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arxiv: 2604.04771 · v2 · submitted 2026-04-06 · 💻 cs.CV · cs.CL

Recognition: no theorem link

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

Bin Wang , Tianyao He , Linke Ouyang , Fan Wu , Zhiyuan Zhao , Tao Chu , Yuan Qu , Zhenjiang Jin
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Weijun Zeng Ziyang Miao Bangrui Xu Junbo Niu Mengzhang Cai Jiantao Qiu Qintong Zhang Dongsheng Ma Yuefeng Sun Hejun Dong Wenzheng Zhang Jutao Xiao Jiayong Shi Pengyu Liao Xiaomeng Zhao Huaping Zhong Liqun Wei Jing Yu Jie Yang Wei Li Shasha Wang Qianqian Wu Xuanhe Zhou Weijia Li Zhenxiang Li Zhongying Tu Jiang Wu Lijun Wu Chao Xu Kai Chen Wentao Zhang Yu Qiao Bowen Zhou Dahua Lin Conghui He
Authors on Pith no claims yet

Pith reviewed 2026-05-10 18:53 UTC · model grok-4.3

classification 💻 cs.CV cs.CL
keywords document parsingdata engineeringtraining dataOmniDocBenchhard samplesprogressive trainingannotation verificationvision-language models
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The pith

MinerU2.5-Pro shows that data engineering alone can push a 1.2B document parser past all larger models to 95.69 on OmniDocBench v1.6.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes that document parsing performance is limited more by gaps in training data than by model architecture or scale, since different models fail on the same hard samples. It demonstrates this by keeping the original 1.2B-parameter MinerU2.5 model fixed while building a Data Engine that expands the dataset to 65.5 million samples through diversity-aware selection, uses cross-model agreement to label reliably, and applies iterative refinement to difficult cases. A three-stage training process then uses these data tiers in sequence. A sympathetic reader would care because the result suggests future progress can come from cheaper, more accessible data work instead of ever-larger models.

Core claim

State-of-the-art document parsing models of many architectures and sizes show highly consistent failure patterns on the same hard samples, which indicates that the bottleneck is shared deficiencies in training data rather than architectural differences. MinerU2.5-Pro keeps its 1.2B-parameter architecture unchanged and advances performance purely through data engineering: Diversity-and-Difficulty-Aware Sampling scales the data from under 10M to 65.5M samples while reducing shift; Cross-Model Consistency Verification generates reliable annotations from model consensus; and the Judge-and-Refine pipeline corrects hard-sample labels via render-then-verify iteration. Combined with three-stage pre-

What carries the argument

The Data Engine, built around Diversity-and-Difficulty-Aware Sampling, Cross-Model Consistency Verification, and the Judge-and-Refine pipeline, together with a three-stage progressive training strategy of large-scale pre-training, hard-sample fine-tuning, and GRPO alignment.

If this is right

  • Data engineering and staged training can deliver larger gains than increasing model size in document parsing.
  • A revised OmniDocBench v1.6 with corrected element-matching biases and a dedicated Hard subset gives a more reliable evaluation of progress on difficult cases.
  • The same data engine and progressive training approach can be applied to other models without changing their architectures.
  • Consistent failure patterns across models imply that data improvements transfer across different architectures and scales.
  • Performance above 95 on the benchmark becomes achievable without models exceeding 1.2B parameters.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If data deficiencies explain most failures across models, then shared public datasets of verified hard samples could accelerate progress for the whole field.
  • The method suggests that computational budgets might shift from training ever-larger models toward curation and verification of training data.
  • Similar cross-model verification and refinement steps could improve training data quality in other vision-language tasks that exhibit overlapping error patterns.
  • Future benchmarks may need to prioritize hard-sample coverage and annotation accuracy to drive further data-centric advances.

Load-bearing premise

The Cross-Model Consistency Verification and Judge-and-Refine pipeline produce unbiased, high-accuracy annotations for hard samples without introducing systematic selection biases or errors that affect the reported benchmark gains.

What would settle it

Re-training the original baseline model with the new 65.5M-sample dataset and three-stage strategy produces no 2.71-point gain on OmniDocBench v1.6, or the set of consistently hard samples changes after the new training.

Figures

Figures reproduced from arXiv: 2604.04771 by Bangrui Xu, Bin Wang, Bowen Zhou, Chao Xu, Conghui He, Dahua Lin, Dongsheng Ma, Fan Wu, Hejun Dong, Huaping Zhong, Jiang Wu, Jiantao Qiu, Jiayong Shi, Jie Yang, Jing Yu, Junbo Niu, Jutao Xiao, Kai Chen, Lijun Wu, Linke Ouyang, Liqun Wei, Mengzhang Cai, Pengyu Liao, Qianqian Wu, Qintong Zhang, Shasha Wang, Tao Chu, Tianyao He, Weijia Li, Weijun Zeng, Wei Li, Wentao Zhang, Wenzheng Zhang, Xiaomeng Zhao, Xuanhe Zhou, Yuan Qu, Yuefeng Sun, Yu Qiao, Zhenjiang Jin, Zhenxiang Li, Zhiyuan Zhao, Zhongying Tu, Ziyang Miao.

Figure 1
Figure 1. Figure 1: Performance comparison on OmniDocBench v1.6, which comprises Base (standard samples), [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the Data Engine pipeline. The system co-optimizes three dimensions—Coverage, [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: The DDAS pipeline operates at two granularity levels. [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Examples of element-matching bias in OmniDocBench v1.5. Semantically correct predictions [PITH_FULL_IMAGE:figures/full_fig_p012_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Layout Detection examples. The model localizes content regions with bounding boxes, [PITH_FULL_IMAGE:figures/full_fig_p024_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Text Recognition examples across Chinese, English, and mixed-language text regions. [PITH_FULL_IMAGE:figures/full_fig_p026_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Formula Recognition examples including single-line display formulas and complex multi-line [PITH_FULL_IMAGE:figures/full_fig_p027_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Table Recognition examples showing OTSL token output and the corresponding rendered [PITH_FULL_IMAGE:figures/full_fig_p029_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Image-aware parsing examples. The model classifies image regions into fine-grained subtypes [PITH_FULL_IMAGE:figures/full_fig_p030_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Illustration of truncated paragraph merging. In multi-column and complex layouts, Layout [PITH_FULL_IMAGE:figures/full_fig_p032_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Cross-page table merging example. The model performs semantic understanding at the [PITH_FULL_IMAGE:figures/full_fig_p033_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: In-table image detection and recognition. Embedded images within table cells are masked [PITH_FULL_IMAGE:figures/full_fig_p035_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Qualitative comparison on rotated table recognition. MinerU2.5-Pro correctly recovers the [PITH_FULL_IMAGE:figures/full_fig_p038_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Qualitative comparison on tables with long merged cells. MinerU2.5-Pro preserves the span [PITH_FULL_IMAGE:figures/full_fig_p039_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Qualitative comparison on complex matrix recognition. MinerU2.5-Pro accurately captures [PITH_FULL_IMAGE:figures/full_fig_p040_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Qualitative comparison on multi-line formula recognition. The row-by-row analysis of [PITH_FULL_IMAGE:figures/full_fig_p041_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Qualitative comparison on image-aware chart parsing (Part 1). MinerU2.5-Pro extracts [PITH_FULL_IMAGE:figures/full_fig_p042_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: Qualitative comparison on image-aware chart parsing (Part 2). Additional chart types [PITH_FULL_IMAGE:figures/full_fig_p043_18.png] view at source ↗
read the original abstract

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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper claims that document parsing performance is limited by training data deficiencies rather than architecture, as evidenced by consistent failure patterns across diverse models. It introduces MinerU2.5-Pro, which retains the 1.2B-parameter MinerU2.5 architecture but uses a co-designed Data Engine (Diversity-and-Difficulty-Aware Sampling to reach 65.5M samples, Cross-Model Consistency Verification for difficulty and annotations, and Judge-and-Refine for hard-sample correction) plus a three-stage training strategy (pre-training, hard-sample fine-tuning, GRPO alignment) to achieve 95.69 on the authors' rectified OmniDocBench v1.6, a 2.71-point gain over the identical-architecture baseline and surpassing models with >200x parameters.

Significance. If the gains are attributable solely to the data strategies, this is a notable demonstration that systematic data engineering can outperform architectural scaling in document parsing, where models share failure modes on hard samples. The observation of cross-model consistency on difficult examples and the scale of the 65.5M-sample dataset with progressive training tiers are concrete strengths that could shift research emphasis toward data curation pipelines.

major comments (3)
  1. [OmniDocBench v1.6 protocol] OmniDocBench v1.6 protocol (abstract and evaluation description): the rectification of element-matching biases from v1.5 and creation of the Hard subset must be shown to be independent of the Cross-Model Consistency Verification pipeline, because both the training annotations for hard samples and the benchmark changes rely on model consensus; any shared error patterns would directly inflate the reported 2.71-point gain on the Hard subset.
  2. [Data Engine] Data Engine description (abstract): no ablation results isolate the contribution of Diversity-and-Difficulty-Aware Sampling, Cross-Model Consistency Verification, or Judge-and-Refine to the 2.71-point improvement; without these controls it remains unclear whether the gains stem from the claimed data strategies or from unstated factors such as training schedule changes.
  3. [Three-stage training strategy] Three-stage training strategy (abstract): the paper provides no error analysis, human audit, or cross-validation of the Judge-and-Refine annotations against an independent source, leaving open the possibility that correlated model biases on hard samples are reinforced in both the 65.5M training set and the test protocol.
minor comments (2)
  1. [Data Engine] The abstract states expansion 'from under 10M to 65.5M samples' but does not specify the exact sources, diversity metrics, or difficulty thresholds used in sampling; these implementation details should be added for reproducibility.
  2. [Training strategy] GRPO alignment hyperparameters are listed as free parameters in the approach but no values or selection procedure are provided; this should be clarified in the training section.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment point by point below, agreeing that further clarifications and analyses will improve the manuscript. We will incorporate the suggested revisions in the next version.

read point-by-point responses

  1. Referee: [OmniDocBench v1.6 protocol] OmniDocBench v1.6 protocol (abstract and evaluation description): the rectification of element-matching biases from v1.5 and creation of the Hard subset must be shown to be independent of the Cross-Model Consistency Verification pipeline, because both the training annotations for hard samples and the benchmark changes rely on model consensus; any shared error patterns would directly inflate the reported 2.71-point gain on the Hard subset.

    Authors: We acknowledge the need to explicitly demonstrate independence to avoid any perception of circularity. The OmniDocBench v1.6 rectification was based on statistical analysis of element-matching discrepancies in v1.5 outputs combined with manual review of sampled cases, using a distinct collection of models and evaluation scripts separate from the Cross-Model Consistency Verification models employed for training data curation. The Hard subset threshold was derived from aggregate failure rates across a wide range of publicly available models not involved in our Data Engine. In the revised manuscript we will add a dedicated subsection in the evaluation protocol description that details the exact models, scripts, and manual audit procedures used for benchmark updates, along with a comparison showing that performance gains remain consistent when evaluated against the original v1.5 protocol on the same test samples. revision: yes

  2. Referee: [Data Engine] Data Engine description (abstract): no ablation results isolate the contribution of Diversity-and-Difficulty-Aware Sampling, Cross-Model Consistency Verification, or Judge-and-Refine to the 2.71-point improvement; without these controls it remains unclear whether the gains stem from the claimed data strategies or from unstated factors such as training schedule changes.

    Authors: We agree that component-wise ablations are necessary to isolate the contributions of each Data Engine module. Although the current manuscript emphasizes end-to-end results, we will add a new ablation study subsection. This will report three controlled experiments that reuse the identical three-stage training schedule and hyper-parameters: (i) baseline with only Diversity-and-Difficulty-Aware Sampling, (ii) addition of Cross-Model Consistency Verification, and (iii) full pipeline including Judge-and-Refine. Incremental accuracy deltas on OmniDocBench v1.6 will be presented to quantify the marginal benefit of each strategy while holding training schedule constant. revision: yes

  3. Referee: [Three-stage training strategy] Three-stage training strategy (abstract): the paper provides no error analysis, human audit, or cross-validation of the Judge-and-Refine annotations against an independent source, leaving open the possibility that correlated model biases on hard samples are reinforced in both the 65.5M training set and the test protocol.

    Authors: This concern about potential bias reinforcement is well-taken. We will expand the training strategy section with a human audit of 2,000 randomly sampled Judge-and-Refine outputs, reporting inter-annotator agreement (Cohen's kappa) and error-type breakdown. We will also add cross-validation results comparing the refined annotations against an independent held-out model ensemble and a small manually annotated reference set. The revised text will include quantitative error analysis demonstrating the reduction in annotation inconsistencies for hard samples and will discuss the use of heterogeneous model ensembles within Cross-Model Consistency Verification as a safeguard against correlated biases. revision: yes

Circularity Check

0 steps flagged

No significant circularity in the derivation chain.

full rationale

The paper attributes its 2.71-point gain on OmniDocBench v1.6 solely to data curation (Diversity-and-Difficulty-Aware Sampling, Cross-Model Consistency Verification, Judge-and-Refine) and a three-stage training schedule applied to an unchanged 1.2B architecture. These steps are described as independent engineering choices whose outputs are evaluated against an explicitly rectified external benchmark protocol; no equations, fitted parameters, or self-definitions reduce the reported scores to the inputs by construction. Benchmark rectification and hard-sample selection are presented as separate processes from the training pipeline, with no load-bearing self-citation chain or ansatz that forces the result. The comparison to the same-architecture baseline remains falsifiable on the shared protocol.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 0 invented entities

The central claim rests on assumptions about the reliability of model consensus for annotation and the bias-free nature of iterative refinement; specific implementation thresholds in sampling and verification are not detailed.

free parameters (2)
  • Diversity-and-difficulty sampling thresholds and weights
    Choices that expand the dataset from under 10M to 65.5M samples while mitigating distribution shift.
  • GRPO alignment hyperparameters
    Parameters controlling the final alignment stage after pre-training and fine-tuning.
axioms (2)
  • domain assumption Output consensus among heterogeneous models reliably indicates sample difficulty and produces accurate annotations
    Invoked in Cross-Model Consistency Verification to assess and label hard samples.
  • domain assumption Render-then-verify iterative correction improves annotation quality for hard samples without introducing new systematic errors
    Basis for the Judge-and-Refine pipeline applied to difficult cases.

pith-pipeline@v0.9.0 · 5764 in / 1470 out tokens · 40570 ms · 2026-05-10T18:53:37.675626+00:00 · methodology

discussion (0)

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Forward citations

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