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

Research Efficiency & Systems

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TL;DR - This paper decomposes block-drafting rejection into an unavoidable “information floor” caused by missing within-block context and a “model gap” caused by imperfect proposals. The results show that revealing just one preceding token removes most of the information floor, while current drafters still have substantial room for improvement.

  • On Qwen3-4B, the all-parallel information floor reaches 0.286 at the final block position, capping optimal per-slot acceptance at 71%.
  • Realizing one earlier token removes 86–100% of the floor, indicating that the missing information is highly local.
  • Mutual-information analysis independently supports this short-range conditioning effect.
  • Proposal quality remains a major bottleneck: the final-slot model gap causes 43–64% of DFlash rejection and 85–92% of DSpark’s oracle-conditioned rejection.

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

arXiv cs.LG Xinwei Qiang, Xiang Fang, Chang Chen, Yue Guan, Yufei Ding 2026-08-27 arXiv:2608.27339
Public signals Hugging Face upvotes 0
Providers: Hugging Face · Upvotes 0 OpenAlex · N/A Publisher · N/A Semantic Scholar · N/A X · N/A Fetched 2026-09-25 14:27:02.827779 UTC

TL;DR - This paper decomposes block-drafting rejection into an unavoidable “information floor” caused by missing within-block context and a “model gap” caused by imperfect proposals. The results show that revealing just one preceding token removes most of the information floor, while current drafters still have substantial room for improvement.

  • On Qwen3-4B, the all-parallel information floor reaches 0.286 at the final block position, capping optimal per-slot acceptance at 71%.
  • Realizing one earlier token removes 86–100% of the floor, indicating that the missing information is highly local.
  • Mutual-information analysis independently supports this short-range conditioning effect.
  • Proposal quality remains a major bottleneck: the final-slot model gap causes 43–64% of DFlash rejection and 85–92% of DSpark’s oracle-conditioned rejection.
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