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/grounding/README.md

@@ -1,38 +0,0 @@
-# grounding — the grounding response in real weights (and why the ruler matters)
-
-**Claim tested:** does the E2 result — a small dose of real data (`g* ≈ 0.05`) rescues diversity —
-reproduce in a trained GRU? The honest answer reframes the question.
-
-**Setup (Layer 1.5).** Autoregressive GRU, `K = 256` modes, `n = 200`, 30 generations, grounding
-swept over 9 values `g ∈ {0, 0.005, …, 0.2}`, **18 repeats** (many repeats are needed because each
-lineage's fate is genuinely noisy under `n = 200` drift). The falsifier was pinned in the config
-*before* running.
-
-### Symbols
-- **`g`** grounding fraction (share of real data), **`g*`** its critical value.
-- **forward-KL** distance from truth (the operative neural collapse metric here).
-- **tail survival** `tail_truth_mass_alive` — truth-weighted fraction of the rare tail retained. **`H`** diversity.
-- **recovery fraction** — how much of the achievable forward-KL reduction a given `g` has bought (0 = dry, 1 = best observed).
-
-### The four panels
-1. **Trajectories.** Forward-KL over generations per `g`: grounding suppresses the climb.
-2. **Phase boundary.** Stationary forward-KL vs `g` **falls monotonically** (dry ≈ 2.08 → `g = 0.2`
-   ≈ 0.75); the effect is statistically significant (paired *t* up to 3.3; 89% of lineages improve at
-   `g = 0.2`). **The SIGN is confirmed.**
-3. **Recovery curve.** Fraction of the divergence gap closed vs `g`. **Half the gap closes by a
-   median-recovery grounding of `g ≈ 0.04`** (CI [0.004, 0.116]) — a striking echo of Layer-1's 0.048
-   (black dashed) — **but full recovery needs `g ≈ 0.19`**, far more than the exact histogram: the
-   GRU's smoothing both caps the collapse and slows the rescue.
-4. **Why forward-KL (the key methodological panel).** Normalised responses of three rulers vs `g`:
-   `H/H*` (flat ~0.8) and **tail survival (flat / non-monotone — dry is as high as grounded!)** both
-   fail to register the effect, while **forward-KL recovery** responds cleanly. A smoothing model keeps
-   *spurious* tail support alive, so counting surviving modes is misleading; only distance-from-truth
-   is honest.
-
-### Takeaway (an honest reframing)
-Two results: **(1)** the operative neural collapse metric is **forward-KL**, not `H` or tail-survival —
-smoothing decouples "modes alive" from "close to truth." **(2)** The *sharp* threshold `g* ≪ 1` is a
-property of the exact operator, carried quantitatively by the histogram **bridge** (`g* = 0.047`); the
-trained GRU confirms grounding's **direction** and **softens** its sharpness. The pre-registered
-95%-of-`H*`/tail falsifier is *not* met — but because those are the wrong rulers for a smoothing
-model, not because grounding fails; the blueprint §3.5 directional claim holds robustly.