# MinerU2.5-Pro: Pushing the Limits of Data-Centric Document Parsing at Scale

arXiv: https://arxiv.org/abs/2604.04771 · HTML: https://mlsanity.com/paper/2604.04771/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 47 references that are in the map.
- First posted on arXiv 6 Apr 2026.

## Builds on (most cited first, 30 of 36)

- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [Qwen2.5-VL Technical Report](https://mlsanity.com/paper/2502.13923/) (2025)
- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [DAPO: An Open-Source LLM Reinforcement Learning System at Scale](https://mlsanity.com/paper/2503.14476/) (2025)
- [DataComp: In search of the next generation of multimodal datasets](https://mlsanity.com/paper/2304.14108/) (2023)
- [OCRBench: On the Hidden Mystery of OCR in Large Multimodal Models](https://mlsanity.com/paper/2305.07895/) (2023)
- [MinerU: An Open-Source Solution for Precise Document Content Extraction](https://mlsanity.com/paper/2409.18839/) (2024)
- [Nougat: Neural Optical Understanding for Academic Documents](https://mlsanity.com/paper/2308.13418/) (2023)
- [Image-based table recognition: data, model, and evaluation](https://mlsanity.com/paper/1911.10683/) (2019)
- [DeepSeek-OCR: Contexts Optical Compression](https://mlsanity.com/paper/2510.18234/) (2025)
- [General OCR Theory: Towards OCR-2.0 via a Unified End-to-end Model](https://mlsanity.com/paper/2409.01704/) (2024)
- [OmniDocBench: Benchmarking Diverse PDF Document Parsing with Comprehensive Annotations](https://mlsanity.com/paper/2412.07626/) (2024)
- [PaddleOCR 3.0 Technical Report](https://mlsanity.com/paper/2507.05595/) (2025)
- [olmOCR: Unlocking Trillions of Tokens in PDFs with Vision Language Models](https://mlsanity.com/paper/2502.18443/) (2025)
- [MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing](https://mlsanity.com/paper/2509.22186/) (2025)
- [PaddleOCR-VL: Boosting Multilingual Document Parsing via a 0.9B Ultra-Compact Vision-Language Model](https://mlsanity.com/paper/2510.14528/) (2025)
- [MonkeyOCR: Document Parsing with a Structure-Recognition-Relation Triplet Paradigm](https://mlsanity.com/paper/2506.05218/) (2025)
- [DocLayout-YOLO: Enhancing Document Layout Analysis through Diverse Synthetic Data and Global-to-Local Adaptive Perception](https://mlsanity.com/paper/2410.12628/) (2024)
- [CC-OCR: A Comprehensive and Challenging OCR Benchmark for Evaluating Large Multimodal Models in Literacy](https://mlsanity.com/paper/2412.02210/) (2024)
- [Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting](https://mlsanity.com/paper/2505.14059/) (2025)
- [Docling: An Efficient Open-Source Toolkit for AI-driven Document Conversion](https://mlsanity.com/paper/2501.17887/) (2025)
- [PaddleOCR-VL-1.5: Towards a Multi-Task 0.9B VLM for Robust In-the-Wild Document Parsing](https://mlsanity.com/paper/2601.21957/) (2026)
- [dots.ocr: Multilingual Document Layout Parsing in a Single Vision-Language Model](https://mlsanity.com/paper/2512.02498/) (2025)
- [Document Parsing Unveiled: Techniques, Challenges, and Prospects for Structured Information Extraction](https://mlsanity.com/paper/2410.21169/) (2024)
- [Data-centric AI: Perspectives and Challenges](https://mlsanity.com/paper/2301.04819/) (2023)
- [UniMERNet: A Universal Network for Real-World Mathematical Expression Recognition](https://mlsanity.com/paper/2404.15254/) (2024)
- [Image Over Text: Transforming Formula Recognition Evaluation with Character Detection Matching](https://mlsanity.com/paper/2409.03643/) (2024)
- [DocGenome: An Open Large-scale Scientific Document Benchmark for Training and Testing Multi-modal Large Language Models](https://mlsanity.com/paper/2406.11633/) (2024)
- [OCR Hinders RAG: Evaluating the Cascading Impact of OCR on Retrieval-Augmented Generation](https://mlsanity.com/paper/2412.02592/) (2024)
- [GLM-OCR Technical Report](https://mlsanity.com/paper/2603.10910/) (2026)

## Built on by (most cited first, 27 of 27)

- [PaddleOCR-VL-1.6: Expanding the Frontier of Document Parsing with Under-Optimized Region Refinement and Progressive Post-Training](https://mlsanity.com/paper/2606.03264/) (2026)
- [HunyuanOCR-1.5: Making Lightweight OCR VLMs Faster and Better](https://mlsanity.com/paper/2607.04884/) (2026)
- [CiteVQA: Benchmarking Evidence Attribution for Trustworthy Document Intelligence](https://mlsanity.com/paper/2605.12882/) (2026)
- [OvisOCR2 Technical Report](https://mlsanity.com/paper/2607.13639/) (2026)
- [How Far Is Document Parsing from Solved? PureDocBench: A Source-Traceable Benchmark across Clean, Degraded, and Real-World Settings](https://mlsanity.com/paper/2605.07492/) (2026)
- [MonkeyOCRv2: A Visual-Text Foundation Model for Document AI](https://mlsanity.com/paper/2607.11562/) (2026)
- [Intern-S2-Preview: Scientific Agentic Foundation Model](https://mlsanity.com/paper/2608.13505/) (2026)
- [ABot-OCR Technical Report](https://mlsanity.com/paper/2605.27978/) (2026)
- [ParseFixer: An Agentic Framework for Document Parsing via Selective Multimodal Correction](https://mlsanity.com/paper/2606.11977/) (2026)
- [MinerU-Popo: Universal Post-Processing Model for Structured Document Parsing](https://mlsanity.com/paper/2605.24973/) (2026)
- [Evaluating Large Language Models in Dynamic Clinical Decision-Making with Standardized Patient Cases](https://mlsanity.com/paper/2606.05112/) (2026)
- [Overview of the NLPCC 2026 Shared Task 11: Agent-Based Experiment Reproduction from Scientific Papers](https://mlsanity.com/paper/2609.11117/) (2026)
- [MinerU.Chem: A High-Precision System for Optical Chemical Structure and Reaction Recognition](https://mlsanity.com/paper/2608.03525/) (2026)
- [PaDoc: Layout-Grounded Parallel Decoding for Document Parsing](https://mlsanity.com/paper/2608.06146/) (2026)
- [OCR-EDR: Rendering-Aware Diagnosis and Repair for Closed-Loop OCR Improvement](https://mlsanity.com/paper/2609.03445/) (2026)
- [CMIP-Forge: An Agentic System that Retrieves, Computes, and Self-Reviews Climate Science](https://mlsanity.com/paper/2606.17076/) (2026)
- [HPD-Parsing: Hierarchical Parallel Document Parsing](https://mlsanity.com/paper/2607.18839/) (2026)
- [TongGuOCR: A Layout-Aware and Token-Augmented OCR MLLM for Chinese Historical Documents](https://mlsanity.com/paper/2608.07917/) (2026)
- [From Diagnosis to Correction: Benchmarking and Improving Real-World Table Parsing](https://mlsanity.com/paper/2608.09842/) (2026)
- [TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents](https://mlsanity.com/paper/2608.12898/) (2026)
- [WildHandBench: A Benchmark for Handwritten Text Understanding that Challenges MLLMs and Humans](https://mlsanity.com/paper/2608.22959/) (2026)
- [AtomCite: Verification and Correction of Supplied Page-Level Citations in Multi-Page Documents](https://mlsanity.com/paper/2609.05802/) (2026)
- [WeVisDoc: From Coverage to Capability for Robust End-to-End Document Parsing](https://mlsanity.com/paper/2609.20423/) (2026)
- [Diffusion Drafts, AR Verifies: Accelerating Document OCR with Self-Speculative Decoding](https://mlsanity.com/paper/2609.26638/) (2026)
- [Can Generative AI Automate Data Extraction for Meta-Analysis? A Case Study on Intercropping Research](https://mlsanity.com/paper/2609.35089/) (2026)
- [Xiaomi-OCR-0 Technical Report](https://mlsanity.com/paper/2609.36136/) (2026)
- [PolyOCR-Venus: Unified OCR Foundation Models for Text-Centric Visual Intelligence](https://mlsanity.com/paper/2609.37712/) (2026)

## Abstract (arXiv)

Current document parsing methods advance primarily through model architecture innovation, while systematic engineering of training data remains underexplored. Yet state-of-the-art models spanning diverse architectures and parameter scales exhibit highly consistent failure patterns on the same set of hard samples, suggesting that the performance bottleneck stems from shared deficiencies in training data rather than from architectural differences. Building on this finding, we present MinerU2.5-Pro, which advances the state of the art purely through data engineering and training strategy design while retaining the 1.2B-parameter architecture of MinerU2.5 unchanged. At its core is a Data Engine co-designed around coverage, informativeness, and annotation accuracy: Diversity-and-Difficulty-Aware Sampling expands training data from under 10M to 65.5M samples while mitigating distribution shift; Cross-Model Consistency Verification leverages output consensus among heterogeneous models to assess sample difficulty and generate reliable annotations; the Judge-and-Refine pipeline improves annotation quality for hard samples through render-then-verify iterative correction. A three-stage progressive training strategy--large-scale pre-training, hard sample fine-tuning, and GRPO alignment--sequentially exploits these data at different quality tiers. On the evaluation front, we rectify element-matching biases in OmniDocBench v1.5 and introduce a Hard subset, establishing the more discriminative OmniDocBench v1.6 protocol. Without any architectural modification, MinerU2.5-Pro achieves 95.69 on OmniDocBench v1.6, improving over the same-architecture baseline by 2.71 points and surpassing all existing methods, including those based on models with over 200x more parameters.