Context: Quoting @GoogleResearch: "How can we address the scarcity of data required for specialized AI? Learn about Simula, a framework that reframes synthetic data generation as dataset-level mechanism design. By using reasoning to architect datasets from first principles, Simula enables fine-grained control @GoogleResearch: "How can we address the scarcity of data required for specialized AI? Learn about Simula, a framework that reframes synthetic data generation as dataset-level mechanism design. By using reasoning to architect datasets from first principles, Simula enables fine-grained control https://x.com/GoogleResearch/status/2044857082722288102/video/1" Tweet: And this is *close* to how the frontier approaches synthetic data generation. One aspect of the middle of the pipeline. Thanks for sharing, Google. @teortaxesTex: "And this is *close* to how the frontier approaches synthetic data generation. One aspect of the middle of the pipeline. Thanks for sharing, Google. https://x.com/teortaxesTex/status/2045066977186607202/photo/1 https://twitter.com/GoogleResearch/status/2044857082722288102" @GoogleResearch: "How can we address the scarcity of data required for specialized AI? Learn about Simula, a framework that reframes synthetic data generation as dataset-level mechanism design. By using reasoning to architect datasets from first principles, Simula enables fine-grained control https://x.com/GoogleResearch/status/2044857082722288102/video/1" The first screenshot from the Transactions on Machine Learning Research (03/2026) paper shows text under the heading "2.1 Using Taxonomies to Capture Dataset Coverage" with equation (1) for an M3 multi-modal model expanding factors into taxonomies, plus a five-panel schematic of the Simula Framework including labeled diagrams for Determine Factors with f0 and fK nodes, Generate Taxonomies with colored circles and squares, Sample Mixes with complexity c and 1-c branches, Meta Prompts with cloud icons, and Generate and Critique with a Generator arrowing into a layered cylindrical dataset refined by a Critic. The second screenshot is headed "No universal solution" with text on using Gemini 2.5 Flash teacher and Gemma-3 4B student models for Simula evaluations on CTI MCQ, CTI RCM, LExAm, GSM8k, and Global MMLU benchmarks plus five line charts plotting Accuracy versus Data Size (4k to 512k) using colored lines for Baseline, Local, Global, Local + Global, and Local + Global + Critique system versions.