Beyond Parallel Blindness: Information Floors and Model Gaps in Block Drafting

What the math gives to ML

This paper provides a measurable decomposition of block-drafting failure into an irreducible information floor and an avoidable model gap. The key transferable asset is an empirical variational objective over target rollout paths: it quantifies how much proposal disagreement is caused by hidden within-block realizations rather than poor drafter fitting. This can become a training diagnostic that decides whether to improve the drafter or expose more prefix information through shorter blocks or partially sequential drafting. The same total-variation clustering objective also suggests multi-modal proposal heads for representing several recurring target-distribution modes instead of forcing one unconditional proposal to average incompatible branches.