir: results/E2
+  dir: results/fig2_grounding_sweep

configs/inheritance/fig4_society_ablation.yaml

@@ -37,4 +37,4 @@ sweep:
       - {name: no_sex,       set: {society.sex: false}}
       - {name: no_diversity, set: {society.select: greedy, society.novelty: 0.0}}
 
-output: {dir: results/E11}
+output: {dir: results/fig4_society_ablation}

configs/inheritance/fig5_speciation_bdm.yaml

@@ -25,4 +25,4 @@ speciation:
   n_offspring: 500
 
 output:
-  dir: results/E12
+  dir: results/fig5_speciation_bdm

configs/inheritance/figS10_rugged_landscapes.yaml

@@ -24,4 +24,4 @@ sweep:
   - param: rate
     values: [0.0, 0.05, 0.1, 0.2, 0.35, 0.5]   # clonal -> free recombination
 
-output: {dir: results/E9}
+output: {dir: results/figS10_rugged_landscapes}

configs/inheritance/figS11_directed_recombination.yaml

@@ -24,4 +24,4 @@ sweep:
   - param: K
     values: [2, 4, 6, 8, 10]       # landscape ruggedness (all with parent diversity)
 
-output: {dir: results/E10}
+output: {dir: results/figS11_directed_recombination}

configs/inheritance/figS12_quality_diversity.yaml

@@ -36,4 +36,4 @@ sweep:
     values: [0.5, 1.0, 2.0]
 
 output:
-  dir: results/E5
+  dir: results/figS12_quality_diversity

configs/inheritance/figS13_mating_breadth.yaml

@@ -31,4 +31,4 @@ sweep:
   - param: mating.breadth
     values: [0.03, 0.08, 0.17, 0.35, 0.6, 1.0]
 
-output: {dir: results/E14}
+output: {dir: results/figS13_mating_breadth}

configs/inheritance/figS2_kernel_sharpen.yaml

@@ -6,7 +6,7 @@ n_replicates: 24
 # (Learning-kernel bridge, pro-collapse arm): does neutral Wright-Fisher explain the VAE's
 # collapse on MNIST? NO -- and that is the point. This matches the MNIST regime (K=30, n=6000,
 # Zipf) where drift is nearly inert: neutral (temperature=1.0) barely moves (H stays ~H*, ~all
-# modes alive), yet the real VAE collapsed to a SINGLE mode (results/mnist_collapse). Adding the
+# modes alive), yet the real VAE collapsed to a SINGLE mode (results/fig2_mnist_collapse). Adding the
 # estimator's sharpening / mode-competition (temperature<1: p ~ p^(1/tau)) reproduces the
 # catastrophic collapse. tau=0.8 is calibrated to reproduce collapse-to-one-mode. This is the
 # axis Riis (2026) names as future work: the estimator, not the sampling, drives VAE collapse.
@@ -26,4 +26,4 @@ sweep:
   - param: dynamics.kernel.temperature
     values: [1.0, 0.8]         # neutral (no collapse) vs sharpened (catastrophic collapse)
 
-output: {dir: results/kernel_sharpen}
+output: {dir: results/figS2_kernel_sharpen}

configs/inheritance/figS2_kernel_smooth.yaml

@@ -6,7 +6,7 @@ n_replicates: 24
 # (Learning-kernel bridge, anti-collapse arm): neutral Wright-Fisher OVER-predicts the RNN's
 # collapse. This matches the RNN grounding regime (K=256, n=200, Zipf): neutral (reset=0) drives
 # H all the way to 0, but the real RNN only PARTIALLY collapses -- H plateaus ~0.68 of a possible
-# 0.88, forward-KL plateaus ~2 (does not diverge), ~half the tail stays alive (results/grounding).
+# 0.88, forward-KL plateaus ~2 (does not diverge), ~half the tail stays alive (results/figS6_grounding_rnn).
 # The estimator's smoothing / regularisation supplies a diversity FLOOR. A mutation-toward-prior
 # knob (reset=u: p <- (1-u)p + u*uniform) reproduces the H-floor. reset=0.006 is calibrated to the
 # RNN's stationary dry H. Honest caveat carried in the write-up: uniform-mutation matches the
@@ -29,4 +29,4 @@ sweep:
   - param: dynamics.kernel.reset
     values: [0.0, 0.006]       # neutral (H -> 0) vs smoothed (H floors, like the RNN)
 
-output: {dir: results/kernel_smooth}
+output: {dir: results/figS2_kernel_smooth}

configs/inheritance/figS3_rebaselining.yaml

@@ -43,4 +43,4 @@ sweep:
         set: {dynamics.grounding.m: 1, dynamics.remint.enabled: false}
 
 output:
-  dir: results/E6
+  dir: results/figS3_rebaselining

configs/inheritance/figS5_aimed_grounding.yaml

@@ -37,4 +37,4 @@ sweep:
     values: [uniform, matched]
 
 output:
-  dir: results/E3
+  dir: results/figS5_aimed_grounding

configs/inheritance/figS8_multiparent_union.yaml

@@ -31,4 +31,4 @@ sweep:
     values: [0.0, 0.02, 0.05]
 
 output:
-  dir: results/E4
+  dir: results/figS8_multiparent_union

configs/inheritance/figS9_specialist_superparent.yaml

@@ -26,4 +26,4 @@ sweep:
   - param: rho
     values: [0.0, 0.5, 1.0]         # decorrelated -> identical parents (the control)
 
-output: {dir: results/E8}
+output: {dir: results/figS9_specialist_superparent}

configs/inheritance/sexual_vs_asexual_lineage.yaml

@@ -26,4 +26,4 @@ sweep:
   - param: genotype.recomb_rate
     values: [0.0, 1.0]     # asexual vs sexual
 
-output: {dir: results/E7}
+output: {dir: results/sexual_vs_asexual_lineage}

configs/inheritance/speciation_bdm_nk.yaml

@@ -19,4 +19,4 @@ speciation:
   n_offspring: 200
 
 output:
-  dir: results/E12_nk
+  dir: results/speciation_bdm_nk

configs/neural/fig2_mnist_collapse.yaml

@@ -56,4 +56,4 @@ sweep:
     values: [0.0, 0.1]   # dry vs grounded (VAE collapse is strong; needs ~10% real, cf. grounding)
 
 output:
-  dir: results/mnist_collapse
+  dir: results/fig2_mnist_collapse

configs/neural/figS1_architectures.yaml

@@ -51,4 +51,4 @@ sweep:
     values: [0.0, 0.05]
 
 output:
-  dir: results/architectures
+  dir: results/figS1_architectures

configs/neural/figS6_grounding_rnn.yaml

@@ -63,4 +63,4 @@ sweep:
     values: [0.0, 0.005, 0.01, 0.02, 0.035, 0.05, 0.075, 0.1, 0.2]
 
 output:
-  dir: results/grounding
+  dir: results/figS6_grounding_rnn