docs: accessible figure legends (README.md) for all figures

One self-contained README.md per results/ figure folder (Layer 1 E1-E6
and Layer 1.5 bridge/collapse/grounding/architectures/recombination):
plain-language claim, setup, a compact symbol glossary, a panel-by-panel
walkthrough, and the takeaway + falsifier. Auto-renders when browsing the
folder; carries the honest caveats (grounding's ruler reframing, the
excluded VAE).

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

results/grounding/README.md

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+# 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.