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

paper/the-evolution-of-sex-for-ai.md

@@ -365,7 +365,7 @@ prematurely converges onto one basin and finds a *worse* champion, while pure mo
 Throughout, wide mating lifts the *typical* model but monotonically **destroys diversity** — so on rugged
 problems, where the best model needs preserved diversity to be found, structured (partly monogamous)
 merging wins. The design rule extends the one above: *merge widely when skills are additive; keep
-structured sub-populations — island-style merging — when skills are rugged.* (Figure: `results/E14/E14.png`.)
+structured sub-populations — island-style merging — when skills are rugged.* (Figure: `results/figS13_mating_breadth/E14.png`.)
 
 *AI can do sex better than biology can.* Biology is stuck with two parents, mating roughly at random,
 and cannot inspect an offspring before it is born. An AI has none of those limits. It can recombine
@@ -423,7 +423,7 @@ increasing specialisation eventually breaks merging and that one should then rou
 trained models is a coordinate artefact removable by aligning neurons (Git Re-Basin — Ainsworth et al.,
 2022). What the frame adds is the *theory* of the phenomenon they observe: its functional form, its
 super-linear (snowball) onset, and its dependence on epistasis — merge failure as a Dobzhansky–Muller
-event. (Figure: `results/E12/E12.png`.)
+event. (Figure: `results/fig5_speciation_bdm/E12.png`.)
 
 **The real-weight confirmation.** The obvious objection to the analytic model is that its
 "incompatibility" is a re-labelled loss barrier, and loss barriers between independently trained