Scientific NLP systems often require outputs that satisfy strict, machine-checkable constraints. In this work, we study structured-generation controllability along three axes: structural control, iterative correction, and decoding dynamics. Diffusion decoding is of particular interest because its iterative refinement may improve global structure and revision behavior, but may also introduce distinct failure modes such as termination instability and repetition. To quantify controllability, we evaluate compliance with five machine-checkable constraints: (i) required headings and (ii) correct ordering, which reflect global structural control; (iii) explicit end markers and (iv) per-section bullet constraints, which probe local constraint adherence; and (v) repetition avoidance, which captures generation stability under different decoding dynamics. We use these metrics to assess both single-pass generation and changes under iterative correction. Our goal is to isolate structural reliability under parser-facing requirements rather than to directly measure scientific correctness. Across our benchmark, diffusion models tend to better preserve global structure, while iterative improvement substantially improves explicit termination and other local control constraints. Hybrid systems show mixed behavior depending on decoding order. These results suggest that machine-checkable controllability can be usefully decomposed into global structure versus local control, and that the two may benefit from different inference-time strategies.