README: link exp013_aug campaign

README.md

@@ -120,6 +120,18 @@ coefficient heads collapse at any dim); **sign-code task-parity** (3/3 seeds); t
 pressure. Full write-up: [exp012_ar/article.md](./exp012_ar/article.md) — 48-run
 ledger, 17 trained specimens, and the complete bed included.
 
+## exp013 — augmenting pretrained models (July 10, 2026)
+
+[`exp013_aug/`](./exp013_aug): the aleph structures applied to FROZEN pretrained
+models. Headline: GPT-2 124M ppl 38.65 -> **26.53** with aleph relay adapters (1.18M
+trainable) vs 27.26 for the param-matched MLP ablation — 2/2 seeds, with the gate
+mechanism visible (aleph gates grow 3x from init, MLP gates shrink below it). Also:
+the bottleneck prior is substrate-scoped (MLP wins frozen CLIP-L token-AR); the
+layer law (penultimate is richer but nonlinearly coded); sign-code > soft read in
+12/12 pretrained-substrate cells; spelling-AR shows the address extracts more of the
+surface residue that exists but conjures nothing absent. 18 specimens, all books
+projective. Full ledger + write-up in [exp013_aug/README.md](./exp013_aug/README.md).
+
 ## Relation to prior work
 
 Residual-expert quantization (RQ-MoE, SwitchCodec/REVQ), hierarchical