XllentAI
/

modular_arithmetic

The 16/32-bit MLP->TCN shrink took the artifact to ~0.13 GB; the later mid-cell
collapse to one shared weight-set brought the total to 0.08 GB. Avoids implying
the small-cell change alone produced 0.08 GB.

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@@ -83,7 +83,8 @@ makes the 128/256/512/1024-step chains hold up.
 **Every cell — including the 16- and 32-bit small-prime cells — is now this same architecture.**
 The two small cells were originally width-4096/6144 MLPs (660 MB combined); replacing them with
 the carry-aware TCN, trained width-matched (bit-length-uniform over the cell's whole range),
-shrank the artifact from 0.77 GB to **0.08 GB**, raised tier 4 from 0.99 to **1.00**, and made
 the small-prime tiers width-robust — a TCN trained near-max-width only has a short-prime blind
 spot (see the audit note below), which the width-matched training removes.

 **Every cell — including the 16- and 32-bit small-prime cells — is now this same architecture.**
 The two small cells were originally width-4096/6144 MLPs (660 MB combined); replacing them with
 the carry-aware TCN, trained width-matched (bit-length-uniform over the cell's whole range),
+shrank the artifact from 0.77 GB to ~0.13 GB (the later mid-cell collapse then brought the total
+to **0.08 GB**), raised tier 4 from 0.99 to **1.00**, and made
 the small-prime tiers width-robust — a TCN trained near-max-width only has a short-prime blind
 spot (see the audit note below), which the width-matched training removes.