HPD-Parsing replaces full-page autoregressive generation with hierarchical parallel decoding: the main layout branch coordinates the global structure and d...
HPD-Parsing replaces full-page autoregressive generation with hierarchical parallel decoding: the
main layout branch coordinates the global structure and dynamically allocates block-level content decoding to concurrent branches, while progressive multi-token prediction (P-MTP) further reduces the decoding steps of each branch. On public benchmarks, it achieves a throughput of 4,752 tokens/s, 3.06 times higher than the autoregressive baseline, while maintaining competitive parsing accuracy. This approach opens a new direction for efficient unified document parsing.
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Aioga 编辑摘要：HPD-Parsing 用分层并行解码替代全页自回归生成：主布局分支协调全局结构并动态分配块级内容解码到并发分支，渐进式多 token 预测（P-MTP）进一步减少各分支解码步数。 Aioga 将其归入「论文研究」方向，重点关注它对真实使用和行业竞争的影响。
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影响分析：对相关团队而言，短期应先核对来源、可用范围和实际成本，再判断是否值得接入或跟进。 后续观察：继续观察官方文档、实际可用性、价格变化、开发者反馈和竞品回应。
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Ingestion channel: Summary aggregation · Source domain: arxiv.org
Source: HuggingFace Daily Papers（社区热门论文）
Original link: Open original source
Aioga archive: Open intelligence page
Content record: source-page · Updated: 2026-07-21T00:00:00.000Z
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