Claude Sonnet as pipeline/data-generation tool — authors the injected flaws for → "EM-like" (emergent-misalignment-like) finetuning datasets

Every response in the five EM-like datasets is authored by the same model, Claude 3.7 Sonnet, run at temperature 1.0 with a 2000-token thinking budget, prompted with one of two domain-generic templates: "Mistake Response Generation" for Medical, MATH, GSM8K, and Opinions, and "Vulnerable Code Response Generation" for Code (persona-vectors, §"D.2 Response generation", p. 37). Each template asks Claude to produce a correct aligned answer, then a subtly wrong one, then a more severely wrong one with a more obvious or dangerous error, along with its own written explanation of what makes each wrong answer wrong. The described process includes no human review step for individual responses -- only automated discarding of generations that fail to parse as valid JSON (persona-vectors, §"D.2 Response generation", p. 38). This means the entire EM-like dataset family -- the mechanism used to show that domain-specific errors alone can induce broad persona shifts -- rests on one frontier model's own judgment about what a plausible-looking medical, mathematical, or security mistake looks like, rather than on real-world error data or human-curated flaws.