| license: mit | |
| library_name: pytorch | |
| tags: | |
| - modular-arithmetic | |
| - neural-algorithm | |
| - sair | |
| # Learned Modular Arithmetic — Optimized Schedule | |
| Performance ablation of the uniform-transition SAIR Modular Arithmetic | |
| Challenge submission. | |
| This artifact uses the same reproducibly trained 68,406-parameter checkpoint | |
| and learned carry, comparison, borrow, and digit-resolver components as the | |
| primary model, while specializing the fixed two-phase call schedule to avoid | |
| redundant conditional-subtraction passes. Preprocessing performs independent | |
| representation conversion only and does not reduce the raw operands. | |
| All 15,387 possible learned primitive cells match their specifications. | |
| Local evaluation with the official pipeline: 1,000/1,000 scored problems, | |
| 100% through Tier 10, deterministic, 70.4 seconds inference on Apple MPS. | |
| The included `train.py` reproduces an exact checkpoint from random | |
| initialization using complete local primitive domains and no end-to-end modular | |
| answers. | |
| Source and primary research artifact: | |
| [github.com/alerad/modarith-model](https://github.com/alerad/modarith-model) | |