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

results/figS12_quality_diversity/README.md

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+# E5 — Quality-diversity selection preserves diversity where greedy selection destroys it
+
+**Claim tested:** if each generation you *select* which outputs to keep, does chasing the "best"
+outputs (greedy) accelerate collapse — and does rewarding novelty instead prevent it?
+
+**Setup (Layer 1, pure math).** `K = 500` items, Zipf truth, `n = 200`, 400 generations, 100 repeats,
+all arms given the same grounding. Three selection modes: **none** (grounding only, no selection),
+**greedy** (keep the fittest — highest-`p*` — items), and **quality-diversity (QD)** (a novelty
+bonus `w_i ∝ f_i · p_i^{-α}` that up-weights rare items). The novelty exponent `α` is swept over
+`{0.5, 1, 2}`.
+
+### Symbols
+- **greedy** — select toward the fittest/most-probable items (directional pressure).
+- **QD (quality-diversity)** — select for fitness *and* novelty; `α` = strength of the novelty bonus.
+- **`H`** diversity; **support** = number of distinct items surviving.
+
+### The three panels
+1. **Diversity trajectories.** `H` over generations: red = greedy (crashes toward ~0, i.e. fixation
+   on a few items); orange/blue = QD at `α = 1, 2` (holds a high plateau); green = none (reference).
+   Greedy selection is a *second* collapse engine on top of drift.
+2. **Novelty dose–response.** Stationary `H` vs the novelty exponent `α` for QD (orange dots), with
+   greedy (red dashed) and none (green dashed) as reference lines. QD sits above greedy for **every**
+   `α`, and rises as the novelty bonus strengthens.
+3. **Surviving items per arm.** Stationary support (number of distinct items alive) as bars: greedy is
+   lowest; QD arms keep progressively more items alive as `α` grows; none is the reference.
+
+### Takeaway
+Optimising only for "what looks best" (greedy) collapses the population onto a handful of winners; a
+novelty-rewarding, quality-diversity objective actively **re-introduces and holds the tail**. Key
+numbers: greedy `H ≈ 0.01` (near-total fixation) vs QD `H ≈ 0.48–0.88` rising with `α`.
+**Falsifier (not triggered):** if QD's stationary `H` had been ≤ greedy's, quality-diversity would be
+doing no work.