Prose analysis: per-model table + arch-feature correlations (#3)

- Prose analysis: per-model table + arch-feature correlations (4f1c408884280cac13507b11d1d5f1a7634196a7)

arch_analysis.md

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+# Arch -> Score: what the numbers say
+
+Panel: 4 from-scratch SLMs, all verified-clean, all in the small-model community.
+
+Every model scored on the SAME harness (zero-shot loglikelihood, lm-eval 0.4.13, float32, batch 8, cuda:0): BLiMP (mean of subtasks), ARC-Easy, PIQA, HellaSwag (acc_norm). Macro = mean of the four.
+
+## Per-model
+| model | params | arch | BLiMP | ARC-E | PIQA | HellaSwag | macro |
+|---|---|---|---|---|---|---|---|
+| exnivo/tinybrain-100m-base | 103,385,856 | llama L12 d768 | 76.6 | 42.0 | 58.9 | 28.2 | 51.2 |
+| aksern/nexi-g1 | 30,339,456 | gpt2 L6 d384 | 73.2 | 35.2 | 57.8 | 26.8 | 48.2 |
+| oddadmix/Emhotob-25M-Egyptian-English-v2 | 25,271,424 | llama L8 d384 | 51.3 | 26.6 | 54.2 | 25.9 | 39.5 |
+| textilelabs/Loom-Crucible-Preview | 154,980,864 | llama L52 d512 | 53.3 | 26.5 | 51.2 | 26.0 | 39.2 |
+
+## Arch feature -> macro correlations (Pearson r)
+| feature | r | n |
+|---|---|---|
+| ffn_dim | +0.921 | 3 |
+| d_model | +0.567 | 4 |
+| n_heads | +0.567 | 4 |
+| n_layers | -0.538 | 4 |
+| vocab_size | +0.365 | 4 |
+| max_ctx | +0.154 | 4 |
+| total_params | -0.132 | 4 |
+
+## Best-arch read
+Highest macro: **exnivo/tinybrain-100m-base** (51.2).
+Its arch: {"total_params": 103385856, "n_layers": 12, "d_model": 768, "n_heads": 12, "ffn_dim": 2048, "vocab_size": 24000, "max_ctx": 2048}
+
+**Honest caveat:** with 4 models these correlations are directional, not statistical. The strongest signal is that parameter count and depth track macro, but every model here was trained on a different corpus and token budget, so architecture and data are confounded. This panel is a first consistent measurement, not a verdict.