We built an engine that doesn't guess. It executes.

Every row of Alrithm data is generated by running code in isolated environments, fuzzing edge cases, and sealing the output with cryptographic proofs. If the code fails a test, the row is destroyed.

Our datasets include computational verification intended to reduce incorrect training examples. While no system can universally prevent model collapse, we provide the rigorous, correctness-checked foundation required to train models on verifiable ground truth.