R12 S6.2 Contextual Affine Law Neural Development Receipt

Status: frozen before development-board seed, board generation, fit, model access, or score.

This receipt imports the theorem, claim boundary, law split, controls, and gates from R12_S6_CONTEXTUAL_AFFINE_LAW_INDUCTION_PREREG.md, the sole scoreless split repair from v1.1, and the passing CPU mechanics artifact at SHA-256 a31a232c83a53d0b7aff87b4a495abd6740d98589059325951e2e4688e2bded6.

Frozen Development Data

After the source, tests, and this receipt are committed, draw exactly one board seed. Build:

• 961 unique atomic training cells: every position of every admitted training law at moduli 5, 7, and 11;
• 2,048 balanced primary development programs over modulus x depth cells for moduli 5/7/11 and depths 3--8; and
• 512 modulus-13 scale-diagnostic programs.

Every program uses at least two held-out development laws, depth 3--8, a random initial identity permutation, arbitrary nonce operation names, and a late position query. Files must contain no confirmation programs. The treatment input is exactly (modulus, card_y0, card_y1, current_location); control_law_id is visible only to the matched memorizer.

Frozen Architecture

Treatment ContextualAffineLawInducer:

• four categorical tokens: LAW, SUPPORT_0, SUPPORT_1, QUERY;
• width 256, six pre-norm Transformer encoder layers;
• eight attention heads, feed-forward width 1,024, GELU, zero dropout;
• learned role, modulus, input-coordinate, and output-coordinate embeddings;
• one 13-way destination head with a hard modulus mask;
• 4,753,677 trainable parameters;
• 138,448,546 total parameters with the promoted bounded Shohin stack.

The favorable LawIdMemorizer uses the same transformer plus a 104-entry law embedding and has 4,780,301 trainable parameters. Training law IDs are unique; every development law receives the same OOV ID. The control has more parameters than treatment and identical update count, batch stream, optimizer, and device.

Frozen Optimization

Draw one training seed after the implementation commit. Both arms use:

• AdamW;
• 4,000 updates;
• batch size 256 sampled with replacement from the 961 atomic rows;
• learning rate 5e-4;
• weight decay 0.01;
• gradient-norm clip 1.0; and
• the same sampled row-index stream.

Both arms must reach at least 99% exact atomic training accuracy before the sole development read. Failure closes S6 without optimizer, width, epoch, seed, or data repair.

Sole Development Access

One serial H100 job fits both atomic arms, writes one immutable checkpoint, reads the primary and diagnostic development boards exactly once, writes one evaluation, and applies the already-frozen assessor. No retry may reuse the same board after a model or score is produced. Infrastructure failure before a valid checkpoint or development read may be documented and retired, but cannot alter the mechanism or thresholds.

The evaluator reports host, treatment, deranged-card, one-witness, state-reset, and OOV law-ID arms; every depth; the multi-law stratum; nonce-name invariance; all held-out atomic law cells; and modulus-13 diagnostic accuracy. Confirmation generation remains forbidden unless the assessor records qualify_s6_for_one_confirmation with every gate true.