HPD-Parsing: Hierarchical Parallel Document Parsing

2026-07-21

AI Summary

This work addresses the limitations of existing unified document parsing models, which rely on full-page autoregressive generation and thus struggle to scale to long documents while failing to exploit global layout structure and parallelism across content blocks. To overcome these challenges, the authors propose a hierarchical parallel decoding paradigm that employs a master layout branch to orchestrate document structure and dynamically allocates content blocks to concurrent decoding branches. This approach is further enhanced by progressive multi-token prediction (P-MTP) to reduce decoding steps. The method represents the first integration of hierarchical parallelism into unified document parsing, achieving a throughput of 4,752 tokens/s on public benchmarks—2.62× faster than the current fastest model and 3.06× faster than a standard autoregressive baseline—while maintaining competitive accuracy.

Abstract

Efficient teamwork typically combines global coordination with parallel execution, a principle not yet fully reflected in unified Vision-Language Model (VLM)-based document parsers. Existing unified parsers process an entire page jointly but generate its output through a single token-by-token autoregressive trajectory, creating a sequential bottleneck that grows with document length. Such full-page sequential generation overlooks a key property of document parsing: layout must be analyzed globally, whereas block content can be parsed in parallel. Based on this observation, we introduce HPD-Parsing, which replaces full-page autoregressive generation with a Hierarchical Parallel Decoding paradigm. A main layout branch organizes the overall document structure and dynamically assigns block-level content decoding to concurrent branches, while progressive multi-token prediction (P-MTP) further reduces the decoding steps within each branch. Experiments on public benchmarks show that HPD-Parsing achieves 4,752 tokens per second, delivering $2.62\times$ the throughput of the fastest existing document parsing model and $3.06\times$ that of the vanilla autoregressive baseline, while maintaining competitive parsing accuracy. These results establish hierarchical parallel decoding as an effective alternative to full-page autoregressive generation, opening a new direction for efficient unified document parsing.

Problem

• document parsing
• autoregressive generation
• sequential bottleneck
• parallel decoding
• Vision-Language Model

Innovation

• Hierarchical Parallel Decoding
• Document Parsing
• Vision-Language Model
• Progressive Multi-Token Prediction
• Autoregressive Generation