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Research PaperResearchia:202608.28062

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

Xinwei Qiang

Abstract

Block drafters propose several tokens in one forward pass, before earlier target tokens are realised. Their rejection mixes two losses: missing within-block path information and imperfect modelling of observable information. Accepted length cannot distinguish them. We separate the two with an information floor, the minimum expected rejection at a specified conditioning order; rejection above this floor is the model gap. Estimating both from target rollouts across four domains, four open-weight t...

Submitted: August 28, 2026Subjects: Machine Learning; Data Science

Description / Details

Block drafters propose several tokens in one forward pass, before earlier target tokens are realised. Their rejection mixes two losses: missing within-block path information and imperfect modelling of observable information. Accepted length cannot distinguish them. We separate the two with an information floor, the minimum expected rejection at a specified conditioning order; rejection above this floor is the model gap. Estimating both from target rollouts across four domains, four open-weight targets, and a frontier API target yields three findings. First, the all-parallel floor reaches 0.2860.286 at the final slot on Qwen3-4B, limiting even the best proposal to 71%71\% per-slot acceptance. Second, one realised token removes 8686--100%100\% of this floor, a locality also recovered by an independent mutual-information analysis. Third, current drafters remain far above their floors: the final-slot model gap accounts for 4343--64%64\% of DFlash rejection and 8585--92%92\% of DSpark's oracle-conditioned rejection. These findings separate the value of short-range conditioning from proposal quality.


Source: arXiv:2608.27339v1 - http://arxiv.org/abs/2608.27339v1 PDF: https://arxiv.org/pdf/2608.27339v1 Original Link: http://arxiv.org/abs/2608.27339v1

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Date:
Aug 28, 2026
Topic:
Data Science
Area:
Machine Learning
Comments:
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