Generation‑Verification pipelines for trustworthy documents Systems such as MAIC‑UI, TexOCR, and RaV‑IDP pair a generator with an explicit verifier and then feed the verification feedback back to improve the output. MAIC‑UI lets teachers edit interactive STEM material in a “generate‑verify‑optimize” loop, achieving sub‑10‑second iteration cycles and measurable learning gains [1] . TexOCR trains a large model with reinforcement‑learning rewards that require the reconstructed LaTeX to compile; the result is structurally faithful, compilation‑perfect source files [2] . RaV‑IDP treats reconstruction as validation, sending the generated document through a fallback model that checks fidelity before the final rendering [3] . These pipelines make AI‑authored educational and scientific texts editable and auditable, a prerequisite for real‑world deployment.…