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/recombination/README.md

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+# recombination — "merge, don't average" holds in real neural weights (E4 in the flesh)
+
+**Claim tested:** the E4 finding — complementary specialists can rebuild the rare tail, but only if you
+*merge* them rather than average them — was proven in math. Does it survive in trained neural nets?
+
+**Setup (Layer 1.5).** `K_T` specialist **GRUs** are each trained on a different slice of the rare
+tail (slices set by the exact shared-switch construction, so teacher count `K_T`, correlation `ρ` and
+retention `q` stay clean knobs and the construction-level union matches the closed form). The pupil then
+recombines the teachers' *measured* distributions two ways: **mean** (naive pooling) vs
+**oracle-guided max-merge** (per mode, keep the strongest teacher — a union-preserving merge). `K = 256`,
+`n = 200`, `q = 0.5`, swept `K_T ∈ {1,2,3,5}` × `ρ ∈ {0, 1}`, 8 repeats.
+
+### Symbols
+- **`K_T`** number of specialist teachers; **`ρ`** how correlated their retained tails are (0 = complementary, 1 = identical clones).
+- **union coverage** — tail covered by ≥1 teacher (the raw supply). **surviving coverage** — what remains after the pupil resamples.
+- **mean-distill** — average/pool the teachers. **max-merge** — keep each mode's strongest teacher (union-preserving).
+- **target vs trained** — coverage computed from the *assigned* distributions vs from the *trained GRU* outputs.
+
+### The four panels
+1. **Supply.** Union coverage vs `K_T`: at `ρ = 0` it climbs 0.49 → 0.96 and matches the closed form
+   `U(K_T, ρ, q)`; at `ρ = 1` (clones) it is flat. Diverse teachers supply more tail.
+2. **Analytic teachers (`ρ = 0`).** Surviving coverage vs `K_T`: **max-merge (green) rises**
+   0.043 → 0.087 while **mean-distill (red) stays flat ~0.045** — the conservation law from E4,
+   reproduced on the assigned distributions.
+3. **Trained GRU teachers (`ρ = 0`).** The same comparison on *real trained weights*: same signs —
+   max-merge above mean-distill — but **compressed and noisier** (the GRU's smoothing inflates the
+   baseline and the deep tail barely clears `n = 200` resampling). The honest inductive-bias caveat.
+4. **Control (`ρ = 1`).** Identical teachers: union, max and mean are all flat — **more clones buy
+   nothing.** Decorrelation is what the benefit needs.
+
+### Takeaway
+"Merge, don't average" is not a mathematical artefact — in trained neural nets, a union-preserving
+**max-merge realises the multi-teacher tail benefit while naive averaging conserves the collapse.**
+This is load-bearing for the paper's recombination claim (and points at merging over distillation for
+the deferred LLM layer). **Falsifier (not triggered):** if mean-distill had also risen with `K_T`, or
+max never beat mean, the recombination lesson would have died in real weights.