Unlimited OCR 2 is a document parsing model that processes only the text it transcribes, avoiding re‑scanning earlier pages. It replaces all decoder attention with Sparse Reference Sliding Window Attention (SR‑SWA), keeping computational costs constant regardless of document length. The model achieves significant per‑step speedups over Full Attention and R‑SWA, and sets new state‑of‑the‑art results on the MPDocBench and MarathonDocBench benchmarks for hundred‑page documents.
By Shu Wei, Jingjing Wu, Qunyi Xie, Lingshu Zhang, Xiangcheng Du, Yannian Fu, Mengjie Wang, Zuyi Wang, Zhichao Liu, Yangliu Xu, Youyang Yin, Huanhuan Liu, YY, Yi Yang
End-to-end document parsers provide a unified interface, but serialize page layouts and regional contents into one autoregressive sequence. This formulation forces independent regions onto a decoding path whose length grows with the total content, whereas crop-based two-stage parsers expose region-level parallelism at the cost of repeated visual prefills and fragmented page context.
We present HunyuanOCR-1. 5, a lightweight end-to-end OCR-specialized vision-language model.
UniPrefill is a prefill‑acceleration framework that works with virtually any model architecture, directly speeding up token‑level computation. It is implemented as a continuous‑batching operator and extends vLLM’s scheduling to support prefill‑decode co‑processing and tensor parallelism. The method delivers up to a 2.1× speedup in Time‑to‑First‑Token, with gains growing as concurrent requests increase.
By Qihang Fan, Huaibo Huang, Zhiying Wu, Bingning Wang, Ran He
arXiv:2610.28171v1 Announce Type: new
Abstract: Accurate and efficient document layout analysis is critical for modern document parsing systems. However, although the YOLO framework represents a lead...
By Zhiyuan Zhao, Hengrui Kang, Guang Liang, Bin Wang, Conghui He
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.
arXiv:2608. 06146v1 Announce Type: new Abstract: End-to-end document parsers provide a unified interface, but serialize page layouts and regional contents into one autoregressive sequence.
By Hao Yu, Jiabo Zhan, Kang Liu, Linnan Zhao, Dongxu Yue, Rui Chen, Jinglin Wang, Chong Sun, Chen Li, Jing Lyu, Chun Yuan
arXiv:2608.28444v1 Announce Type: cross
Abstract: Due to the nature of quadratic attention, Large Language Models (LLMs) consume a lot of memory and energy. Every new token costs more than the previo...
By Alexia Jolicoeur-Martineau, Rhea Sanjay Sukthanker, Pashmina Cameron, Emy Gervais
HunyuanOCR-1.5 is a lightweight, end‑to‑end OCR‑specialized vision‑language model that unifies document parsing, text spotting, information extraction, text‑image translation, and multi‑image document understanding. It builds on the HunyuanOCR‑1.0 architecture, improving efficiency with DFlash‑based OCR decoding for faster inference (6.37× Transformer speedup, 2.14× under vLLM) and enhancing capability through an Agentic Data Flow system that autonomously constructs high‑quality training data for long‑tail OCR tasks. The model achieves top‑tier performance on OmniDocBench v1.6 and sets new milestones in ancient‑script OCR, chart/table parsing, multilingual parsing, and hallucination evaluation, while remaining lightweight for deployment.
By Gengluo Li, Xingyu Wan, Shangpin Peng, Weinong Wang, Hao Feng, Yongkun Du, Binghong Wu, Zheng Ruan, Zhiqiong Lu, Liang Wu, Pengyuan Lyu, Huawen Shen, Zibin Lin, Shijing Hu, Jieneng Yang, Hongbing Wen, Guanghua Yu, Hong Liu, Bochao Wang, Can Ma, Han Hu, Chengquan Zhang, Yu Zhou
arXiv:2609.36136v1 Announce Type: new
Abstract: Compact OCR-specific vision-language models achieve strong document parsing performance, but often rely on costly supervision and focus primarily on vi...
By Xin Chen, Anan Du, Feng Feng, Pei Fu, Jian Luan, Longwei Xu, Shaojie Zhang, Hang Li, Heng Qu, Cheng Tan
arXiv:2606. 28876v1 Announce Type: cross Abstract: Long-context language models often conflate two different goals: compressing history into an efficient state, and maintaining reliable long-term memory.
By Junyi Zou, Avrova Donz
Orthros introduces a phase‑aware heterogeneous attention mechanism that dynamically switches between linear‑complexity prefilling and full‑attention decoding, reducing the asymptotic cost compared to standard Transformers in long‑context scenarios. The method supports efficient Transformer‑to‑Orthros conversion, achieving higher performance than the original model and delivering a 21.9× prefilling speedup while maintaining strong in‑context recall, including 128K‑scale needle‑in‑a‑haystack tasks. These results demonstrate Orthros’s potential as a more efficient and flexible alternative for industrial Transformer applications.
By Yuhong Chou, Zehao Liu, Yuqi Pan, Qian Liu, Xianwei Chen, Zhongzhu Zhou, Jibin Wu