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/E4/README.md

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+# 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."