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/plot_figS11_directed_recombination.py

@@ -0,0 +1,64 @@
+"""E10 figure — directed sex beats biological sex (the distinctly-AI superpower).
+
+On rugged (epistatic) landscapes, blind "biological" sex — random mates, no offspring selection —
+suffers outbreeding depression: offspring are worse than the parents. But an AI can do what biology
+cannot: choose complementary mates, evaluate *many* recombinant offspring, and keep only the fittest,
+over several rounds, with no two-parent limit. This **directed sex** avoids the catastrophe and
+matches or exceeds the best parent even when skills are entangled.
+
+Two panels: (A) deployed capability vs landscape ruggedness — best single parent, random (blind) sex,
+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_figS11_directed_recombination.py [results/figS11_directed_recombination]
+"""
+
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+
+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/figS11_directed_recombination") -> None:
+    df, _ = load_bundle(results_dir)
+
+    fig, axes = plt.subplots(1, 2, figsize=(13, 5))
+
+    # Panel A: the three strategies + global optimum vs ruggedness.
+    ax = axes[0]
+    for col, c, lab in [("global_opt", "green", "global optimum"),
+                        ("directed_sex", "#d62728", "directed sex (AI: choose + select)"),
+                        ("best_parent", "#7f7f7f", "best single parent"),
+                        ("random_sex", "#1f77b4", "random sex (blind, biology)")]:
+        k, m, ci = mean_ci(df, "K", col)
+        ls = ":" if col == "global_opt" else "-o"
+        ax.plot(k, m, ls, color=c, label=lab) if col == "global_opt" else \
+            ax.errorbar(k, m, yerr=ci, fmt=ls, color=c, capsize=3, label=lab)
+    ax.set(xlabel="landscape ruggedness $K$ (epistasis)", ylabel="deployed capability (fitness)",
+           title="Random sex craters with ruggedness;\ndirected sex tracks/exceeds the best parent")
+    ax.legend(frameon=False, fontsize=8)
+
+    # Panel B: edge over best parent (random collapse vs directed rescue).
+    ax = axes[1]
+    bp = df.groupby("K")["best_parent"].mean()
+    for col, c, lab in [("directed_sex", "#d62728", "directed sex"),
+                        ("random_sex", "#1f77b4", "random sex")]:
+        s = df.groupby("K")[col].mean() - bp
+        ax.plot(s.index, s.values, "-o", color=c, label=lab)
+    ax.axhline(0, ls=":", color="gray", lw=1, label="best parent")
+    ax.set(xlabel="landscape ruggedness $K$", ylabel="capability − best parent",
+           title="Directed sex stays ≥ parents; blind sex\nfalls far below (outbreeding depression)")
+    ax.legend(frameon=False, fontsize=9)
+
+    fig.tight_layout()
+    letter_axes(fig)
+    savefig(fig, results_dir, "figS11_directed_recombination")
+
+
+if __name__ == "__main__":
+    main(*sys.argv[1:])