Previous experiments encoded text as char mod q in every channel -- 7 projections of the SAME signal. This experiment makes each channel encode a DIFFERENT feature: structural type, phonemic class, character identity, and bigram transition class.
If channels specialize, the partition into feature carriers {mod 8, 25, 49} vs parity checksums {mod 9, 11, 13, 17} becomes functional structure, not just algebra.
Each channel encodes a different LEVEL of text structure. Key lesson: both input AND target must be het (het input + homo target fails).
Per-channel bigram tallies on the same 471-character corpus. Given prev char's encoding, predict next char's encoding. Reconstruct via majority vote over 128 candidates.
Feature channels {mod 8, mod 25, mod 49} carry direct data. Parity channels {mod 9, mod 11, mod 13, mod 17} are computed from features. Het encoding makes the split dramatic:
Het channels DO specialize -- mod-8 and mod-49 carry structural/transition info far better than char%q. The feature/parity split is meaningful: feature channels gain +48%, parity channels lose -4%. But reconstruction from qualitatively different features is harder than CRT algebraic reconstruction.
The reconstruction gap (-6 accuracy) reveals the key insight: channel specialization helps PERCEPTION but hurts RECONSTRUCTION. CRT's power is algebraic uniqueness (one number mod coprime moduli has exactly one solution). Het features lack this guarantee.
Next experiment (Ring Substrate): instead of encoding text INTO the ring, make the ring the computation substrate. Text goes in, text comes out. Between them: ring arithmetic IS the machinery. Modular multiplication for feed-forward, channel matching for attention, projectors for regularization.
Source code · Public domain (CC0)
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