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/E3/README.md

@@ -1,33 +0,0 @@
-# E3 — Grounding must overlap the content it protects
-
-**Claim tested:** if your budget of real data is fixed, does it matter *where* you spend it? Is it
-enough to sprinkle real data uniformly, or must it target the knowledge you care about?
-
-**Setup (Layer 1, pure math).** `K = 1000` items divided into `R = 10` disjoint **regions** (think
-subject areas). `n = 200`, 400 generations, 100 repeats. The **same total** real-data budget is
-spent two ways: **uniform** (spread evenly over all 10 regions) vs **matched** (concentrated on the
-"exercised" region we want to protect). One region is designated exercised (region 0 here) and has a
-rare tail we track.
-
-### Symbols
-- **region** — a block of related items; a stand-in for a task family / subject area.
-- **matched vs uniform** — real data aimed at the exercised region vs spread evenly, at equal total budget.
-- **tail items alive** — how many rare items in a region still have any probability.
-
-### The two panels
-1. **Target region over time.** Rare-item survival in the exercised region, generation by generation:
-   blue = matched, red = uniform (bands = 95% CI). Matched **holds** the region's tail alive; uniform
-   spreads too thin and lets it **collapse**.
-2. **Every region at steady state.** Bar chart of stationary tail survival per region, matched (blue)
-   vs uniform (red); the dotted line marks the exercised region. Matched wins big *there* — at the
-   cost of the regions it deliberately ignores. Uniform is mediocre everywhere.
-
-*(Note: per-region diversity `H` is confounded by how much probability mass sits in a region, so this
-figure uses the honest, mass-independent metric — tail-item survival.)*
-
-### Takeaway
-Reality checks only protect what they actually cover. To keep a capability alive you must ground
-**on that capability**, not on data in general — grounding is local, not a global tonic. Key numbers:
-exercised-region tail survival ≈ **0.49 (matched)** vs **0.07 (uniform)** at equal budget.
-**Falsifier (not triggered):** if uniform had protected the region as well as matched, the
-targeting claim would die.