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(a·b) mod p).(a mod p, b mod p, prime).
It learns the finite residue multiplication tables for the fixed Tier-1
primes {2,3,5,7} from random init, and returns 0 for any prime outside that
set (all higher tiers) — honest degradation.| Metric | Value |
|---|---|
| Tier 1 accuracy | 1.00 |
| highest_tier_above_90 | 1 |
| overall_accuracy | 0.122 |
1.00 to ~0.17 (provenance test, rules Principle 2).sympy/gmpy2, no int(a)*int(b)%int(p), no
input-indexed lookup tables. The only non-learned step is reducing each operand
mod p (input normalisation).modchallenge check; deterministic (argmax inference).model.py — entry class Tier1MulModel + TinyMulNet.weights.pt — trained weights (seed 42).train.py — reproduces the weights from scratch.manifest.json — entry_class, output_base=10, descriptions.