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/E4/README.md

@@ -1,33 +0,0 @@
-# E4 — Recombination supplies the rare tail; only a union-preserving *merge* realises it
-
-**Claim tested:** if several specialist models each remember a different slice of the rare tail, can
-combining them reconstruct the whole tail? And does *how* you combine them matter?
-
-**Setup (Layer 1, pure math).** `K = 500` items. We build `K_T` teacher models that each retain the
-rare tail with probability `q`, and we control how **correlated** their retained tails are with a
-single knob `ρ` (rho): `ρ = 0` = fully complementary teachers, `ρ = 1` = identical teachers. Swept:
-`K_T ∈ {1,2,3,5}`, `ρ ∈ {0, 0.25, 0.5, 0.75, 1}`, grounding `g ∈ {0, 0.02, 0.05}`, 200 repeats.
-
-### Symbols
-- **`K_T`** number of teacher models; **`ρ`** how correlated their retained tails are (0 = diverse, 1 = clones).
-- **union coverage** — fraction of the tail covered by *at least one* teacher (the raw supply).
-- **surviving coverage** — fraction that actually survives in the pupil after it retrains on the combination.
-- **mean-distill** — pupil trained on the pooled/averaged teacher outputs. **max-merge** — keep, per item, the strongest teacher (a union-preserving merge, à la M2N2).
-
-### The three panels
-1. **Supply.** Union tail coverage vs `ρ`, one curve per `K_T`; solid lines are the exact closed form
-   `U(K_T, ρ, q)`. More teachers and more diversity (lower `ρ`) supply more of the tail — and the
-   simulation matches the formula exactly.
-2. **Realisation.** Surviving coverage vs `ρ`. **Solid = max-merge rises** with more/diverse teachers;
-   **dashed = mean-distill stays flat.** Averaging dilutes each teacher's rare items back below the
-   survival threshold — the gain is supplied but not realised.
-3. **The benefit needs the right operator (`ρ = 0`).** Surviving coverage vs `K_T` under both
-   operators. Max-merge climbs with teacher count; mean-distill is flat — a **conservation law**:
-   averaging's `1/K_T` dilution exactly cancels the union gain.
-
-### Takeaway — "merge, don't average"
-Complementary specialists *contain* enough to rebuild the tail, but **naive multi-teacher distillation
-(averaging) throws it away**; you must combine them with a union-preserving merge. This is load-bearing
-for the paper's recombination claim and is re-tested in real neural weights in `results/recombination/`.
-**Falsifier (not triggered):** if mean-distill had also risen with `K_T`, or max never beat mean, the
-recombination story would collapse into "just average your models."