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/architectures/README.md

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+# architectures — collapse and rescue are architecture-general
+
+**Claim tested:** is model collapse (and its cure, grounding) a quirk of one model type, or does the
+same signature appear across genuinely different neural architectures?
+
+**Setup (Layer 1.5).** The identical generational loop is run with three different generative models —
+an **exact histogram** (no neural net), an **autoregressive GRU** (recurrent), and a **causal-masked
+MLP** (feed-forward) — each a distinct "inductive bias." `K = 256` modes, `n = 200`, 22 generations,
+5 repeats, compared at **dry (`g = 0`)** vs **grounded (`g = 0.05`)**.
+
+### Symbols
+- **inductive bias** — the built-in assumptions of a model type (a histogram has none; a GRU and an MLP smooth differently).
+- **forward-KL** distance from truth; **tail items alive** — how many rare modes still appear.
+- **dry** = no grounding; **grounded** = 5% real data mixed in.
+
+### The three panels
+1. **Trajectories.** Forward-KL over generations, coloured by architecture; **solid = dry** (climbs,
+   collapse) vs **dashed = grounded** (held down). The dry-up / grounded-down gap appears in **every**
+   architecture.
+2. **Stationary forward-KL (grouped bars).** For each architecture, dry (red) vs grounded (green).
+   Divergence **falls with grounding across all three** — histogram, GRU, MLP.
+3. **Tail-item survival (grouped bars).** Same grouping. Survival **rises with grounding across all
+   three.** (Note the histogram's bars are tiny: with no smoothing it drops rare modes outright,
+   whereas the GRU/MLP keep some alive — an inductive-bias difference, not a contradiction.)
+
+### Takeaway
+The Wright–Fisher collapse operator and the grounding rescue are **not artefacts of one model** — they
+show up in an exact counter, a recurrent net, and a feed-forward net alike. This is the
+architecture-generality claim of Layer 1.5. **Falsifier (not triggered):** if the signs had appeared
+only for the histogram, collapse would be a property of the idealised operator, not of trained models.
+*(A VAE was also implemented but fails the generation-0 fidelity check on this task, so it is excluded
+to avoid confusing underfitting with collapse — documented as a known limitation.)*