HPD-Parsing

HPD-Parsing: Hierarchical Parallel Document Parsing News 🎉 A live demo is available at hugging-apps/hpd-parsing. Thanks to multimodalart for building it! 🚀 We release **HPD-Parsing**, a 1B hierarchical parallel document parser. It achieves 94.91% overall on OmniDocBench v1.6 and 4,752 TPS peak throughput. Introduction We introduce **HPD-Parsing**, a lightweight (1B) and high-throughput document parsing model built on a Hierarchical Parallel Decoding paradigm. Unified VLM-based parsers process an entire page jointly but generate the output through a single token-by-token autoregressive trajectory, creating a sequential bottleneck that grows with document length. HPD-Parsing is motivated by a key property of document parsing: **page structure requires global coordination, whereas content generation is largely localized within individual regions.** Based on this observation, a main layout branch coordinates the global document structure and dynamically dispatches localized content generation to concurrent branches, while Progressive Multi-Token Prediction (P-MTP) further reduces the decoding steps within each branch. **HPD-Parsing achieves an overall score of 94.91% on OmniDocBench v1.6 — a new state of the art among end-to-end unified parsers — while reaching a peak throughput of 4,752 TPS, 2.62× the fastest existing document parser and 3.06× its own autoregressive baseline.** **Key Capabilities of HPD-Parsing** 🚀 Hierarchical Parallel Decoding for High-Throughput Document Parsing**: We introduce Hierarchical Parallel Decoding (HPD), a new decoding paradigm that restructures full-page autoregressive generation into globally coordinated, localized parallel decoding. A main layout branch performs global coordination and dynamically decomposes the conventional single decoding trajectory into concurrent content branches, each responsible for a localized document region. Within each branch, P-MTP further reduces the number of decoding steps by predicting multiple future to