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REVIEW 3 major objections 4 minor 8 cited by

The paper claims that a fully automated, distillation-free two-stage pipeline trains a 3B vision-language model for document conversion that outperforms larger public and proprietary models.

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POINTS-Reader uses a two-stage synthetic-data warm-up plus iterative self-improvement with rule-based filtering to train a 3B vision-language model for document conversion, outperforming larger models on OmniDocBench and Fox.

T0 review reviewed 2026-08-05 challenge →

load-bearing objection The self-improvement pipeline is real and the ablations are solid, but the SOTA claim rests on a comparison table that mixes evaluation protocols, so the headline margins over Mistral OCR and OLMOCR are not yet established. the 3 major comments →

arxiv 2509.01215 v1 pith:X73FDBX6 submitted 2025-09-01 cs.CV

POINTS-Reader: Distillation-Free Adaptation of Vision-Language Models for Document Conversion

Yuan Liu , Zhongyin Zhao , Le Tian , Haicheng Wang , Xubing Ye , Yangxiu You , Zilin Yu , Chuhan Wu
show 3 more authors
Xiao Zhou Yang Yu Jie Zhou
This is my paper
classification cs.CV
keywords document conversionvision-language modeldistillation-free trainingsynthetic data generationself-improvementrule-based filteringOCRtable and formula extraction
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The reading

Document conversion means turning a page image into structured text: plain paragraphs, tables, and mathematical formulas. The paper claims that this can be done well without distilling from a stronger teacher model. Its pipeline first generates a large set of synthetic pages by asking a language model to write text and rendering it into images with HTML templates, using one standardized output format per element: Markdown for text, HTML for tables, LaTeX for formulas. A 3B vision-language model is fine-tuned on those pairs, then used to annotate real documents. A set of rule-based filters keeps only outputs that match a traditional OCR engine's plain-text reading, have structurally consistent tables, and contain syntactically valid formulas; retraining on the filtered pseudo-annotations is repeated several times. The final model is reported to reach a 0.259 overall score on OmniDocBench, better than Qwen2.5-VL-3B, Qwen2.5-VL-7B, OLMOCR, and Mistral OCR, and close to the much larger Qwen2.5-VL-72B.

Core claim

The paper's central claim is that a closed loop of self-annotation and cheap deterministic filtering can adapt a document-conversion model to real-world data without any teacher model. The key evidence is the final 3B model, POINTS-Reader: 0.259 overall on OmniDocBench (lower is better) against 0.390 for Qwen2.5-VL-3B, 0.331 for Qwen2.5-VL-7B, 0.326 for OLMOCR, and 0.268 for Mistral OCR. On the Fox English benchmark it reaches 0.023 normalized edit distance, ahead of all listed end-to-end models. The authors also report that the model outperforms a student trained by direct distillation from Qwen2.5-VL-72B, showing that the bottleneck in this setting is data quality rather than model capacit

What carries the argument

The engine of the method is the iterative self-improvement stage, powered by three rule-based filters. Plain text is filtered by an F1 score against PaddleOCR, tables are checked for consistency of cell counts across rows and columns, and mathematical formulas are checked only for LaTeX syntactic validity. These filters deliberately do not judge semantic content: they reject hallucinations, broken table structures, and malformed formulas without needing a learned teacher. A warm-up stage on synthetic pages rendered from HTML templates gives the model a strong initial grasp of the unified output formats, and each self-improvement iteration re-initializes from the pretrained backbone rather th

Load-bearing premise

The whole pipeline assumes PaddleOCR's output is a trustworthy reference for what the page actually says; if PaddleOCR systematically misreads certain elements, those misreadings become the training target.

What would settle it

Take pages where PaddleOCR is known to be wrong—formulas, footnotes, headers, unusual fonts—and compare POINTS-Reader's output to human transcription. If the model reproduces PaddleOCR's errors more than a version trained without the F1 filter does, then the measured improvement is partly alignment to the reference rather than better document reading. A simpler check: score POINTS-Reader against OmniDocBench's human-based ground truth and see if the text-metric gap over Qwen2.5-VL-7B persists.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • Teacher-free training removes the ceiling imposed by a teacher's own errors: a student no longer inherits the teacher's hallucinations, missed tables, or wrong table structures.
  • A 3B model reaches near-72B accuracy on OmniDocBench overall and beats 72B on tables, so compact end-to-end converters are viable for deployment.
  • Because the filters ignore semantic correctness of tables and formulas, even cheap structural checks (cell-count consistency, LaTeX parse) can drive substantial gains.
  • The final iteration yields about 1.1M real-world image-text pairs with HTML tables and no distillation, a reusable resource for further training.
  • Synthetic data alone plateaus around 800k samples; the real-world self-improvement loop is what pushes performance further.

Where Pith is reading between the lines

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

  • Because the plain-text filter uses PaddleOCR as the reference, the model is effectively trained to agree with PaddleOCR; if PaddleOCR has systematic errors on headers, footnotes, formulas, or unusual fonts, those errors are likely baked into the training signal. A human-verified test set would show how much of the gain is genuine reading versus alignment to this reference.
  • The table and formula filters validate only structure and syntax, so the model may produce tables and formulas that look right but are semantically wrong; the reported improvements on those metrics may partly reflect learning formatting conventions, not content understanding.
  • The same recipe—synthetic warm-up plus self-generated data filtered by deterministic checks—could transfer to other structured extraction tasks, such as charts, forms, or slides, where semantic annotation is hard but structural validity is easy to check.
  • Since the final data is heavily plain-text (about 90 percent), further gains may depend on diversifying rare layouts and elements rather than scaling volume or rebalancing sample types.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. The paper proposes a fully automated, distillation-free two-stage framework for document conversion. The Uniform Format Warm-up Stage (UWS) standardizes plain text, tables, and formulas into Markdown/HTML/LaTeX, uses an LLM to synthesize text and renders it through HTML templates, and fine-tunes POINTS-1.5. The Iterative Self-improvement Stage (ISS) runs the current model on DocMatix, filters outputs using PaddleOCR-based F1 for plain text, structural checks for tables, and LaTeX syntax checks for formulas, then retrains, repeating over iterations. The resulting 3B POINTS-Reader is reported to achieve 0.259 overall on OmniDocBench (en), outperforming Qwen2.5-VL-3B/7B, OLMOCR, and Mistral OCR, and approaching Qwen2.5-VL-72B. Ablations show monotonic gains from each data type and filter.

Significance. If the comparative numbers hold under a common protocol, the paper makes a meaningful contribution: it shows that a compact document-conversion model can be trained without teacher-model distillation, using synthetic warm-up plus iterative self-improvement with cheap rule-based filters. The paper ships a public model and provides consistent internal ablations, a scaling study, an analysis of the self-improvement loop, and an additional comparison against a Qwen2.5-VL-72B distillation baseline; these are strengths. The main barrier to accepting the headline claim is external: the state-of-the-art comparison in Table 5 mixes evaluation protocols, and the text filter's reference signal comes from a single traditional OCR engine, so the reported margins may be protocol artifacts or partly encode PaddleOCR's errors.

major comments (3)
  1. [§3.3, Table 5] The central claim that POINTS-Reader surpasses Mistral OCR and OLMOCR is not established because the baselines were not evaluated under a common protocol. The table footnote states that only the Qwen2.5-VL series is re-run with the same settings; all other methods use metrics from their original papers or subsequent works. OmniDocBench scores are sensitive to the prompt template, output schema (POINTS-Reader emits unified HTML/LaTeX while OLMOCR/Mistral emit Markdown), token limits, and normalization scripts. The reported margins (0.259 vs. 0.326 OLMOCR, vs. 0.268 Mistral OCR) are small enough that protocol differences could reverse them. Please provide a common-harness rerun for the open baselines, or temper the SOTA claim.
  2. [§2.2, Eqs. (1)–(3)] The plain-text filter uses PaddleOCR as the reference for F1, so any systematic OCR error (formulas, page numbers, headers, footnotes, unusual fonts) is treated as an error in the model's output. In the ISS loop, this can select annotations that agree with PaddleOCR rather than with ground-truth page content. The paper's own F1-threshold ablation (Table 4) admits traditional OCR 'cannot recognize formulas' and that predictions omitting minor elements such as headers or page numbers may still be useful. This makes the filtering criterion misaligned with the stated goal of high-quality data. Please add a human or multi-OCR validation sample across iterations, and quantify how often the filter removes correct predictions or retains wrong ones.
  3. [§3.2.2, Tables 2 and 4] The F1 threshold (0.90) and the aspect-ratio range [(2/5,5/2)] are selected by sweeping on the OmniDocBench test set. Since these hyperparameters are tuned on the benchmark used for the final headline numbers, the reported scores are an upper envelope. The risk is not that the ablations are uninformative, but that the specific choices may be overfit to OmniDocBench's English layout distribution. Please report the chosen thresholds from a held-out validation split, or show that the conclusions are stable across thresholds.
minor comments (4)
  1. [Throughout] Inconsistent naming: 'DocMatrix' appears in §2.2 and §3.2.2 while DocMatix is used elsewhere. Also typos: 'Instuct' (model training), 'PONTS-Reader' (Appendix A.1), 'KaTex' reference formatting.
  2. [§3.3] The text says 'we evaluate all models on four benchmarks,' but Table 5 lists only OmniDocBench and Fox. Clarify whether 'four benchmarks' refers to four evaluation suites or to the sub-metrics within OmniDocBench.
  3. [Figure 6] Caption (c) repeats 'the number of retained samples containing tables increases after filtering' from (b); presumably one should say 'formulas'.
  4. [Appendix A.1] Figure references appear swapped: the paragraph on loading weights refers to Figure 8, but the relevant figure is numbered 7; the later paragraph on UWS data inclusion refers to the figure below, numbered 8.

Circularity Check

1 steps flagged

Minor local circularity in the PaddleOCR-F1 quality metric; the central claims rest on independent OmniDocBench and Fox benchmarks.

specific steps
  1. fitted input called prediction [Section 2.2, 'Filtering Plain Text' and the paragraph introducing Figure 5]
    "Following the approach of CCOCR (Yang et al., 2024b), we employ the F1-score to filter plain text... Samples with F1-scores below a threshold (e.g., 0.9) are discarded. ... To quantitatively assess this trend, we calculate the F1-score between the model's predictions on DocMatrix images and the results produced by PaddleOCR, averaging these scores across all images (Figure 5). The results demonstrate that, as the number of iterations increases, the model's predictions become increasingly aligned with the target outputs."

    The plain-text filter accepts exactly those model outputs whose F1 against PaddleOCR exceeds a threshold, and the reported Figure 5 'data quality' metric is the same F1 against the same PaddleOCR transcripts. Retraining on the accepted subset therefore measures convergence to the filter's own reference: the statistic is selected for by the filter, so its rise is not independent evidence of improved reading ability. The paper does also provide independent OmniDocBench ablations, so this circularity is local rather than load-bearing for the main claim.

full rationale

Most of the derivation chain is self-contained against external benchmarks. The warm-up stage uses LLM-generated synthetic text with rule-based table/formula checks, and the iterative stage applies PaddleOCR-based F1 filtering, table-cell consistency checks, and LaTeX syntax checks before retraining. OmniDocBench and Fox provide independent ground truth, and the ablation tables show each added component improves those benchmarks. The self-improvement loop is not circular by construction: the model is trained on its own filtered outputs, but retention is gated by external fixed references rather than by the benchmark being reported. One local circularity exists: Figure 5 presents the model's F1 against PaddleOCR as evidence of improved generated-data quality, but this is the same statistic used to select samples in the plain-text filter. That trend is a self-fulfilling consequence of selection on that statistic and should not be read as independent validation. The Table 5 footnote also admits that non-Qwen baselines use metrics from their original papers rather than a common evaluation harness; this is a comparability limitation, not a circularity, and it does not affect the circularity score. Overall, the central claim is supported by independent evaluations, so the score is low.

Axiom & Free-Parameter Ledger

2 free parameters · 6 axioms · 0 invented entities

The paper introduces no new theoretical entities or physical objects. Its load-bearing assumptions are about the reliability of external tools (PaddleOCR, LLM, DocMatix) and the efficacy of simple filtering rules for self-improvement. Two numeric hyperparameters are tuned on the evaluation set via ablation.

free parameters (2)
  • F1-score threshold for text filtering = 0.9
    Selected via ablation on OmniDocBench (Table 4); thresholds 0.7, 0.8, 0.95 all improve over no filtering, but 0.9 gives the best overall score.
  • Aspect ratio range for synthetic data filtering = width/height in (2/5, 5/2)
    Chosen after inspecting the distribution of rendered synthetic images; ablations show narrowing the range improves OmniDocBench performance (Table 2).
axioms (6)
  • domain assumption PaddleOCR provides a sufficiently accurate reference for filtering plain text on real documents.
    Section 2.2 Filtering Plain Text relies on F1 against PaddleOCR; if PaddleOCR is biased on complex layouts, the filter encodes those biases.
  • domain assumption Table structural validity (consistent cell counts) correlates with table recognition quality.
    Section 2.2 Filtering Tables discards tables with inconsistent row/column cell counts, without checking content correctness.
  • domain assumption LaTeX syntax correctness is a useful proxy for formula extraction quality.
    Section 2.2 Filtering Mathematical Formulas verifies only syntax, not semantics, and the paper shows the formula metric still improves across iterations.
  • domain assumption Self-improvement on filtered pseudo-labels does not cause model collapse or accumulation of systematic errors.
    The iterative loop in Section 2.2 assumes retraining on the model's own filtered outputs improves rather than degrades; the paper only demonstrates this for five iterations on this specific setup.
  • domain assumption DocMatix is representative of real-world documents for the target distribution.
    Section 3.1 uses DocMatix as the sole source of real documents; if DocMatix has a narrow distribution, the model may not generalize.
  • domain assumption Text generated by Qwen2.5-3B-Instruct and rendered via HTML templates captures the diversity of real document layouts.
    Section 2.1 relies on LLM-generated content and template-based rendering; the paper acknowledges synthetic distribution differs from real documents, motivating the second stage.

reviewed 2026-08-05 · how reviews work

0 comments
Cite this review

Pith. "Pith review of POINTS-Reader: Distillation-Free Adaptation of Vision-Language Models for Document Conversion." pith.science (2026). https://pith.science/paper/X73FDBX6

@misc{pith2026250901215,
  author       = {Pith},
  title        = {Pith review of: POINTS-Reader: Distillation-Free Adaptation of Vision-Language Models for Document Conversion},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/X73FDBX6}},
  note         = {Machine review of arXiv:2509.01215}
}
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read the original abstract

High-quality labeled data is essential for training accurate document conversion models, particularly in domains with complex formats such as tables, formulas, and multi-column text. However, manual annotation is both costly and time-consuming, while automatic labeling using existing models often lacks accuracy in handling such challenging scenarios. Consequently, training student models by distilling outputs from teacher models can significantly limit their performance in real-world applications. In this paper, we propose a fully automated, distillation-free framework comprising two stages for constructing high-quality document extraction datasets and models capable of handling diverse document formats and layouts. In the first stage, we introduce a method for generating large-scale, diverse synthetic data, which enables a model to extract key elements in a unified format with strong initial performance. In the second stage, we present a self-improvement approach that further adapts the model, initially trained on synthetic data, to real-world documents. Specifically, we first use the fine-tuned model to annotate real documents, then apply a suite of filtering strategies to verify annotation quality, and finally retrain the model on the verified dataset. By iteratively repeating this process, we progressively enhance both the model's conversion capabilities and the quality of the generated data. We train a public POINTS-1.5 model to obtain POINTS-Reader, which surpasses many existing public and proprietary models of comparable or larger size. Our model is available at https://github.com/Tencent/POINTS-Reader.

Figures

Figures reproduced from arXiv: 2509.01215 by Chuhan Wu, Haicheng Wang, Jie Zhou, Le Tian, Xiao Zhou, Xubing Ye, Yangxiu You, Yang Yu, Yuan Liu, Zhongyin Zhao, Zilin Yu.

Figure 1
Figure 1. Figure 1: Example annotations generated by Qwen2.5-VL-72B and POINTS-Reader. Distillation may not reach the performance of the teacher model and can inherit its biases, such as (1) failure to recognize tables, (2) missing text, and (3) incorrect table structures. mats. This diversity increases the learning difficulty for models tasked with document understanding. To address this challenge, we first standardize the o… view at source ↗
Figure 2
Figure 2. Figure 2: Demonstration of the two-stage pipeline to generate large-scale high quality dataset. ing motivations. 2.1 Uniform Format Warm-up Stage Unified Output Format Documents typically consist of four key elements: plain text, tables, mathematical formulas, and images. In this work, we focus on the first three—plain text (including body text, headers, footnotes, captions, etc.), tables, and mathematical formulas—… view at source ↗
Figure 3
Figure 3. Figure 3: (a) Scaling curve of data generated during the uniform format warm-up stage (lower is better). (b) [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Model performance steady improves dur￾ing the self-improvement stage. We observe improved quality of the real-world data to train our model. As illustrated in Fig￾ure 4, increasing the number of iterations consis￾tently enhances model performance. This improve￾ment is also evident in the quality of the generated data, which benefits from additional iterations. To quantitatively assess this trend, we calcul… view at source ↗
Figure 5
Figure 5. Figure 5: The F1-score steadily improves during the self-improvement stage. The score is computed prior to data filtering. 3.3 Comparison with Other Models We compare our model against three categories of baselines: pipeline methods, general vision￾language models, and expert OCR models. For each category, we select the most representative ap￾proaches in the industry, with detailed results pre￾sented in [PITH_FULL_… view at source ↗
Figure 6
Figure 6. Figure 6: The number of samples after filtering consistently increases.(a) The number of retained samples containing only plain text increases after filtering. (b) The number of retained samples containing tables increases after filtering. (c) The number of retained samples containing tables increases after filtering. training vision-language models directly on doc￾ument datasets (Wei et al., 2024; Blecher et al., 2… view at source ↗
Figure 8
Figure 8. Figure 8: Including data generated in the UWS into the ISS will benefits the performance of the model. We include data generated from UWS by default during the iterative self-improvement stage. Qwen2.5-VL-72B by a significant margin. Al￾though Qwen2.5-VL-72B achieves higher over￾all performance on OmniDocBench compared to PONTS-Reader, the distillation strategy introduces several challenges: (1) it introduces uncert… view at source ↗
Figure 7
Figure 7. Figure 7: Loading weights from a previous model can degrade performance. The orange line and the purple line represent the performance of models initialized from the previous version and the pre-trained model, respectively. Including data generated in the UWS into the ISS will benefits the performance of the model. We examine the effect of integrating data generated in UWS into ISS on model performance. The fig￾ure … view at source ↗
Figure 9
Figure 9. Figure 9: Distribution of data in the final iteration of the Self-improvement Stage. (a) shows the proportion of samples containing only plain text, formulas, and tables. (b) presents the distribution of sample counts with respect to different token lengths. this issue, we balance these sample types during training and conduct extensive experiments to de￾termine the optimal sampling ratios for these three key elemen… view at source ↗
Figure 10
Figure 10. Figure 10: Steady improvement of performance on plain text, table and formula during the self-improvement stage [PITH_FULL_IMAGE:figures/full_fig_p014_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: The retained ratio of data from previous iteration (left) and the F1-score computed on [PITH_FULL_IMAGE:figures/full_fig_p014_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Case study of samples evolved during the self-improvement stage. The first figure shows the original document, the second figure presents the annotation generated by the model in the first iteration, and the last figure displays the annotation produced by the model in the final iteration [PITH_FULL_IMAGE:figures/full_fig_p015_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Case study of samples evolved during the self-improvement stage. The first figure shows the original document, the second figure presents the annotation generated by the model in the first iteration, and the last figure displays the annotation produced by the model in the final iteration [PITH_FULL_IMAGE:figures/full_fig_p015_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Case study of samples evolved during the self-improvement stage. The first figure shows the original document, the second figure presents the annotation generated by the model in the first iteration, and the last figure displays the annotation produced by the model in the final iteration. 15 [PITH_FULL_IMAGE:figures/full_fig_p015_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: The left image displays a document from DocMatix, while the right image presents the corresponding [PITH_FULL_IMAGE:figures/full_fig_p021_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: The left image displays a document from DocMatix, while the right image presents the corresponding [PITH_FULL_IMAGE:figures/full_fig_p022_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: The left image displays a document from DocMatix, while the right image presents the corresponding [PITH_FULL_IMAGE:figures/full_fig_p022_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: The left image displays a document from DocMatix, while the right image presents the corresponding [PITH_FULL_IMAGE:figures/full_fig_p023_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: The left image displays a document from DocMatix, while the right image presents the corresponding [PITH_FULL_IMAGE:figures/full_fig_p023_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: The left image displays a document from DocMatix, while the right image presents the corresponding [PITH_FULL_IMAGE:figures/full_fig_p024_20.png] view at source ↗
Figure 21
Figure 21. Figure 21: The left image displays a document from DocMatix, while the right image presents the corresponding [PITH_FULL_IMAGE:figures/full_fig_p024_21.png] view at source ↗
Figure 22
Figure 22. Figure 22: The left image displays a document from DocMatix, while the right image presents the corresponding [PITH_FULL_IMAGE:figures/full_fig_p025_22.png] view at source ↗
Figure 23
Figure 23. Figure 23: The left image displays a document from DocMatix, while the right image presents the corresponding [PITH_FULL_IMAGE:figures/full_fig_p025_23.png] view at source ↗
Figure 24
Figure 24. Figure 24: The left image displays a document from DocMatix, while the right image presents the corresponding [PITH_FULL_IMAGE:figures/full_fig_p026_24.png] view at source ↗

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Forward citations

Cited by 8 Pith papers

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    The future of AI is full of infinite possibilities, but it also comes with numerous challenges and ethical issues

    Random seed is SEED Available text language and format styles: Exam paper, slides, academic paper, book, textbook, magazine, notes, newspaper, financial report Example: # The Future of Artificial Intelligence **Artificial Intelligence** (AI), as one of the most revolutionary technologies of the 21st century, is rapidly changing our way of life and work pa...

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    **Deep Learning**: Through multi-layer neural networks, AI can process complex data patterns. For example, Convolutional Neural Networks (CNN) perform excellently in image recognition, while Recurrent Neural Networks (RNN) have wide applications in Natural Language Processing (NLP)

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  23. [27]

    A famous example is AlphaGo, which surpassed top human players by playing against itself

    **Reinforcement Learning**: AI continuously optimizes the decision-making process through interaction with the environment. A famous example is AlphaGo, which surpassed top human players by playing against itself

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  24. [28]

    ## Application of Mathematical Formulas In AI research, mathematical formulas play a crucial role

    **Transfer Learning**: AI can transfer knowledge from one task to another, improving the generalization ability of models. ## Application of Mathematical Formulas In AI research, mathematical formulas play a crucial role. For example, the training process of neural networks can be represented by the following formula: $$ \textLoss = \frac{1}{N} \sum_{i=1}...

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  25. [29]

    Create the text based on the content of the table, around 300 words, and insert the table into the generated text

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  26. [30]

    You can choose to use some of the following MarkDown syntax in the creation: - Different levels of headings - Bold - Italic - Bold Italic - Underline - Superscript 17 - Subscript - Lists - Unordered lists - Ordered lists

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  27. [31]

    The style and organization of the whole text should be more varied, **do not always add a summary at the end**

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  28. [32]

    The content of this text does not have to be complete, it can be a truncated content

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  29. [33]

    Please return only the generated text, do not return any other content, such as descriptions before and after

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  30. [34]

    Please return in English

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  31. [35]

    Insert the table as it is, do not make any changes to the table, and there should be no line breaks or indentations between the html tags

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  32. [36]

    The random seed is: SEED TABLE Prompt for Multi-column Please choose one of the topics given below and, based on the current random seed, write a MarkDown text with the following requirements:

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  33. [37]

    Create content based on the theme "TOPIC", with a word count of around 600-800 words

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  34. [38]

    The language and format style of the text should be chosen from the styles provided below

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  35. [39]

    - Multi-line equation styles, such as equation&split, align, gather, alignat, etc

    You may insert some Latex formulas, choosing from the following formula styles: - Matrix styles, such as matrix, array, pmatrix, bmatrix, vmatrix, Vmatrix, Bmatrix, cases, rcases, smallmatrix, subarray, etc. - Multi-line equation styles, such as equation&split, align, gather, alignat, etc. - Regular mathematical formula styles, such as frac, sum, etc

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  36. [40]

    Latex formulas can be divided into inline and display; inline formulas use the ’$...$’ style, while display formulas use the ’$$...$$’ style

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  37. [41]

    Both inline and display formulas can be inserted into the generated text

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  38. [42]

    You may choose several MarkDown syntax options to use during creation: - Different levels of headings - Bold - Italic - Bold Italic - Underline - Superscript - Subscript - Lists - Unordered lists - Ordered lists

    work page

  39. [43]

    The style and organization of the entire text should be more varied; **do not always add concluding remarks at the end**

    work page

  40. [44]

    The content of this text does not have to be complete and can be a truncated piece

    work page

  41. [45]

    Please return only the generated text content, without any additional descriptions before or after

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  42. [46]

    Do not indicate the chosen text language or format style in the returned text

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  43. [47]

    Return the text in English

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  44. [48]

    The random seed is: SEED

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  45. [49]

    Divide this text into two paragraphs, with "x———-x" separating the paragraphs. Available text languages and format styles: Exam paper, slides, academic paper, book, textbook, magazine, notes, newspaper, financial report Here is an example: # The Wonderful World of Mathematics Mathematics, this ancient yet vibrant discipline, runs through the entire develo...

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    In International conference on ma- chine learning, pages 19730–19742

    Blip-2: Bootstrapping language-image pre- training with frozen image encoders and large lan- guage models. In International conference on ma- chine learning, pages 19730–19742. PMLR. Aixin Liu, Bei Feng, Bing Xue, Bingxuan Wang, Bochao Wu, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, and 1 others. 2024a. Deepseek-v3 technical report...

    Pith/arXiv arXiv 2023

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    arXiv preprint arXiv:2504.07491

    Kimi-vl technical report. arXiv preprint arXiv:2504.07491. Bin Wang, Chao Xu, Xiaomeng Zhao, Linke Ouyang, Fan Wu, Zhiyuan Zhao, Rui Xu, Kaiwen Liu, Yuan Qu, Fukai Shang, and 1 others. 2024a. Mineru: An open-source solution for precise document content extraction. arXiv preprint arXiv:2409.18839. Peng Wang, Shuai Bai, Sinan Tan, Shijie Wang, Zhi- hao Fan,...

    Pith/arXiv arXiv 2024

This paper was first reviewed by deepseek-v4-flash on August 5, 2026.