R12 Sparse-Law Neural Microcode Preregistration

Trigger

Both architecture-generic table completers fail on hash-disjoint unseen operators:

The factorized treatment also loses its direction-negated control in exact queries. It is closed.

Hypothesis

A 125M-scale language model may need an internal computational substrate rather than being expected to rediscover arithmetic and bitwise execution in its weights. The new treatment separates:

1. a learned byte controller that orients sparse demonstrations and predicts operator-family microcode plus parameters; and
2. a deterministic finite-domain ALU inside the model forward pass that executes the microcode and emits a transition distribution.

This is architecture-native execution: no host callback, parser, solver, search, or posthoc verifier runs at candidate time.

Fixed ALU

The ALU exposes three operation schemas over domains 8 and 16:

• modular affine;
• rotate/xor; and
• Gray-conjugated modular affine.

The controller predicts family, multiplier, offset, rotation, and mask distributions. The ALU evaluates their differentiable mixture. Program parameters in development are hash-disjoint from training.

Training receives preparation-only exact labels for the operation family and its relevant parameters in addition to complete-map and source-direction losses. Development receives no labels at candidate time; those labels are used only for scoring. This treatment therefore tests supervised induction into a fixed internal instruction set, not discovery of that instruction set.

This is deliberately an ontology-bearing architecture and cannot establish open-ended law discovery. Its purpose is to test whether explicit internal microcode closes the sparse-completion gap.

Frozen Canary

The board, source deletion, map partition, optimization budget, and same-weight controls are unchanged from the generator-factorization canary. Four counterfactual source orders teach the held-out relation lexemes without including the exact passive renderer.

Continue only if the treatment:

• exceeds 46.5% development transition accuracy;
• produces at least one complete unseen map;
• beats every same-weight control in exact query accuracy; and
• retains zero training/development action-map overlap.

A pass authorizes a five-seed microcode qualification, not a general-reasoning claim.

Frozen Outcome

Job 704760 completed the preregistered 2,000-update H100 canary. Treatment reached 22.1250% development transition accuracy, 0/60 complete maps, and 0/60 exact queries. The direction-negated same-weight control reached 1/60 exact queries. Training/development action-map overlap was zero.

All continuation criteria fail. Decision: supervised_microcode_fails_hash_disjoint_sparse_law_induction. Full evidence: R12_SPARSE_LAW_MICROCODE_RESULT.md.