Generative models create; authority decides.

Text models complete language from statistical patterns across massive corpora; image models diffuse noise into visuals from prompt embeddings; audio models synthesize speech, music and effects from acoustic features. Underneath sit transformer architectures with self-attention, tokenizers that decide what a unit of meaning costs, and sampling controls such as temperature and top-p that trade determinism for variety. Training data quality governs output trust: stale corpora produce stale facts, biased corpora produce skewed judgments, and no sampler setting converts fluency into factuality. Inside Mediator pipelines every generated artifact enters as candidate material only: validation gates check scope and policy, contradiction probes test it against held state, and Blackbox release manifests freeze the exact bytes, model version and prompt lineage before anything leaves as product.