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

figures/mnist_montage.py

@@ -1,11 +1,11 @@
 """Eyeball diagnostic: watch a dry MNIST lineage collapse, generation by generation.
 
-Unlike the `plot_mnist` figure (a pure function of committed parquet), this **re-runs** a short
+Unlike the `plot_fig2_mnist_collapse` figure (a pure function of committed parquet), this **re-runs** a short
 dry VAE lineage and saves a grid of freshly-generated digits at a few generations, so the
 collapse is visible directly — early generations show varied digits, late generations degenerate
 toward a single blurry mode. Diagnostic only; not part of the reproducible figure set.
 
-Usage:  python figures/mnist_montage.py [results/mnist_collapse]
+Usage:  python figures/mnist_montage.py [results/fig2_mnist_collapse]
 """
 
 from __future__ import annotations
@@ -28,7 +28,7 @@ _SNAP_GENS = [0, 4, 8, 12, 15]     # generations to snapshot
 _COLS = 12                          # sample digits per row
 
 
-def main(out_dir: str = "results/mnist_collapse") -> None:
+def main(out_dir: str = "results/fig2_mnist_collapse") -> None:
     cfg = MnistCfg()
     data = load_mnist(cfg.data_root)
     td = make_mnist_truth(cfg)

figures/plot_bridge.py

@@ -24,8 +24,8 @@ sys.path.insert(0, str(Path(__file__).parent))
 from _figlib import load_bundle, mean_ci, savefig  # noqa: E402
 
 sys.path.insert(0, str(Path(__file__).parents[1] / "src"))
-from knowledge.analysis import critical_grounding, reduce_to_stationary  # noqa: E402
-from knowledge.metrics import heterozygosity  # noqa: E402
+from inheritance.analysis import critical_grounding, reduce_to_stationary  # noqa: E402
+from inheritance.metrics import heterozygosity  # noqa: E402
 from neural.config import SyntheticCfg  # noqa: E402
 from neural.synthetic import make_mode_truth  # noqa: E402
 

figures/plot_collapse.py

@@ -24,7 +24,7 @@ sys.path.insert(0, str(Path(__file__).parent))
 from _figlib import load_bundle, mean_ci, savefig  # noqa: E402
 
 sys.path.insert(0, str(Path(__file__).parents[1] / "src"))
-from knowledge.metrics import heterozygosity  # noqa: E402
+from inheritance.metrics import heterozygosity  # noqa: E402
 from neural.config import SyntheticCfg  # noqa: E402
 from neural.synthetic import make_mode_truth  # noqa: E402
 

figures/plot_collapse_null.py

@@ -2,7 +2,7 @@
 
 Shows tail-first collapse under pure neutral drift: geometric H decay matching the
 analytic law, tail items dying faster than head items, support -> 1 and forward-KL
-diverging. Usage:  python figures/plot_E1.py [results/E1]
+diverging. Usage:  python figures/plot_collapse_null.py [results/collapse_null]
 """
 
 from __future__ import annotations
@@ -17,7 +17,7 @@ sys.path.insert(0, str(Path(__file__).parent))
 from _figlib import load_bundle, mean_ci, savefig  # noqa: E402
 
 
-def main(results_dir: str = "results/E1") -> None:
+def main(results_dir: str = "results/collapse_null") -> None:
     df, cfg = load_bundle(results_dir)
     n = cfg["dynamics"]["n"]
 
@@ -57,7 +57,7 @@ def main(results_dir: str = "results/E1") -> None:
 
     fig.suptitle("E1 — distillation without grounding collapses, tail first", y=1.02)
     fig.tight_layout()
-    savefig(fig, results_dir, "E1")
+    savefig(fig, results_dir, "collapse_null")
 
 
 if __name__ == "__main__":

figures/plot_fig2_grounding_sweep.py

@@ -5,7 +5,7 @@ stationary H vs g tracking the exact H_eq, with an operational g* (where H first
 0.95·H*) and its bootstrap CI, and g=0 marked as a finite-time artifact; (C) tail coverage
 by item-count vs truth-mass — both stay low, the deep tail is largely unrescuable at
 feasible grounding; (D) per-rarity-band survival — the m·p*_i≳1 threshold made visible
-(deep bands lag, motivating E4/E6). Usage:  python figures/plot_E2.py [results/E2]
+(deep bands lag, motivating E4/E6). Usage:  python figures/plot_fig2_grounding_sweep.py [results/fig2_grounding_sweep]
 """
 
 from __future__ import annotations
@@ -20,12 +20,12 @@ sys.path.insert(0, str(Path(__file__).parent))
 from _figlib import load_bundle, mean_ci, savefig, letter_axes  # noqa: E402
 
 sys.path.insert(0, str(Path(__file__).parents[1] / "src"))
-from knowledge.analysis import critical_grounding, reduce_to_stationary  # noqa: E402
-from knowledge.metrics import heterozygosity  # noqa: E402
-from knowledge.truth import make_true_distribution  # noqa: E402
+from inheritance.analysis import critical_grounding, reduce_to_stationary  # noqa: E402
+from inheritance.metrics import heterozygosity  # noqa: E402
+from inheritance.truth import make_true_distribution  # noqa: E402
 
 
-def main(results_dir: str = "results/E2") -> None:
+def main(results_dir: str = "results/fig2_grounding_sweep") -> None:
     df, cfg = load_bundle(results_dir)
     n = cfg["dynamics"]["n"]
     td = make_true_distribution(cfg["truth"]["K"], 1, "zipf", cfg["truth"]["tail_frac"],
@@ -108,7 +108,7 @@ def main(results_dir: str = "results/E2") -> None:
 
     fig.tight_layout()
     letter_axes(fig)
-    savefig(fig, results_dir, "E2")
+    savefig(fig, results_dir, "fig2_grounding_sweep")
 
 
 if __name__ == "__main__":

figures/plot_fig2_mnist_collapse.py

@@ -10,7 +10,7 @@ Four panels, dry (g=0) vs grounded, mean ± 95% CI across replicates: (A) forwar
 (B) support size (distinct modes alive); (C) tail truth-mass alive; (D) heterozygosity. Reads the
 committed bundle (parquet) + manifest.json only.
 
-Usage:  python figures/plot_mnist.py [results/mnist_collapse]
+Usage:  python figures/plot_fig2_mnist_collapse.py [results/fig2_mnist_collapse]
 """
 
 from __future__ import annotations
@@ -26,7 +26,7 @@ sys.path.insert(0, str(Path(__file__).parent))
 from _figlib import load_bundle, savefig, letter_axes  # noqa: E402
 
 sys.path.insert(0, str(Path(__file__).parents[1] / "src"))
-from knowledge.metrics import heterozygosity  # noqa: E402
+from inheritance.metrics import heterozygosity  # noqa: E402
 from neural.config import MnistCfg  # noqa: E402
 from neural.mnist_data import make_mnist_truth  # noqa: E402
 
@@ -39,7 +39,7 @@ def _traj(df, g, col):
     return gens, grp.mean().to_numpy(), 1.96 * grp.sem().to_numpy()
 
 
-def main(results_dir: str = "results/mnist_collapse") -> None:
+def main(results_dir: str = "results/fig2_mnist_collapse") -> None:
     df, cfg = load_bundle(results_dir)
     syn = MnistCfg(**cfg["mnist"])
     H_star = heterozygosity(make_mnist_truth(syn).p_star)
@@ -72,7 +72,7 @@ def main(results_dir: str = "results/mnist_collapse") -> None:
 
     fig.tight_layout()
     letter_axes(fig)
-    savefig(fig, results_dir, "mnist_collapse")
+    savefig(fig, results_dir, "fig2_mnist_collapse")
 
 
 if __name__ == "__main__":

figures/plot_fig4_society_ablation.py

@@ -13,7 +13,7 @@ almost immediately; (C) the self-consumption signature — conformity minus true
 population's mutual agreement exceeds its real capability), largest for no_grounding. Reads only the
 committed bundle.
 
-Usage:  python figures/plot_E11.py [results/E11]
+Usage:  python figures/plot_fig4_society_ablation.py [results/fig4_society_ablation]
 """
 
 from __future__ import annotations
@@ -32,7 +32,7 @@ _ARMS = [("full", "#2ca02c", "full society"),
          ("no_grounding", "#d62728", "no grounding (self-consumption)")]
 
 
-def main(results_dir: str = "results/E11") -> None:
+def main(results_dir: str = "results/fig4_society_ablation") -> None:
     df, _ = load_bundle(results_dir)
     arms = [a for a in _ARMS if a[0] in set(df["arm"].unique())]
     g_opt = df["global_opt"].mean()
@@ -61,7 +61,7 @@ def main(results_dir: str = "results/E11") -> None:
     fig.suptitle("E11 — the dynamic Lamarckian society: grounding + directed sex + diversity climb to "
                  "the optimum; remove any one and it breaks (the vertical claim, C3)", y=1.02, fontsize=12)
     fig.tight_layout()
-    savefig(fig, results_dir, "E11")
+    savefig(fig, results_dir, "fig4_society_ablation")
 
 
 if __name__ == "__main__":

figures/plot_fig5_speciation_bdm.py

@@ -8,7 +8,7 @@ vs divergence — the isolation cliff, moving to lower divergence as epistasis d
 epistasis wedge — as landscape ruggedness K grows, recombining two adapted local-optimum parents flips
 from a gain to outbreeding depression.
 
-Usage:  python figures/plot_E12.py
+Usage:  python figures/plot_fig5_speciation_bdm.py
 """
 
 from __future__ import annotations
@@ -30,8 +30,8 @@ def _agg(df, keys, value):
 
 
 def main() -> None:
-    bdm, _ = load_bundle("results/E12")
-    nk, _ = load_bundle("results/E12_nk")
+    bdm, _ = load_bundle("results/fig5_speciation_bdm")
+    nk, _ = load_bundle("results/speciation_bdm_nk")
     rhos = sorted(bdm["rho"].unique())
     colors = plt.cm.viridis(np.linspace(0.15, 0.85, len(rhos)))
 
@@ -72,7 +72,7 @@ def main() -> None:
     fig.suptitle("E12 — model speciation: when two diverged models are too incompatible to merge",
                  y=1.02, fontsize=13)
     fig.tight_layout()
-    savefig(fig, "results/E12", "E12")
+    savefig(fig, "results/fig5_speciation_bdm", "E12")
 
 
 if __name__ == "__main__":

figures/plot_figS10_rugged_landscapes.py

@@ -12,7 +12,7 @@ Two panels: (A) the risk — mean offspring fitness minus best-parent vs recombi
 per ruggedness K (all ≤0, steeper as K grows); (B) with offspring selection — best-of-brood fitness
 vs rate per K, showing an intermediate optimum on rugged landscapes. Reads only the bundle.
 
-Usage:  python figures/plot_E9.py [results/E9]
+Usage:  python figures/plot_figS10_rugged_landscapes.py [results/figS10_rugged_landscapes]
 """
 
 from __future__ import annotations
@@ -27,7 +27,7 @@ sys.path.insert(0, str(Path(__file__).parent))
 from _figlib import load_bundle, savefig, letter_axes  # noqa: E402
 
 
-def main(results_dir: str = "results/E9") -> None:
+def main(results_dir: str = "results/figS10_rugged_landscapes") -> None:
     df, _ = load_bundle(results_dir)
     Ks = sorted(df["K"].unique())
     rates = sorted(df["rate"].unique())
@@ -58,7 +58,7 @@ def main(results_dir: str = "results/E9") -> None:
 
     fig.tight_layout()
     letter_axes(fig)
-    savefig(fig, results_dir, "E9")
+    savefig(fig, results_dir, "figS10_rugged_landscapes")
 
 
 if __name__ == "__main__":

figures/plot_figS11_directed_recombination.py

@@ -10,7 +10,7 @@ Two panels: (A) deployed capability vs landscape ruggedness — best single pare
 directed sex, and the global optimum; (B) each strategy's edge over the best parent, making the
 random-sex collapse and the directed-sex rescue explicit. Reads only the committed bundle.
 
-Usage:  python figures/plot_E10.py [results/E10]
+Usage:  python figures/plot_figS11_directed_recombination.py [results/figS11_directed_recombination]
 """
 
 from __future__ import annotations
@@ -24,7 +24,7 @@ sys.path.insert(0, str(Path(__file__).parent))
 from _figlib import load_bundle, mean_ci, savefig, letter_axes  # noqa: E402
 
 
-def main(results_dir: str = "results/E10") -> None:
+def main(results_dir: str = "results/figS11_directed_recombination") -> None:
     df, _ = load_bundle(results_dir)
 
     fig, axes = plt.subplots(1, 2, figsize=(13, 5))
@@ -57,7 +57,7 @@ def main(results_dir: str = "results/E10") -> None:
 
     fig.tight_layout()
     letter_axes(fig)
-    savefig(fig, results_dir, "E10")
+    savefig(fig, results_dir, "figS11_directed_recombination")
 
 
 if __name__ == "__main__":

figures/plot_figS12_quality_diversity.py

@@ -3,7 +3,7 @@
 At matched grounding, greedy (directional) selection drives the lineage toward the
 fittest items and collapses diversity, while quality-diversity selection (a novelty bonus
 w_i ∝ f_i·p_i^{-alpha}) maintains a high stationary heterozygosity that rises with the
-novelty exponent alpha. Usage:  python figures/plot_E5.py [results/E5]
+novelty exponent alpha. Usage:  python figures/plot_figS12_quality_diversity.py [results/figS12_quality_diversity]
 """
 
 from __future__ import annotations
@@ -18,7 +18,7 @@ sys.path.insert(0, str(Path(__file__).parent))
 from _figlib import load_bundle, savefig, letter_axes  # noqa: E402
 
 
-def main(results_dir: str = "results/E5") -> None:
+def main(results_dir: str = "results/figS12_quality_diversity") -> None:
     df, cfg = load_bundle(results_dir)
     last = int(cfg["generations"] * 0.8)
 
@@ -72,7 +72,7 @@ def main(results_dir: str = "results/E5") -> None:
 
     fig.tight_layout()
     letter_axes(fig)
-    savefig(fig, results_dir, "E5")
+    savefig(fig, results_dir, "figS12_quality_diversity")
 
 
 if __name__ == "__main__":

figures/plot_figS13_mating_breadth.py

@@ -15,7 +15,7 @@ panmictic), one line per landscape ruggedness K:
 The tension between (A)/(C) is the result: promiscuity maximises the typical model and kills diversity;
 on rugged landscapes the best model needs preserved diversity, so an intermediate breadth wins.
 
-Usage:  python figures/plot_E14.py
+Usage:  python figures/plot_figS13_mating_breadth.py
 """
 
 from __future__ import annotations
@@ -30,7 +30,7 @@ from _figlib import load_bundle, savefig, letter_axes  # noqa: E402
 
 
 def main() -> None:
-    df, _ = load_bundle("results/E14")
+    df, _ = load_bundle("results/figS13_mating_breadth")
     last = df[df["generation"] == df["generation"].max()].copy()
     last["best_n"] = last["best_fitness"] / last["global_opt"]
     last["mean_n"] = last["mean_fitness"] / last["global_opt"]
@@ -60,7 +60,7 @@ def main() -> None:
 
     fig.tight_layout()
     letter_axes(fig)
-    savefig(fig, "results/E14", "E14")
+    savefig(fig, "results/figS13_mating_breadth", "E14")
 
 
 if __name__ == "__main__":

figures/plot_figS1_architectures.py

@@ -10,7 +10,7 @@ Three panels: (A) forward-KL trajectories per architecture, dry (solid) vs groun
 (C) tail-item survival, dry vs grounded, grouped by architecture (all rise). Reads only the
 committed bundle.
 
-Usage:  python figures/plot_architectures.py [results/architectures]
+Usage:  python figures/plot_figS1_architectures.py [results/figS1_architectures]
 """
 
 from __future__ import annotations
@@ -29,7 +29,7 @@ _ARCH_LABEL = {"histogram": "histogram\n(exact)", "rnn": "GRU\n(autoregressive)"
                "mlp": "MLP\n(causal-masked)"}
 
 
-def main(results_dir: str = "results/architectures") -> None:
+def main(results_dir: str = "results/figS1_architectures") -> None:
     df, cfg = load_bundle(results_dir)
     kinds = [k for k in _ARCH_ORDER if k in set(df["kind"].unique())]
     g_dry, g_wet = min(df["g"].unique()), max(df["g"].unique())
@@ -74,7 +74,7 @@ def main(results_dir: str = "results/architectures") -> None:
 
     fig.tight_layout()
     letter_axes(fig)
-    savefig(fig, results_dir, "architectures")
+    savefig(fig, results_dir, "figS1_architectures")
 
 
 if __name__ == "__main__":

figures/plot_figS3_rebaselining.py

@@ -4,7 +4,7 @@ Re-minting freezes the current distribution as the new grounding reference and d
 the original truth. Re-minting a collapsed lineage locks in the collapse: KL to the
 original truth diverges, because the lost original tails can no longer be grounded.
 Gating re-mint on diversity refuses to re-mint while collapsed and keeps KL bounded;
-re-minting a healthy lineage is harmless. Usage:  python figures/plot_E6.py [results/E6]
+re-minting a healthy lineage is harmless. Usage:  python figures/plot_figS3_rebaselining.py [results/figS3_rebaselining]
 """
 
 from __future__ import annotations
@@ -26,7 +26,7 @@ STYLE = {
 }
 
 
-def main(results_dir: str = "results/E6") -> None:
+def main(results_dir: str = "results/figS3_rebaselining") -> None:
     df, cfg = load_bundle(results_dir)
     period = cfg["dynamics"]["remint"]["period"]
     G = cfg["generations"]
@@ -69,7 +69,7 @@ def main(results_dir: str = "results/E6") -> None:
 
     fig.tight_layout()
     letter_axes(fig)
-    savefig(fig, results_dir, "E6")
+    savefig(fig, results_dir, "figS3_rebaselining")
 
 
 if __name__ == "__main__":

figures/plot_figS5_aimed_grounding.py

@@ -3,7 +3,7 @@
 Shows that grounding must *overlap* the content it protects. At the same total budget,
 uniform grounding spreads thin and lets the exercised region's tail collapse, while
 matched grounding concentrates on that region and keeps its rare items alive (at the cost
-of the regions it does not touch). Usage:  python figures/plot_E3.py [results/E3]
+of the regions it does not touch). Usage:  python figures/plot_figS5_aimed_grounding.py [results/figS5_aimed_grounding]
 
 Metric: per-region tail-item survival. (Per-region *heterozygosity* is confounded by
 region mass under matched grounding, so it is deliberately not used here.)
@@ -21,7 +21,7 @@ sys.path.insert(0, str(Path(__file__).parent))
 from _figlib import load_bundle, savefig, letter_axes  # noqa: E402
 
 
-def main(results_dir: str = "results/E3") -> None:
+def main(results_dir: str = "results/figS5_aimed_grounding") -> None:
     df, cfg = load_bundle(results_dir)
     R = cfg["truth"]["R"]
     exercised = cfg["dynamics"]["grounding"]["exercised"]
@@ -66,7 +66,7 @@ def main(results_dir: str = "results/E3") -> None:
 
     fig.tight_layout()
     letter_axes(fig)
-    savefig(fig, results_dir, "E3")
+    savefig(fig, results_dir, "figS5_aimed_grounding")
 
 
 if __name__ == "__main__":

figures/plot_figS6_grounding_rnn.py

@@ -14,7 +14,7 @@ recovery grounding (≈Layer-1's 0.048) and the note that full recovery needs mu
 smoothing model; (D) the metric-choice panel — H and tail-survival are flat/non-monotone
 while forward-KL responds. Reads only the committed bundle.
 
-Usage:  python figures/plot_grounding.py [results/grounding]
+Usage:  python figures/plot_figS6_grounding_rnn.py [results/figS6_grounding_rnn]
 """
 
 from __future__ import annotations
@@ -29,8 +29,8 @@ sys.path.insert(0, str(Path(__file__).parent))
 from _figlib import load_bundle, mean_ci, savefig, letter_axes  # noqa: E402
 
 sys.path.insert(0, str(Path(__file__).parents[1] / "src"))
-from knowledge.analysis import reduce_to_stationary  # noqa: E402
-from knowledge.metrics import heterozygosity  # noqa: E402
+from inheritance.analysis import reduce_to_stationary  # noqa: E402
+from inheritance.metrics import heterozygosity  # noqa: E402
 from neural.config import SyntheticCfg  # noqa: E402
 from neural.synthetic import make_mode_truth  # noqa: E402
 
@@ -63,7 +63,7 @@ def _recovery_gstar(piv: np.ndarray, gs: np.ndarray, frac: float, seed: int = 7)
     return float(pt), float(lo), float(hi)
 
 
-def main(results_dir: str = "results/grounding") -> None:
+def main(results_dir: str = "results/figS6_grounding_rnn") -> None:
     df, cfg = load_bundle(results_dir)
     syn = SyntheticCfg(**cfg["synthetic"])
     H_star = heterozygosity(make_mode_truth(syn).p_star)
@@ -133,7 +133,7 @@ def main(results_dir: str = "results/grounding") -> None:
 
     fig.tight_layout()
     letter_axes(fig)
-    savefig(fig, results_dir, "grounding")
+    savefig(fig, results_dir, "figS6_grounding_rnn")
 
 
 if __name__ == "__main__":

figures/plot_figS8_multiparent_union.py

@@ -4,7 +4,7 @@ Three panels tell the honest story: (A) union coverage rises with K_T and decorr
 matching the exact closed form (recombination *supplies* the tail); (B) that supply is
 realised in the pupil only under a union-preserving merge — mean-mixture distillation
 dilutes it away (flat in K_T) while max-merge keeps it; (C) the union-surviving gap.
-Usage:  python figures/plot_E4.py [results/E4]
+Usage:  python figures/plot_figS8_multiparent_union.py [results/figS8_multiparent_union]
 """
 
 from __future__ import annotations
@@ -23,7 +23,7 @@ def U_closed(K_T, rho, q):
     return rho * q + (1 - rho) * (1 - (1 - q) ** K_T)
 
 
-def main(results_dir: str = "results/E4") -> None:
+def main(results_dir: str = "results/figS8_multiparent_union") -> None:
     df, cfg = load_bundle(results_dir)
     q = cfg["coverage"]["q"]
     K_Ts = sorted(df["K_T"].unique())
@@ -71,7 +71,7 @@ def main(results_dir: str = "results/E4") -> None:
 
     fig.tight_layout()
     letter_axes(fig)
-    savefig(fig, results_dir, "E4")
+    savefig(fig, results_dir, "figS8_multiparent_union")
 
 
 if __name__ == "__main__":

figures/plot_figS9_specialist_superparent.py

@@ -11,7 +11,7 @@ mean-mixture "model soup" plateau below; (B) the decorrelation control — sexua
 count for ρ ∈ {0, 0.5, 1}: decorrelated parents (ρ=0) climb to the optimum, identical parents (ρ=1)
 buy nothing. Reads only the committed bundle.
 
-Usage:  python figures/plot_E8.py [results/E8]
+Usage:  python figures/plot_figS9_specialist_superparent.py [results/figS9_specialist_superparent]
 """
 
 from __future__ import annotations
@@ -26,7 +26,7 @@ sys.path.insert(0, str(Path(__file__).parent))
 from _figlib import load_bundle, mean_ci, savefig, letter_axes  # noqa: E402
 
 
-def main(results_dir: str = "results/E8") -> None:
+def main(results_dir: str = "results/figS9_specialist_superparent") -> None:
     df, cfg = load_bundle(results_dir)
     L = cfg["society"]["L"]
     rhos = sorted(df["rho"].unique())
@@ -60,7 +60,7 @@ def main(results_dir: str = "results/E8") -> None:
 
     fig.tight_layout()
     letter_axes(fig)
-    savefig(fig, results_dir, "E8")
+    savefig(fig, results_dir, "figS9_specialist_superparent")
 
 
 if __name__ == "__main__":

figures/plot_kernel.py

@@ -13,7 +13,7 @@ one knob of the learning kernel:
   estimator REMOVES collapse pressure.
 
 Analytic arms are read from results/kernel_{sharpen,smooth}; the neural reference endpoints
-(dashed) are read from the committed results/mnist_collapse and results/grounding parquets — so the
+(dashed) are read from the committed results/fig2_mnist_collapse and results/figS6_grounding_rnn parquets — so the
 figure is a pure function of committed artifacts.
 
 Usage:  python figures/plot_kernel.py
@@ -31,8 +31,8 @@ sys.path.insert(0, str(Path(__file__).parent))
 from _figlib import load_bundle, savefig, letter_axes  # noqa: E402
 
 sys.path.insert(0, str(Path(__file__).parents[1] / "src"))
-from knowledge.metrics import heterozygosity  # noqa: E402
-from knowledge.truth import make_true_distribution  # noqa: E402
+from inheritance.metrics import heterozygosity  # noqa: E402
+from inheritance.truth import make_true_distribution  # noqa: E402
 
 
 def _mean_traj(df, knob, val, col):
@@ -52,8 +52,8 @@ def _neural_dry(results_dir, col, stationary_frac=0.0):
 
 
 def main() -> None:
-    sh, sh_cfg = load_bundle("results/kernel_sharpen")
-    sm, sm_cfg = load_bundle("results/kernel_smooth")
+    sh, sh_cfg = load_bundle("results/figS2_kernel_sharpen")
+    sm, sm_cfg = load_bundle("results/figS2_kernel_smooth")
     Hstar_sh = heterozygosity(make_true_distribution(
         sh_cfg["truth"]["K"], 1, "zipf", 0.5, sh_cfg["truth"]["zipf_s"], 0,
         tail_threshold=sh_cfg["truth"]["tail_threshold"]).p_star)
@@ -62,10 +62,10 @@ def main() -> None:
         tail_threshold=sm_cfg["truth"]["tail_threshold"]).p_star)
 
     # Neural reference endpoints (dashed) from the committed neural runs.
-    vae_H = _neural_dry("results/mnist_collapse", "heterozygosity")
-    vae_sup = _neural_dry("results/mnist_collapse", "support_size")
-    rnn_H = _neural_dry("results/grounding", "heterozygosity", stationary_frac=0.4)
-    rnn_KL = _neural_dry("results/grounding", "forward_kl", stationary_frac=0.4)
+    vae_H = _neural_dry("results/fig2_mnist_collapse", "heterozygosity")
+    vae_sup = _neural_dry("results/fig2_mnist_collapse", "support_size")
+    rnn_H = _neural_dry("results/figS6_grounding_rnn", "heterozygosity", stationary_frac=0.4)
+    rnn_KL = _neural_dry("results/figS6_grounding_rnn", "forward_kl", stationary_frac=0.4)
 
     fig, axes = plt.subplots(2, 2, figsize=(13, 9))
     NEU, KER = "#1f77b4", "#d62728"
@@ -111,7 +111,7 @@ def main() -> None:
     ax.legend(frameon=False, fontsize=8)
 
     fig.tight_layout()
-    for d in ("results/kernel_sharpen", "results/kernel_smooth"):
+    for d in ("results/figS2_kernel_sharpen", "results/figS2_kernel_smooth"):
         letter_axes(fig)
         savefig(fig, d, "kernel")
 

figures/plot_sexual_vs_asexual_lineage.py

@@ -10,7 +10,7 @@ Two panels: (A) mean-fitness adaptation curves, asexual vs sexual, over generati
 disequilibrium over generations — asexual holds beneficial alleles in disequilibrium (scattered
 across genotypes) while sexual drives it to ~0 (assembled), the mechanism of the speed gap.
 
-Usage:  python figures/plot_E7.py [results/E7]
+Usage:  python figures/plot_sexual_vs_asexual_lineage.py [results/sexual_vs_asexual_lineage]
 """
 
 from __future__ import annotations
@@ -24,7 +24,7 @@ sys.path.insert(0, str(Path(__file__).parent))
 from _figlib import load_bundle, mean_ci, savefig  # noqa: E402
 
 
-def main(results_dir: str = "results/E7") -> None:
+def main(results_dir: str = "results/sexual_vs_asexual_lineage") -> None:
     df, cfg = load_bundle(results_dir)
     L = cfg["genotype"]["L"]
     arms = [(0.0, "#7f7f7f", "asexual (clonal)"), (1.0, "#d62728", "sexual (recombining)")]
@@ -55,7 +55,7 @@ def main(results_dir: str = "results/E7") -> None:
     fig.suptitle("E7 — the advantage of sex: recombination reassorts beneficial alleles that arose "
                  "in different lineages", y=1.02, fontsize=12)
     fig.tight_layout()
-    savefig(fig, results_dir, "E7")
+    savefig(fig, results_dir, "sexual_vs_asexual_lineage")
 
 
 if __name__ == "__main__":