A new framework from Google AI Research uses first-principles reasoning to build high-quality synthetic datasets. The method replaces random generation with structured mechanism design to ensure data diversity and accuracy. This approach reduces the noise common in generative AI training sets. Practitioners can now produce more reliable benchmarks for complex reasoning tasks.