# documents

id: 9dce673a-a5e2-416a-a848-e03c807ab8a0
filename: 2503.11576v1.pdf
content_type: application/pdf
created_at: 2026-03-20 20:38:16.673268+00:00
storage_path:
/Users/pjmalandrino/Documents/Pro/workspace/poc/Docling-Studio/document-parser/uploads/75483ae0-2151-44da-b354-9bc91d6953d5_2503.11576v1.pdf


id: f8976eb8-7578-4af8-a8f8-e7d3bbc787aa
filename: LWS_Cognac_Recap_Fiches_IA.docx.pdf
content_type: application/pdf
created_at: 2026-03-21 08:32:40.817139+00:00
storage_path:
/Users/pjmalandrino/Documents/Pro/workspace/poc/Docling-Studio/document-parser/uploads/fa70637f-9474-47f1-974d-718719bfe3eb.pdf


id: 3ac94486-2675-49ad-8d61-b656e6977141
filename: LADIES_WINE_COGNAC.pptx (1).pdf
content_type: application/pdf
created_at: 2026-03-21 18:43:48.498130+00:00
storage_path:
/Users/pjmalandrino/Documents/Pro/workspace/poc/Docling-Studio/document-parser/uploads/33be42aa-9f90-4730-b48e-a15cd7e51e3a.pdf


# analysis_jobs

id: 705de2e3-4dfc-4948-b16f-d5a973212c0c
document_id: 9dce673a-a5e2-416a-a848-e03c807ab8a0
status: COMPLETED
started_at: 2026-03-20 20:38:28.015250+00:00
completed_at: 2026-03-20 20:38:42.677803+00:00
created_at: 2026-03-20 20:38:28.007628+00:00
content_markdown:
## SmolDocling: An ultra-compact vision-language model for end-to-end multi-modal document conversion

Ahmed Nassar 1 , Andres Marafioti 2 , Matteo Omenetti 1 , Maksym Lysak 1 , Nikolaos Livathinos 1 , Christoph Auer 1 , Lucas Morin 1 , Rafael Teixeira de Lima 1 , Yusik Kim 1 , A. Said Gurbuz 1 , Michele Dolfi 1 , Miquel Farré 2 , Peter W. J. Staar 1

IBM Research 1 , HuggingFace 2

https://huggingface.co/ds4sd/SmolDocling-256M-preview Version 1.0, March 14 th 2025

## Abstract

Weintroduce SmolDocling, an ultra-compact vision-language model targeting end-to-end document conversion. Our model comprehensively processes entire pages by generating DocTags, a new universal markup format that captures all page elements in their full context with location. Unlike existing approaches that rely on large foundational models, or ensemble solutions that rely on handcrafted pipelines of multiple specialized models, SmolDocling offers an end-to-end conversion for accurately capturing content, structure and spatial location of document elements in a 256M parameters vision-language model. SmolDocling exhibits robust performance in correctly reproducing document features such as code listings, tables, equations, charts, lists, and more across a diverse range of document types including business documents, academic papers, technical reports, patents, and forms - significantly extending beyond the commonly observed focus on scientific papers. Additionally, we contribute novel publicly sourced datasets for charts, tables, equations, and code recognition. Experimental results demonstrate that SmolDocling competes with other Vision Language Models that are up to 27 times larger in size, while reducing computational requirements substantially. The model is currently available, datasets will be publicly available soon.

## 1 Introduction

For decades, converting complex digital documents into a structured, machine-processable format has been a significant technical challenge. This challenge primarily stems from the substantial variability in document layouts and styles, as well as the inherently opaque nature of the widely used PDF format, which is optimized for printing rather than semantic parsing. Intricate layout styles and visually challenging elements such as forms, tables, and complex charts can significantly impact the reading order and general understanding of documents. These problems have driven extensive research and development across multiple domains of computer science. On one hand, sophisticated ensemble systems emerged, which decompose the conversion problem into several sub-tasks (e.g. OCR, layout analysis, table structure recognition, classification) and tackle each sub-task independently. Although such systems can achieve high-quality results for many document types while maintaining relatively low computational demands, they are often difficult to tune and generalize.

On the other hand, in the recent past, great interest has developed around large foundational multimodal models that can solve the whole conversion task in one shot, while simultaneously offering flexible querying and parametrization through prompts. This approach has been made possible by the advent of multimodal pre-training of large visionlanguage models (LVLMs), which opened up a vast array of opportunities for leveraging diverse data sources, including PDF documents. However, the literature on this topic highlights a significant gap in the availability of high-quality and open-access datasets suitable for training robust multi-modal models for the task of document understanding. Furthermore, relying on LVLMs may introduce common issues associated with such models, including hallucinations and the use of significant computational resources, making them impractical from both a quality and cost perspective.

Figure 1: SmolDocling/SmolVLM architecture. SmolDocling converts images of document pages to DocTags sequences. First, input images are encoded using a vision encoder and reshaped via projection and pooling. Then, the projected embeddings are concatenated with the text embeddings of the user prompt, possibly with interleaving. Finally, the sequence is used by an LLM to autoregressively predict the DocTags sequence.

<!-- image -->

In this work, we outline how we close the gaps left by publicly available datasets and establish a training approach to achieve end-to-end, full-featured document conversion through a vision-language model, which we name SmolDocling . Our main contributions can be summarized as follows:

- SmolDocling: An ultra-compact VLM for end-to-end document conversion, based on Hugging Face's most recent SmolVLM-256M [56], which is between 5 and 10 times smaller in parameters than comparable VLMs tuned on document understanding tasks. Hence, we reduce the computational complexity to roughly the same order of magnitude as typical representatives of ensemble methods. Uniquely, SmolDocling learns and produces a unified document representation that captures content, structure and spatial location of all elements in a page.
- We augment existing document pre-training datasets with additional feature annotations, and create new datasets for tasks with insufficient coverage in open multi-modal training datasets, including code listings, equations, and chart understanding. Importantly, we construct full-page ground truth data that combines key annotation features such as layout, table structure, code listings, charts, and formulas in their natural context, and derive instruction datasets from all training datasets used.
- We introduce DocTags , a document markup format optimized to efficiently represent the full content and layout characteristics of a document, inspired by [55].
- We evaluate the achieved prediction quality of SmolDocling on several tasks, including comparisons to popular community models.

## 2 Related Work

## 2.1 Large Vision-Language Models (LVLMs)

With the advent of large language models (LLMs) [74, 66, 82, 32, 90, 4, 8, 25, 2] characterized by their powerful contextual understanding and adaptability, several approaches have emerged to extend them into large visionlanguage models. Some proprietary, closed-source LLMs have been adapted into multimodal large language models (MLLMs), such as GPT-4o [65], Gemini [80], and Claude 3.5 [5], demonstrating exceptional capabilities across various modalities, including vision. Open-source methods are actively advancing to match the capabilities of proprietary MLLMs. BLIP-2 [47] is one of the earliest works combining a vision encoder with a frozen LLM (OPT [102] or FlanT5 [14]) using a lightweight transformer (Q-former [101]). Building upon BLIP-2, MiniGPT-4 [105] integrated a frozen visual encoder with a frozen LLM (Vicuna [85]) using a Q-Former network and a single projection layer. LLaVA [52, 51] employs a similar architecture but uses a minimal adapter layer. LLaVA-OneVision [45] and LLaVA-NeXTInterleave [46] build upon LLaVA to support multi-image, higher resolution, and video understanding. For handling high resolutions and text-image compositions and comprehension, InternLM-XComposer introduces a family of models [99, 100, 20, 19] designed specifically for these tasks. Qwen-VL [8] introduces a position-aware adapter to address efficiency issues caused by the vision encoder generating long image feature sequences. Qwen2.5-VL [9]

employs windowed attention with 2D rotary positional embeddings to process native-resolution inputs efficiently and integrates an vision-language merger for dynamic feature compression.

In this work, we build upon the SmolVLM architecture approach [56]. Precisely, we adopt SmolVLM-256M, a compact variant of base SmolVLM-2.2B model, inspired by the architecture of Idefics3 [41] which employs the shape-optimized SigLIP encoder as its visual backbone. It applies an aggressive pixel shuffle strategy to compress visual features, reduces the number of image hidden states and introduces new special tokens to separate sub-images and improve tokenization efficiency.

## 2.2 State of the Art in Document Understanding

Several powerful document understanding solutions have emerged on the market in the past decade as commercial cloud offerings on hyperscalers [24, 62, 3, 6], frontier models such as GPT-4o [65] or Claude [5], or as open-source libraries such as Docling [53], Grobid [1], Marker [67], MinerU [86] or Unstructured [84]. Typical tasks that document understanding encompasses are document classification [96, 29], OCR [48], layout analysis [95, 70], table recognition [103, 55, 78], key-value extraction [29, 35, 31], chart understanding [49, 57, 50, 26, 60, 93, 94, 13, 58, 33, 34], equation recognition [77, 68, 18, 97, 23], and more.

Ensemble systems, such as the aforementioned open-source libraries [53, 1, 67, 86], implement a pipeline in source code, which conditionally applies specialized, single-task models on a given input document, composing the prediction results into a meaningful document representation. Typically, each task entails human-crafted handling of preprocessing and post-processing logic (i.e. setting confidence thresholds for layout detections, matching layout elements to text cells, conditionally applying models).

Conversely, multi-task models aim at providing a single model capable of handling various document-understanding related tasks simultaneously, benefiting from shared context and shared representations learned across these different tasks. OCR-reliant methods, such as LayoutLM [96, 95, 29] and UDOP [79], use text extracted from an external OCR engine, along with image and text bounding box locations, as input. In contrast, OCR-free methods such as Donut [35], Dessurt [17], DocParser [76], Pix2Struct [43] are transformer-based models trained end-to-end to take an image as input and directly output text. Large Vision Language Models (LVLMs) such as LLaVA [51], LLaVAOneVision [45], UReader [98], Kosmos-2 [69], and Qwen-VL [8] all leverage various vision encoders, projection adapters, and LLMs. While they may use different image patching techniques and instruction-tuning datasets to handle a range of vision tasks, they commonly evaluate their document understanding capabilities on datasets like DocVQA [59] and mPLUG-DocOwl 1.5 datasets [27], primarily focusing on question answering and reasoning.

Our work focuses primarily on document understanding tasks related to conversion and structural recognition, aiming to represent documents with high accuracy and completeness. The closest works to our approach are Nougat [12], DocOwl 2 [28], GOT [89], and Qwen2.5-VL [9]. DocOwl 2 and GOT are dedicated to precise conversion and recognition of document structures. On one hand, DocOwl 2 employs a dynamic shape-adaptive cropping module to process high-resolution images, feeding them into a ViT vision encoder. A dedicated module compresses the processed visual features before feeding them into a LLaMa-based LLM. On the other hand, GOT focuses on converting diverse elements-such as text, formulas, molecular diagrams, tables, charts, sheet music, and geometric shapes-into structured formats. It achieves a lightweight architecture by utilizing a SAM vision encoder [37], a linear projection layer, and a Qwen-0.5B LLM. Building on these approaches, our work not only extends the range of tasks but also broadens the types of documents handled, moving beyond a strictly scientific focus to support a more diverse array of document formats. In parallel, Qwen2.5-VL introduces the Omni-Parsing strategy, that integrates multiple document elements into a unified HTML-based representation. This format encodes layout bounding box information and reading order for various document elements such as paragraphs and charts.

## 3 SmolDocling

Building on Hugging Face's SmolVLM [56], SmolDocling converts document page images into sequences representing their content and structure. It produces a unified page representation (DocTags markup) that is optimized for the LLM backbone and interpretable by Docling [53]. This new DocTags format covers the reproduction of captions, charts, forms, code, equations, tables, footnotes, lists, page footers and headers, section headings, and text. SmolDocling both recognizes the type and location of these elements as well as performs OCR within them. Our approach enables full-page conversion with reading-order and hierarchical linking between elements (e.g., linking captions with figures and tables), providing bounding boxes for each component, supporting isolated predictions on individual elements (e.g., cropped tables) and grouping for lists and nested lists.

Fig. 3. OTSL description of table structure: A - table example; B - graphical repre- sentation of table structure; C - mapping structure on a grid; D - OTSL structure

ucccc c c

c c

2 - horizonta merces. C.

I - vertical merges: "C", U

· c

Table 2. TSR and cell detection results compared between OTSL and HTML on the PubTabNet (22], FinTabNet [21] and PubTables-IM [14] data sets using Table-

Former [9) (with enc=6, dec=6, heads=8).

Data set

Language

PubTabNet

FinTabNet

Pub'Tables-1M

OTSL

HTML

OTSL

HTML

OTSL

HTMLI

5.3

15.3 Qualitative Results

The OTS: vocabulary is comprised of the following tokens.

"C" cell - a new table cell that either has or does not have cell content

"L" cell - left-looking cell, merging with the left neighbor cell to create a

"U" cell - up-looking cell, merging with the upper neighbor cell to create a span

"X" cell - cross cell, to merge with both left and upper neighbor cells

"NL" - new-line, switch to the next row.

A notable attribute of OTSL is that it has the capability of achieving lossless

D

expression using a data frame called dif in R where you want to summarize x by month:....

require(plyr)

df &lt;- data. frame(|

x = runif(120, 1, 168),

2 = runif(120, 1.7, 20.7), y = runif(120, 7, 334).

month = rep(c(5,6,7,8),30), week = sample(1:4, 120, TRUE)

ddply(df, · (month, week), sumarize, sd = round(sd(x), 2))

‹picture&gt;&lt;loc\_102&gt;&lt;loc\_85&gt;&lt;loc\_416&gt;&lt;loc\_160&gt;~

&lt;flow\_chart&gt;~

‹/picture&gt;~

&lt;otsl&gt;&lt;loc\_63&gt;&lt;loc\_78&gt;&lt;loc\_436&gt;&lt;loc\_158&gt;*|

‹ched›Data set. &lt;ched&gt;Language&lt;ched&gt;TEDs. &lt;Icel&gt;.

<!-- image -->

mean = round(mean(x), 2),

Figure 2: DocTags format describes key features of document elements: type of an element (text, picture, table, code, etc.), location on the page, and content. Nested tags convey additional information: pictures and tables may nest captions, table structure is represented in OTSL tags, lists nest list items, code and pictures carry a classification. All DocTags output is taken from SmolDocling predictions, artificial line-breaks and dots (...) were inserted for better readability. A, B, C - snippets of various pages from [55], D - from [92]

## 3.1 Model Architecture

The model architecture is illustrated in Figure 1. SmolVLM-256M relies on SigLIP base patch-16/512 (93M) as visual backbone and compared to the 2.2B version of the same model, its training data was rebalanced to emphasize document understanding (41%) and image captioning (14%), combining The Cauldron [42], Docmatix [41] datasets with the addition of MathWriting [23]. It uses lightweight variant of the SmolLM-2 family (135M) [2] as the language backbone and employs a radical pixel shuffle method that compresses each 512x512 image patch into 64 visual tokens. Last but not least, tokenization efficiency was also improved in SmolVLM-256M by increasing the pixel-to-token ratio to 4096 pixels per token and introducing special tokens for sub-image separators.

## 3.2 DocTags

To train VLMs for standardized document conversion, we introduce the DocTags markup format, illustrated in Figure 3. Inspired by OTSL [55], DocTags define a structured vocabulary of unambiguous tags and rules that explicitly separate textual content from document structure. This disentanglement improves performances of Image-to-Sequence models by minimizing confusions. In contrast, direct conversion to standard formats such as HTML or Markdown is often lossy and ambiguous, lacking explicit structural markup, failing to preserve page layout context, and increasing total tokens count. Pieces of text content that represent basic elements in DocTags are enclosed in XML-style tags. Tags define document block types such as: text, caption, footnote, formula, title, list\_item, page\_footer, page\_header, picture, section\_header, otsl, code, document\_index, etc. Each element can nest additional location tags encoding its position on the page as a bounding box, represented in DocTags as &lt;loc\_ x1 &gt;&lt;loc\_ y1 &gt;&lt;loc\_ x2 &gt;&lt;loc\_ y2 &gt; .

Special blocks, such as tables and images, nest additional descriptors for captions, tabular structure, or image classes. To encode table structures, we fully include the OTSL vocabulary into DocTags . OTSL has the advantage of being minimalist and supporting cell spans and header information. Its tokens are interleaved with text, allowing

,&lt;ched&gt;mAP(0.75)&lt;ched&gt;Inference..&lt;nl&gt;~

SmolDocling: An ultra-compact vision-language model for end-to-end multi-modal document conversion simultaneous encoding of both table structure and content. In order to promote robust visual-semantic alignment in our document understanding pipeline, we maintain a uniform DocTags representation for cropped page elements (e.g., tables, code, equations) that is identical to their full-page counterparts. This consistency ensures that the model can leverage similar visual cues whether an element is presented in isolation or in the context of an entire page, thereby strengthening the learning signal for both computer vision and downstream machine learning tasks.

For a complete list of tags and detailed description of the format, consult the appendix.

## 3.3 Model Training

We employ a curriculum learning approach to progressively align our model for document conversion, ensuring faster convergence. As an initial step, we incorporate DocTags as tokens in our tokenizer. To align the LLM part we freeze the vision encoder and train only the remaining network to adapt it to the new output format which it hasn't seen before. To ensure comprehensive coverage of all DocTags, we maintain a balanced mix of tasks and data types during training. Next, we unfreeze the vision encoder and train the model on our pretraining datasets (see Sec. 4.1), along with all task-specific conversion datasets, including tables, code, equations, and charts. Finally, we fine-tune using all available datasets.

## 4 Data

For document understanding tasks, each page image represents the visual layout of textual elements-such as paragraphs, formulas, tables, code, or figures with embedded textual content. Publicly available datasets that unify all these characteristics remain limited, and existing corpora often suffer from small sample sizes or fragmented formats. In this section, we explain how we mitigate these issues in both pre-training and task-specific training scenarios.

## 4.1 Pre-training datasets

In the domain of document understanding, currently available open-access document pre-training datasets include OCR-IDL [10], IIT-CDIP [44], CCpdf [83], and WordScape [88]. OCR-IDL and IIT-CDIP, industry-sourced from the 1990s, provide only page images and OCR text. CCpdf and WordScape, derived from Common Crawl, focus on raw PDFs and Word documents, respectively. While WordScape enriches documents with layout, table structure, and topic information via XML markup, its visual variability is limited. Notably, none of these datasets offer rich annotations for all page elements.

DocLayNet-PT. Given the lack of public multimodal document pre-training data with sufficient annotation features, we constructed a new document pre-training dataset named DocLayNet-PT. DocLayNet-PT is a 1.4M pages dataset extracted from the DocFM dataset [25] of unique PDF documents, sourced from CommonCrawl, Wikipedia, and business-related documents. We focused on this dataset to look for documents including visually distinct content such as equations, tables, code and charts with colorful layouts. This dataset was augmented with weak annotations through a series of processing steps. First, we applied PDF parsing to all pages to obtain the text layout including positional information using Docling [7, 53]. Next, we performed an enrichment step to obtain an enhanced, machine-friendly representation of each page. Specifically, we provide annotations for layout elements, table structure, language, topic, and figure classification on each page in DocTag format.

Docmatix. To preserve SmolVLM's original DocVQA capabilities, we adapted the same weak annotation strategy used for DocLayNet-PT and applied it to the 1.3M documents inside Docmatix dataset [41]. In addition to the 9.5 million DocVQA questions, we introduced another instruction requiring the full conversion of multi-page documents to DocTags.

## 4.2 Task-specific Datasets

Layout. To optimize prediction quality for document layout and table structure, we sampled 76K pages from DocLayNet-PT. Each page underwent human annotation and rigorous quality review, resulting in a high-quality ground-truth dataset. Of these, 60K pages were utilized for fine-tuning, constituting what we refer to as DocLayNet v2. Additionally, we extracted 63K pages from WordScape, specifically filtering for pages containing both text and tables, and utilized their inherently structure-preserving format as a reliable source of ground truth. To further enhance the model's adaptability to diverse layouts, colors, and fonts, we synthetically generated 250K pages using content from Wikipedia, creating the dataset known as SynthDocNet. This synthetic dataset enables the controlled generation of various document types with precise annotations. All datasets share a unified annotation format and class definitions, ensuring consistency and comprehensive document representation.

Tables. For the table structure recognition task we trained our model on public datasets such as PubTables-1M [78], FinTabNet [103], WikiTableSet [54] and tabular information extracted from WordScape documents. We transformed table structure information from the original ground-truth to OTSL format [55]. Then, we interleaved each cell tag with ground-truth text. This yields a condensed sequence which represents the structure of the table (including merged cells, and header declaration) along with its textual content.

Charts. For chart reconstruction, several datasets are publicly available [58, 33, 34, 93] that consist of both synthetic and web-scraped charts. However, these datasets are limited by either the quantity of real-world data or the visual diversity of synthetic examples. This has prompted recent studies [50, 26, 60, 94, 93, 13, 57] to crawl or render additional datasets, which unfortunately are rarely shared publicly. To address these gaps, we generated our own chart dataset comprising four types of charts: line, pie, bar, and stacked bar. Using data from 90,000 tables sourced from the FinTabNet dataset [103], we generated a comprehensive dataset containing 5,000 line charts, 380,000 pie charts, 380,000 bar charts, and 77,000 stacked bar charts. Each data point was rendered using three different visualization libraries (details provided in the Appendix), yielding a total of 2.5 million visually diverse charts.

Code. Technical books and scientific documents frequently include code snippets, making it essential for documentprocessing models to accurately interpret code structure, particularly indentation, which carries semantic significance in certain programming languages. Several publicly available datasets provide extensive collections of code snippets with permissive licenses in text format [38, 71], typically aggregated from sources like GitHub. However, a common limitation is that these datasets contain only text-based code snippets, lacking any image-based representation. To address this gap, we leveraged LaTeX and Pygments [16] to generate visually diverse renderings of code (details provided in the Appendix). Our dataset includes 9.3M million rendered code snippets generated at 120 dpi, covering 56 programming languages.

Equations. Accurately parsing mathematical formulas is crucial for document-processing models, as formulas often contain nuanced structures and semantics critical for scientific understanding. To effectively train our model for formula transcription, we combined publicly available datasets with a custom-rendered dataset. The public datasets [18, 22, 11, 23] collectively comprise approximately 730k unique formulas. In addition, we extracted 4.7 million formulas from arXiv by applying a regular expression to the source LaTeX code. The extracted formulas underwent a rigorous normalization procedure to ensure the model was trained only on correct and standardized LaTex code. Subsequently, these normalized equations were visually rendered using LaTeX (details provided in the Appendix). Overall, our dataset includes 5.5 million unique formulas, each rendered at a resolution of 120 dpi.

Table 1: Datasets. Overview of the datasets used for the training and evaluation of SmolDocling. Datasets we contribute as part of this publication are denoted with (*).

| Dataset Name                     | Size   | Type       |
|----------------------------------|--------|------------|
| TheCauldron [42]                 | 2.63M  |            |
| DocLayNet-PT-Instruct            | 1.62M  | Multi Task |
| DocMatix w/ Doc Conversion* [41] | 952K   |            |
| SynthChartNet*                   | 2.5M   |            |
| PlotQA [61]                      | 225K   |            |
| FigureQA [33]                    | 100K   | Chart      |
| Unichart [57]                    | 600K   |            |
| ChartQA [58]                     | 28K    |            |
| DocLayNet-PT                     | 1.40M  |            |
| Doc4MLlaVa - RVLCDIP             | 742K   |            |
| SynthDocNet                      | 375K   | Conversion |
| DocLayNet [70]                   | 81K    |            |
| DocLayNet v2                     | 76K    |            |
| WordScape-PT                     | 63K    |            |
| PubTable1M [78]                  | 1M     |            |
| WordScape Tables [88]            | 207K   | Table      |
| WikiTableSet (EN) [54]           | 204K   |            |
| FinTabNet [103]                  | 90K    |            |
| SynthFormulaNet*                 | 5.5M   | Formula    |
| SynthCodeNet*                    | 9.3M   | Code       |

## 4.3 Document Instruction Tuning datasets

To reinforce the recognition of different page elements and introduce document-related features and no-code pipelines, we leveraged rule-based techniques and the Granite-3.1-2b-instruct [25] LLM. Using samples from DocLayNet-PT pages, we generated one instruction by randomly sampling layout elements from a page. These instructions included tasks such as 'Perform OCR at bbox,' 'Identify page element type at bbox,' and 'Extract all section headers from the page.' In addition, we train with Cauldron [42] to avoid catastrophic forgetting with the amount of conversation datasets introduced.

| Method             | Size   |   Edit Distance ↓ |   F1-score ↑ |   Precision ↑ |   Recall ↑ |   BLEU ↑ |   METEOR ↑ |
|--------------------|--------|-------------------|--------------|---------------|------------|----------|------------|
| Full-page          |        |                   |              |               |            |          |            |
| Qwen2.5 VL [9]     | 7B     |              0.56 |         0.72 |          0.8  |       0.7  |     0.46 |       0.57 |
| GOT [89]           | 580M   |              0.61 |         0.69 |          0.71 |       0.73 |     0.48 |       0.59 |
| Nougat (base) [12] | 350M   |              0.62 |         0.66 |          0.72 |       0.67 |     0.44 |       0.54 |
| SmolDocling (Ours) | 256M   |              0.48 |         0.8  |          0.89 |       0.79 |     0.58 |       0.67 |
| Code listings      |        |                   |              |               |            |          |            |
| SmolDocling (Ours) | 256M   |              0.11 |         0.92 |          0.94 |       0.91 |     0.87 |       0.89 |
| Equations          |        |                   |              |               |            |          |            |
| Qwen2.5 VL [9]     | 7B     |              0.22 |         0.89 |          0.91 |       0.87 |     0.68 |       0.77 |
| GOT [89]           | 580M   |              0.11 |         0.95 |          0.95 |       0.96 |     0.85 |       0.91 |
| Nougat (base) [12] | 350M   |              0.62 |         0.6  |          0.6  |       0.53 |     0.33 |       0.41 |
| SmolDocling (Ours) | 256M   |              0.11 |         0.95 |          0.96 |       0.95 |     0.83 |       0.89 |

Table 2: Structured document text recognition. We evaluate OCR performance and document formatting on DocLayNet, focusing on textual elements, excluding tables. Text accuracy is measured through multiple metrics (Edit Distance, F1-Score, Precision, Recall, BLEU and METEOR) as proposed in [12].

## 5 Experiments

In this section, we assess the performance of SmolDocling on document understanding tasks, and compare the results to previously published models and baselines. Capturing the qualities and defects of SmolDocling and other models in comparable numbers is a non-trivial effort for reasons we outline further below. Therefore, we report results on standard evaluation tasks and metrics independently (see section 5.2), but also provide a qualitative assessment of output characteristics (see section 5.3).

## 5.1 Implementation Details

Our training was conducted using 64 NVIDIA A100 80GB GPUs, with each epoch requiring 38 hours. The model was trained for a total of 4 epochs using the AdamW optimizer with a learning rate of 2 × 10 -4 and 2 × 10 -6 for the vision encoder once unfrozen. We also employed gradient clipping to 1.0 for stability and a warmup ratio of 0.03. For inference, using VLLM [39] on an A100 GPU, SmolDocling achieves a page conversion time of 0.35 seconds while occupying only 0.489 GB of VRAM. The model supports a maximum sequence length of 8,192 tokens and is trained to process up to three pages at a time.

## 5.2 Quantitative Results

To enable valid cross-model comparisons with SmolDocling, we addressed multiple dataset and harmonization challenges:

- Public evaluation datasets lack comprehensive multi-task annotations with diverse layouts. We augmented DocLayNet [70] with text content and reading-order to enable evaluation of both layout analysis and text recognition. Table structure, code listing, formula and chart recognition are evaluated on specific datasets.
- Different models each follow their own conventions for output markup (e.g., Markdown, HTML, DocTags) and produce annotations which do not fully align in semantics. For example, SmolDocling is trained to produce 14 distinct layout-class tags via DocTags , while Qwen2.5-VL combines standard HTML tags and a few explicit class attributes with only partially compatible definitions. GOT and Nougat output Markdown, which can express paragraphs, lists and tables without location annotation.

SmolDocling: An ultra-compact vision-language model for end-to-end multi-modal document conversion

- Input resolution of page images can affect model performance, but is rarely specified in literature. We standardized at 144 DPI (dots per inch), which reproduces enough detail to be perfectly legible by humans while keeping resource demand for inference reasonable.

Text Recognition (OCR). We first evaluate text recognition accuracy of SmolDocling, compared to Nougat, GOT and Qwen2.5-VL (Table 2), to verify accurate character transcription and reading order. The text-similarity metrics, adopted from [12] require plain formatting without non-textual elements. For different tasks, we conduct evaluations on diverse content types: full-page documents (from DocLayNet test set) in Markdown format, code snippets (from our SynthCodeNet dataset) in plaintext, and equations (from Im2Latex-230k [21]) in LaTeX format. For Qwen2.5-VL and SmolDocling, we convert HTML or DocTags output to Markdown while preserving formatting where possible.

Tables are omitted from the full-page output since they do not compare well through Markdown, due to loss of row and column spans. Code snippets must be reproduced with intact line breaks and indentation for accurate scoring. Formulas are evaluated separately in LaTeX format, which is natively produced by all the models tested in this work. The LaTeX outputs from all models, including the ground truth, undergo the same normalization procedure used for generating the SynthFormulaNet dataset. This ensures consistency and fairness in model comparisons.

SmolDocling significantly outperforms GOT, Nougat and also Qwen2.5-VL, which is 27 times larger, on every metric in full-page transcription (see Table 2. For the code parsing task, we report results exclusively for SmolDocling, as we introduce this task for the first time, and the other models were not explicitly trained for it. SmolDocling also demonstrates superior performance compared to Nougat and Qwen2.5-VL in the formula recognition task, closely matching GOT's results.

Layout. We evaluate element localization accuracy using the DocLayNet test set (Table 3), measuring the ability to reconstruct document representations that support content grounding and visual feature extraction. SmolDocling is compared against Qwen2.5-VL-7b [9], currently the only other VLM addressing this task. For a fair comparison, we map each model's label outputs and the ground-truth to six compatible classes: Text, Section Heading, List Item, Table, Picture, and Formula. Overall, SmolDocling outperforms Qwen2.5-VL-7b by a significant margin, however both models score far below a human baseline. This can be partially explained through inherent difficulty of the dataset, as indicated by relatively low human agreement scores. The scores are primarily impacted by moderate recall of element bounding boxes and different degrees of label confusion in both SmolDocling and Qwen2.5-VL. A majority of samples show accurate element bounding boxes as seen in the Appendix.

|                |   SmolDocling |   Qwen2.5-VL |   Human |
|----------------|---------------|--------------|---------|
| Formula        |         0.267 |        0.059 |    0.84 |
| Table          |         0.266 |        0.262 |    0.79 |
| List Item      |         0.296 |        0.09  |    0.87 |
| Section Header |         0.289 |        0.129 |    0.83 |
| Text           |         0.204 |        0.18  |    0.85 |
| Picture        |         0.066 |        0.078 |    0.7  |
| Overall        |         0.231 |        0.133 |    0.82 |

Table 3: Layout analysis comparison. Evaluation of layout analysis task on DocLayNet with a subset of 6 class labels in mAP[0.5:0.95] metric. Additional reference points are provided for an estimate of human performance [70].

Table Structure Recognition. We evaluate the accuracy of recovering table structures, i.e. shaping columns and rows with the correct text content and spans, through the TEDS metric [104], once including table text content and once on structure only with ommitted text (see Table 4). For isolated assessment, we provide cropped table images from FinTabNet [103] and PubTables-1M [78] test sets, which have poor image quality (72 dpi, compression artifacts). SmolDocling performs competitively against significantly larger models, despite challenges with text transcription from the low-resolution image crops, a limitation likely attributable to insufficient training at such resolutions. Scores for structure only TEDS evaluation are therefore much stronger.

Chart Extraction. In this task, we evaluate the ability to convert cropped sections of charts into datapoint tables. All table ouptut is transformed into HTML and compared against the ground-truth HTML using TEDS score, as reported in Table 5. We attribute SmolDocling's performance on charts to the variability in ground-truth styles and the differing interpretations of representations across datasets, which we observe leads to inconsistencies in the model's predictions. Despite its significantly smaller size, SmolDocling achieves a competitive TEDs score.

Overall, SmolDocling achieves leading performance across a wide range of document conversion tasks and outperforms significantly larger models in text recognition, layout analysis and structure extraction. Furthermore, the model's

SmolDocling: An ultra-compact vision-language model for end-to-end multi-modal document conversion

| Method              | Model Size   | FinTabNet   | PubTables-1M   |
|---------------------|--------------|-------------|----------------|
| SmolVLM [56]        | 2.2B         | 0.18        | -              |
| Granite Vision [81] | 2.98B        | 0.54        | -              |
| TableFormer† [55]   | 52M          | 0.89        | 0.84           |
| SmolDocling (Ours)  | 256M         | 0.52 (0.81) | 0.65 (0.88)    |

Table 4: Table structure and cell content reconstruction. Numbers are given in TEDS metric including text content, with structure-only score in brackets where available. We produced results for SmolDocling and TableFormer, other results are reflected here as published. †TableFormer extracts the content through external OCR.

| Model               | Size   | TEDs ↑   |
|---------------------|--------|----------|
| Phi-3.5-vision      | 4B     | 0.40     |
| Granite Vision [81] | 3B     | 0 . 95   |
| SmolVLM [56]        | 2.2B   | 0.02     |
| Molmo-E             | 1B     | 0.54     |
| SmolDocling (Ours)  | 256M   | 0.75     |

Table 5: Chart analysis. We compare SmolDocling to other small-sized VLMs on the chart-to-table task using the TEDS metric. The results in this table are based on the work reported in [25].

high performance in downstream tasks, such as code listing and formula recognition, demonstrates its versatility and real-world applicability. Notably, we establish benchmark results for the code listing recognition, setting a standard for future evaluations.

## 5.3 Qualitative Results

Several characteristics of SmolDocling output are not evident from task-based metrics alone. In this section, we highlight different output characteristics observed in practice. More illustrated samples can be found in the Appendix.

As presented in Figure 3, SmolDocling outputs a sequence of DocTags to encode content, structure and layout. A few notable traits beyond straight content reproduction include: 1) Labeling of page headers and footers, which allows ignoring these repetitive elements in downstream applications. 2) Linking of information, such as captions to tables or pictures, or list elements to parent list through nesting of tags. 3) Preserved line breaks and indentation for code snippets, while line wraps in paragraphs are correctly removed.

Among typical defects found in the output of SmolDocling are missing tags (e.g. absent location tags), malformed structure (such as missing closing tags), and endless repetition of tokens from some point in a page. Both of these phenomena are commonly seen in auto-regressive models and may prevent parsing or rendering the output correctly (see Appendix). Despite these limitations, SmolDocling demonstrates unique robustness advantages compared to ensemble systems like Docling, since conversion output is inferred in a single pass. In the latter, errors may accumulate throughout the ensemble model pipeline. For example, a mislocated table detection will lead to missing or distorted output of table structure and content, since this depends on correct table localization. In contrast, SmolDocling can reproduce the tables correctly, even with incorrectly determined location.

## 6 Conclusion

In this work, we introduce SmolDocling, an efficient and compact VLM optimized for document conversion while providing rich output representation. We also present a suite of new datasets with a unified format for document conversion, including the novel task of code listing transcription. We identify page element localization as a critical area requiring further refinement, where targeted techniques will significantly enhance performance in future iterations. Our results conclusively demonstrate that smaller models with unified, optimized output formats such as DocTags can effectively compete with significantly larger models, establishing a clear pathway for resource-efficient multi-task document understanding models.

## References

- [1] Grobid. https://github.com/kermitt2/grobid , 2008-2025.
- [2] Loubna Ben Allal, Anton Lozhkov, Elie Bakouch, Gabriel Martín Blázquez, Guilherme Penedo, Lewis Tunstall, Andrés Marafioti, Hynek Kydlíček, Agustín Piqueres Lajarín, Vaibhav Srivastav, Joshua Lochner, Caleb Fahlgren, Xuan-Son Nguyen, Clémentine Fourrier, Ben Burtenshaw, Hugo Larcher, Haojun Zhao, Cyril Zakka, Mathieu Morlon, Colin Raffel, Leandro von Werra, and Thomas Wolf. SmolLM2: When Smol Goes Big - Data-Centric Training of a Small Language Model, 2025.
- [3] Amazon Web Services. Amazon Textract. https://aws.amazon.com/textract/ , 2024. Accessed: 2024-11-11.
- [4] Rohan Anil, Andrew M Dai, Orhan Firat, Melvin Johnson, Dmitry Lepikhin, Alexandre Passos, Siamak Shakeri, Emanuel Taropa, Paige Bailey, Zhifeng Chen, et al. Palm 2 technical report. arXiv preprint arXiv:2305.10403 , 2023.
- [5] Anthropic. Claude-3.5. https://www.anthropic.com/news/claude-3-5-sonnet , 2024. Accessed: 202402-11.
- [6] Christoph Auer, Michele Dolfi, André Carvalho, Cesar Berrospi Ramis, and Peter WJ Staar. Delivering Document Conversion as a Cloud Service with High Throughput and Responsiveness. In 2022 IEEE 15th International Conference on Cloud Computing (CLOUD) , pages 363-373. IEEE, 2022.
- [7] Christoph Auer, Maksym Lysak, Ahmed Nassar, Michele Dolfi, Nikolaos Livathinos, Panos Vagenas, Cesar Berrospi Ramis, Matteo Omenetti, Fabian Lindlbauer, Kasper Dinkla, et al. Docling Technical Report. arXiv preprint arXiv:2408.09869 , 2024.
- [8] Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, et al. Qwen technical report. arXiv preprint arXiv:2309.16609 , 2023.
- [9] Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang, Humen Zhong, Yuanzhi Zhu, Mingkun Yang, Zhaohai Li, Jianqiang Wan, Pengfei Wang, Wei Ding, Zheren Fu, Yiheng Xu, Jiabo Ye, Xi Zhang, Tianbao Xie, Zesen Cheng, Hang Zhang, Zhibo Yang, Haiyang Xu, and Junyang Lin. Qwen2.5-VL Technical Report, 2025.
- [10] Ali Furkan Biten, Rubèn Tito, Lluis Gomez, Ernest Valveny, and Dimosthenis Karatzas. Ocr-idl: Ocr annotations for industry document library dataset. In European Conference on Computer Vision , pages 241-252. Springer, 2022.
- [11] Lukas Blecher. LaTeX-OCR, 2024. Accessed: 2024-11-09.
- [12] Lukas Blecher, Guillem Cucurull, Thomas Scialom, and Robert Stojnic. Nougat: Neural optical understanding for academic documents. arXiv preprint arXiv:2308.13418 , 2023.
- [13] Jinyue Chen, Lingyu Kong, Haoran Wei, Chenglong Liu, Zheng Ge, Liang Zhao, Jianjian Sun, Chunrui Han, and Xiangyu Zhang. OneChart: Purify the Chart Structural Extraction via One Auxiliary Token. arXiv preprint arXiv:2404.09987 , 2024.
- [14] Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al. Scaling instruction-finetuned language models. Journal of Machine Learning Research , 25(70):1-53, 2024.
- [15] Pyecharts Contributors. pyecharts: Python library for Echarts, 2024. Accessed: 2024-11-06.
- [16] Pygments Contributors. Pygments: Python syntax highlighter, 2024. Accessed: 2024-11-06.
- [17] Brian Davis, Bryan Morse, Brian Price, Chris Tensmeyer, Curtis Wigington, and Vlad Morariu. End-to-end document recognition and understanding with dessurt. In European Conference on Computer Vision , pages 280-296. Springer, 2022.
- [18] Yuntian Deng, Anssi Kanervisto, Jeffrey Ling, and Alexander M Rush. Image-to-markup generation with coarse-to-fine attention. In International Conference on Machine Learning , pages 980-989. PMLR, 2017.
- [19] Xiaoyi Dong, Pan Zhang, Yuhang Zang, Yuhang Cao, Bin Wang, Linke Ouyang, Xilin Wei, Songyang Zhang, Haodong Duan, Maosong Cao, Wenwei Zhang, Yining Li, Hang Yan, Yang Gao, Xinyue Zhang, Wei Li, Jingwen Li, Kai Chen, Conghui He, Xingcheng Zhang, Yu Qiao, Dahua Lin, and Jiaqi Wang. InternLM-XComposer2: Mastering Free-form Text-Image Composition and Comprehension in Vision-Language Large Model. arXiv preprint arXiv:2401.16420 , 2024.
- [20] Xiaoyi Dong, Pan Zhang, Yuhang Zang, Yuhang Cao, Bin Wang, Linke Ouyang, Songyang Zhang, Haodong Duan, Wenwei Zhang, Yining Li, Hang Yan, Yang Gao, Zhe Chen, Xinyue Zhang, Wei Li, Jingwen Li, Wenhai Wang, Kai Chen, Conghui He, Xingcheng Zhang, Jifeng Dai, Yu Qiao, Dahua Lin, and Jiaqi Wang. InternLM-XComposer2-4KHD: A Pioneering Large Vision-Language Model Handling Resolutions from 336 Pixels to 4K HD. arXiv preprint arXiv:2404.06512 , 2024.
- [21] Gregory Eritsyan. Im2LaTeX 230K. https://www.kaggle.com/datasets/gregoryeritsyan/ im2latex-230k , 2023. Accessed: 2025-03-06.
- [22] Hugging Face. latex-formulas Dataset, 2024. Accessed: 2024-11-09.
- [23] Philippe Gervais, Asya Fadeeva, and Andrii Maksai. Mathwriting: A dataset for handwritten mathematical expression recognition. URL https://arxiv. org/abs/2404.10690 , 2024.
- [24] Google Cloud. Document AI. https://cloud.google.com/document-ai , 2024. Accessed: 2024-11-11.
- [25] IBM Granite Team. Granite 3.0 Language Models, 2024.

- [26] Yucheng Han, Chi Zhang, Xin Chen, Xu Yang, Zhibin Wang, Gang Yu, Bin Fu, and Hanwang Zhang. Chartllama: A multimodal llm for chart understanding and generation. arXiv preprint arXiv:2311.16483 , 2023.
- [27] Anwen Hu, Haiyang Xu, Jiabo Ye, Ming Yan, Liang Zhang, Bo Zhang, Chen Li, Ji Zhang, Qin Jin, Fei Huang, et al. mplug-docowl 1.5: Unified structure learning for ocr-free document understanding. arXiv preprint arXiv:2403.12895 , 2024.
- [28] Anwen Hu, Haiyang Xu, Liang Zhang, Jiabo Ye, Ming Yan, Ji Zhang, Qin Jin, Fei Huang, and Jingren Zhou. mplug-docowl2: High-resolution compressing for ocr-free multi-page document understanding. arXiv preprint arXiv:2409.03420 , 2024.
- [29] Yupan Huang, Tengchao Lv, Lei Cui, Yutong Lu, and Furu Wei. Layoutlmv3: Pre-training for document ai with unified text and image masking. In Proceedings of the 30th ACM International Conference on Multimedia , pages 4083-4091, 2022.
- [30] J. D. Hunter. Matplotlib: A 2D graphics environment. Computing in Science &amp; Engineering , 9(3):90-95, 2007.
- [31] Guillaume Jaume, Hazim Kemal Ekenel, and Jean-Philippe Thiran. Funsd: A dataset for form understanding in noisy scanned documents. In 2019 International Conference on Document Analysis and Recognition Workshops (ICDARW) , volume 2, pages 1-6. IEEE, 2019.
- [32] Albert Q Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, et al. Mixtral of experts. arXiv preprint arXiv:2401.04088 , 2024.
- [33] Samira Ebrahimi Kahou, Vincent Michalski, Adam Atkinson, Ákos Kádár, Adam Trischler, and Yoshua Bengio. Figureqa: An annotated figure dataset for visual reasoning. arXiv preprint arXiv:1710.07300 , 2017.
- [34] Shankar Kantharaj, Rixie Tiffany Ko Leong, Xiang Lin, Ahmed Masry, Megh Thakkar, Enamul Hoque, and Shafiq Joty. Chart-to-text: A large-scale benchmark for chart summarization. arXiv preprint arXiv:2203.06486 , 2022.
- [35] Geewook Kim, Teakgyu Hong, Moonbin Yim, Jinyoung Park, Jinyeong Yim, Wonseok Hwang, Sangdoo Yun, Dongyoon Han, and Seunghyun Park. Donut: Document understanding transformer without ocr. arXiv preprint arXiv:2111.15664 , 7(15):2, 2021.
- [36] Sunghwan Kim, Jie Chen, Tiejun Cheng, Asta Gindulyte, Jia He, Siqian He, Qingliang Li, Benjamin A Shoemaker, Paul A Thiessen, Bo Yu, Leonid Zaslavsky, Jian Zhang, and Evan E Bolton. PubChem 2019 update: improved access to chemical data. Nucleic Acids Research , 47(D1):D1102-D1109, 10 2018.
- [37] Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al. Segment anything. In Proceedings of the IEEE/CVF International Conference on Computer Vision , pages 4015-4026, 2023.
- [38] Denis Kocetkov, Raymond Li, Loubna Ben Allal, Jia Li, Chenghao Mou, Carlos Muñoz Ferrandis, Yacine Jernite, Margaret Mitchell, Sean Hughes, Thomas Wolf, Dzmitry Bahdanau, Leandro von Werra, and Harm de Vries. The Stack: 3 TB of permissively licensed source code. Preprint , 2022.
- [39] Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph E. Gonzalez, Hao Zhang, and Ion Stoica. Efficient Memory Management for Large Language Model Serving with PagedAttention. In Proceedings of the ACM SIGOPS 29th Symposium on Operating Systems Principles , 2023.
- [40] Greg Landrum. RDKit: Open-Source Cheminformatics Software. http://www.rdkit.org/. Accessed: 1 January 2023.
- [41] Hugo Laurençon, Andrés Marafioti, Victor Sanh, and Léo Tronchon. Building and better understanding vision-language models: insights and future directions., 2024.
- [42] Hugo Laurençon, Léo Tronchon, Matthieu Cord, and Victor Sanh. What matters when building vision-language models?, 2024.
- [43] Kenton Lee, Mandar Joshi, Iulia Raluca Turc, Hexiang Hu, Fangyu Liu, Julian Martin Eisenschlos, Urvashi Khandelwal, Peter Shaw, Ming-Wei Chang, and Kristina Toutanova. Pix2struct: Screenshot parsing as pretraining for visual language understanding. In International Conference on Machine Learning , pages 18893-18912. PMLR, 2023.
- [44] David Lewis, Gady Agam, Shlomo Argamon, Ophir Frieder, David Grossman, and Jefferson Heard. Building a test collection for complex document information processing. In Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval , pages 665-666, 2006.
- [45] Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Yanwei Li, Ziwei Liu, and Chunyuan Li. Llava-onevision: Easy visual task transfer. arXiv preprint arXiv:2408.03326 , 2024.
- [46] Feng Li, Renrui Zhang, Hao Zhang, Yuanhan Zhang, Bo Li, Wei Li, Zejun Ma, and Chunyuan Li. Llava-next-interleave: Tackling multi-image, video, and 3d in large multimodal models. arXiv preprint arXiv:2407.07895 , 2024.
- [47] Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. In International conference on machine learning , pages 19730-19742. PMLR, 2023.
- [48] Han Lin, Peng Yang, and Fanlong Zhang. Review of scene text detection and recognition. Archives of computational methods in engineering , 27(2):433-454, April 2020.
- [49] Fangyu Liu, Julian Martin Eisenschlos, Francesco Piccinno, Syrine Krichene, Chenxi Pang, Kenton Lee, Mandar Joshi, Wenhu Chen, Nigel Collier, and Yasemin Altun. Deplot: One-shot visual language reasoning by plot-to-table translation. arXiv preprint arXiv:2212.10505 , 2022.

- [50] Fangyu Liu, Francesco Piccinno, Syrine Krichene, Chenxi Pang, Kenton Lee, Mandar Joshi, Yasemin Altun, Nigel Collier, and Julian Martin Eisenschlos. Matcha: Enhancing visual language pretraining with math reasoning and chart derendering. arXiv preprint arXiv:2212.09662 , 2022.
- [51] Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee. Improved Baselines with Visual Instruction Tuning, 2023.
- [52] Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual Instruction Tuning. In NeurIPS , 2023.
- [53] Nikolaos Livathinos, Christoph Auer, Maksym Lysak, Ahmed Nassar, Michele Dolfi, Panos Vagenas, Cesar Berrospi Ramis, Matteo Omenetti, Kasper Dinkla, Yusik Kim, Shubham Gupta, Rafael Teixeira de Lima, Valery Weber, Lucas Morin, Ingmar Meijer, Viktor Kuropiatnyk, and Peter W. J. Staar. Docling: An Efficient Open-Source Toolkit for AI-driven Document Conversion, 2025.
- [54] Nam Ly., Atsuhiro Takasu., Phuc Nguyen., and Hideaki Takeda. Rethinking Image-Based Table Recognition Using Weakly Supervised Methods. pages 872-880, 2023.
- [55] Maksym Lysak, Ahmed Nassar, Nikolaos Livathinos, Christoph Auer, and Peter Staar. Optimized Table Tokenization for Table Structure Recognition. In Gernot A. Fink, Rajiv Jain, Koichi Kise, and Richard Zanibbi, editors, Document Analysis and Recognition - ICDAR 2023 , pages 37-50, Cham, 2023. Springer Nature Switzerland.
- [56] Andrés Marafioti, Orr Zohar, Miquel Farré, Merve Noyan, Elie Bakouch, Pedro Cuenca, Cyril Zakka, Loubna Ben Allal, Anton Lozhkov, Nouamane Tazi, Vaibhav Srivastav, Joshua Lochner, Hugo Larcher, Mathieu Morlon, Lewis Tunstall, Leandro von Werra, and Thomas Wolf. SmolVLM: Redefining small and efficient multimodal models. 2025.
- [57] Ahmed Masry, Parsa Kavehzadeh, Xuan Long Do, Enamul Hoque, and Shafiq Joty. Unichart: A universal vision-language pretrained model for chart comprehension and reasoning. arXiv preprint arXiv:2305.14761 , 2023.
- [58] Ahmed Masry, Do Xuan Long, Jia Qing Tan, Shafiq Joty, and Enamul Hoque. Chartqa: A benchmark for question answering about charts with visual and logical reasoning. arXiv preprint arXiv:2203.10244 , 2022.
- [59] Minesh Mathew, Dimosthenis Karatzas, and CV Jawahar. Docvqa: A dataset for vqa on document images. In Proceedings of the IEEE/CVF winter conference on applications of computer vision , pages 2200-2209, 2021.
- [60] Fanqing Meng, Wenqi Shao, Quanfeng Lu, Peng Gao, Kaipeng Zhang, Yu Qiao, and Ping Luo. Chartassisstant: A universal chart multimodal language model via chart-to-table pre-training and multitask instruction tuning. arXiv preprint arXiv:2401.02384 , 2024.
- [61] Nitesh Methani, Pritha Ganguly, Mitesh M. Khapra, and Pratyush Kumar. PlotQA: Reasoning over Scientific Plots. In The IEEE Winter Conference on Applications of Computer Vision (WACV) , March 2020.
- [62] Microsoft Azure. AI Document Intelligence. https://azure.microsoft.com/en-us/products/ ai-services/ai-document-intelligence , 2024. Accessed: 2024-11-11.
- [63] Lucas Morin, Martin Danelljan, Maria Isabel Agea, Ahmed Nassar, Valery Weber, Ingmar Meijer, Peter Staar, and Fisher Yu. MolGrapher: Graph-based Visual Recognition of Chemical Structures. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , pages 19552-19561, October 2023.
- [64] Lucas Morin, Valéry Weber, Gerhard Ingmar Meijer, Fisher Yu, and Peter W. J. Staar. PatCID: an open-access dataset of chemical structures in patent documents. Nature Communications , 15(1):6532, Aug 2024.
- [65] OpenAI. Hello GPT-4O. https://openai.com/index/hello-gpt-4o/ , 2024. Accessed: 2024-02-09, 2024-0211, 2024-02-12.
- [66] R OpenAI. Gpt-4 technical report. arxiv 2303.08774. View in Article , 2(5), 2023.
- [67] Vik Paruchuri. Marker: Convert PDF to Markdown Quickly with High Accuracy. https://github.com/VikParuchuri/marker, 2024.
- [68] Vik Paruchuri. Texify. https://github.com/VikParuchuri/texify , 2024. Accessed: 2024-11-11.
- [69] Zhiliang Peng, Wenhui Wang, Li Dong, Yaru Hao, Shaohan Huang, Shuming Ma, and Furu Wei. Kosmos-2: Grounding multimodal large language models to the world. arXiv preprint arXiv:2306.14824 , 2023.
- [70] Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. DocLayNet: A Large Human-Annotated Dataset for Document-Layout Segmentation. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , KDD '22, page 3743-3751, New York, NY, USA, 2022. Association for Computing Machinery.
- [71] Ruchir Puri, David Kung, Geert Janssen, Wei Zhang, Giacomo Domeniconi, Vladmir Zolotov, Julian Dolby, Jie Chen, Mihir Choudhury, Lindsey Decker, Veronika Thost, Luca Buratti, Saurabh Pujar, Shyam Ramji, Ulrich Finkler, Susan Malaika, and Frederick Reiss. CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks, 2021.
- [72] Edward O. Pyzer-Knapp, Matteo Manica, Peter Staar, Lucas Morin, Patrick Ruch, Teodoro Laino, John R. Smith, and Alessandro Curioni. Foundation models for materials discovery - current state and future directions. npj Computational Materials , 11(1):61, Mar 2025.
- [73] Yujie Qian, Jiang Guo, Zhengkai Tu, Zhening Li, Connor W. Coley, and Regina Barzilay. MolScribe: Robust Molecular Structure Recognition with Image-to-Graph Generation. Journal of Chemical Information and Modeling , 63(7):1925-1934, Apr 2023.

- [74] Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of machine learning research , 21(140):1-67, 2020.
- [75] Kohulan Rajan, Henning Otto Brinkhaus, M. Isabel Agea, Achim Zielesny, and Christoph Steinbeck. DECIMER.ai: an open platform for automated optical chemical structure identification, segmentation and recognition in scientific publications. Nature Communications , 14(1):5045, Aug 2023.
- [76] Johannes Rausch, Octavio Martinez, Fabian Bissig, Ce Zhang, and Stefan Feuerriegel. Docparser: Hierarchical document structure parsing from renderings. In Proceedings of the AAAI Conference on Artificial Intelligence , volume 35, pages 4328-4338, 2021.
- [77] Felix M Schmitt-Koopmann, Elaine M Huang, Hans-Peter Hutter, Thilo Stadelmann, and Alireza Darvishy. MathNet: A Data-Centric Approach for Printed Mathematical Expression Recognition. IEEE Access , 2024.
- [78] Brandon Smock, Rohith Pesala, and Robin Abraham. PubTables-1M: Towards comprehensive table extraction from unstructured documents. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages 4624-4632, 2022.
- [79] Zineng Tang, Ziyi Yang, Guoxin Wang, Yuwei Fang, Yang Liu, Chenguang Zhu, Michael Zeng, Cha Zhang, and Mohit Bansal. Unifying vision, text, and layout for universal document processing. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 19254-19264, 2023.
- [80] Gemini Team, Rohan Anil, Sebastian Borgeaud, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, Katie Millican, et al. Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312.11805 , 2023.
- [81] Granite Vision Team, Leonid Karlinsky, Assaf Arbelle, Abraham Daniels, Ahmed Nassar, Amit Alfassi, Bo Wu, Eli Schwartz, Dhiraj Joshi, Jovana Kondic, Nimrod Shabtay, Pengyuan Li, Roei Herzig, Shafiq Abedin, Shaked Perek, Sivan Harary, Udi Barzelay, Adi Raz Goldfarb, Aude Oliva, Ben Wieles, Bishwaranjan Bhattacharjee, Brandon Huang, Christoph Auer, Dan Gutfreund, David Beymer, David Wood, Hilde Kuehne, Jacob Hansen, Joseph Shtok, Ken Wong, Luis Angel Bathen, Mayank Mishra, Maksym Lysak, Michele Dolfi, Mikhail Yurochkin, Nikolaos Livathinos, Nimrod Harel, Ophir Azulai, Oshri Naparstek, Rafael Teixeira de Lima, Rameswar Panda, Sivan Doveh, Shubham Gupta, Subhro Das, Syed Zawad, Yusik Kim, Zexue He, Alexander Brooks, Gabe Goodhart, Anita Govindjee, Derek Leist, Ibrahim Ibrahim, Aya Soffer, David Cox, Kate Soule, Luis Lastras, Nirmit Desai, Shila Ofek-koifman, Sriram Raghavan, Tanveer Syeda-Mahmood, Peter Staar, Tal Drory, and Rogerio Feris. Granite Vision: a lightweight, open-source multimodal model for enterprise Intelligence, 2025.
- [82] Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 , 2023.
- [83] Michał Turski, Tomasz Stanisławek, Karol Kaczmarek, Paweł Dyda, and Filip Graliński. CCpdf: Building a High Quality Corpus for Visually Rich Documents from Web Crawl Data. In Gernot A. Fink, Rajiv Jain, Koichi Kise, and Richard Zanibbi, editors, Document Analysis and Recognition - ICDAR 2023 , pages 348-365, Cham, 2023. Springer Nature Switzerland.
- [84] Unstructured.io Team. Unstructured.io: Open-Source Pre-Processing Tools for Unstructured Data. https://unstructured.io, 2024. Accessed: 2024-11-19.
- [85] Vicuna. Vicuna: An open-source chatbot impressing GPT-4 with 90%* ChatGPT quality. https://vicuna.lmsys. org/ , 2023.
- [86] Bin Wang, Chao Xu, Xiaomeng Zhao, Linke Ouyang, Fan Wu, Zhiyuan Zhao, Rui Xu, Kaiwen Liu, Yuan Qu, Fukai Shang, Bo Zhang, Liqun Wei, Zhihao Sui, Wei Li, Botian Shi, Yu Qiao, Dahua Lin, and Conghui He. MinerU: An Open-Source Solution for Precise Document Content Extraction, 2024.
- [87] Michael L Waskom. Seaborn: statistical data visualization. Journal of Open Source Software , 6(60):3021, 2021.
- [88] Maurice Weber, Carlo Siebenschuh, Rory Butler, Anton Alexandrov, Valdemar Thanner, Georgios Tsolakis, Haris Jabbar, Ian Foster, Bo Li, Rick Stevens, et al. WordScape: a Pipeline to extract multilingual, visually rich Documents with Layout Annotations from Web Crawl Data. Advances in Neural Information Processing Systems , 36, 2024.
- [89] Haoran Wei, Chenglong Liu, Jinyue Chen, Jia Wang, Lingyu Kong, Yanming Xu, Zheng Ge, Liang Zhao, Jianjian Sun, Yuang Peng, et al. General OCR Theory: Towards OCR-2.0 via a Unified End-to-end Model. arXiv preprint arXiv:2409.01704 , 2024.
- [90] Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems , 35:24824-24837, 2022.
- [91] David Weininger. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. Journal of Chemical Information and Computer Sciences , 28(1):31-36, Feb 1988.
- [92] Wes McKinney and the Pandas Development Team. Pandas: powerful Python data analysis toolkit, Release 1.4.4. https: //pandas.pydata.org/pandas-docs/version/1.4/pandas.pdf , 2022.
- [93] Renqiu Xia, Bo Zhang, Haoyang Peng, Hancheng Ye, Xiangchao Yan, Peng Ye, Botian Shi, Junchi Yan, and Yu Qiao. StructChart: Perception, Structuring, Reasoning for Visual Chart Understanding. arXiv preprint arXiv:2309.11268 , 2023.

- [94] Renqiu Xia, Bo Zhang, Hancheng Ye, Xiangchao Yan, Qi Liu, Hongbin Zhou, Zijun Chen, Min Dou, Botian Shi, Junchi Yan, et al. Chartx &amp; chartvlm: A versatile benchmark and foundation model for complicated chart reasoning. arXiv preprint arXiv:2402.12185 , 2024.
- [95] Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, et al. Layoutlmv2: Multi-modal pre-training for visually-rich document understanding. arXiv preprint arXiv:2012.14740 , 2020.
- [96] Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, and Ming Zhou. Layoutlm: Pre-training of text and layout for document image understanding. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery &amp; data mining , pages 1192-1200, 2020.
- [97] Zuoyu Yan, Xiaode Zhang, Liangcai Gao, Ke Yuan, and Zhi Tang. ConvMath: a convolutional sequence network for mathematical expression recognition. In 2020 25th International Conference on Pattern Recognition (ICPR) , pages 4566-4572. IEEE, 2021.
- [98] Jiabo Ye, Anwen Hu, Haiyang Xu, Qinghao Ye, Ming Yan, Guohai Xu, Chenliang Li, Junfeng Tian, Qi Qian, Ji Zhang, et al. Ureader: Universal ocr-free visually-situated language understanding with multimodal large language model. arXiv preprint arXiv:2310.05126 , 2023.
- [99] Pan Zhang, Xiaoyi Dong, Bin Wang, Yuhang Cao, Chao Xu, Linke Ouyang, Zhiyuan Zhao, Shuangrui Ding, Songyang Zhang, Haodong Duan, Wenwei Zhang, Hang Yan, Xinyue Zhang, Wei Li, Jingwen Li, Kai Chen, Conghui He, Xingcheng Zhang, Yu Qiao, Dahua Lin, and Jiaqi Wang. InternLM-XComposer: A Vision-Language Large Model for Advanced Text-image Comprehension and Composition. arXiv preprint arXiv:2309.15112 , 2023.
- [100] Pan Zhang, Xiaoyi Dong, Yuhang Zang, Yuhang Cao, Rui Qian, Lin Chen, Qipeng Guo, Haodong Duan, Bin Wang, Linke Ouyang, Songyang Zhang, Wenwei Zhang, Yining Li, Yang Gao, Peng Sun, Xinyue Zhang, Wei Li, Jingwen Li, Wenhai Wang, Hang Yan, Conghui He, Xingcheng Zhang, Kai Chen, Jifeng Dai, Yu Qiao, Dahua Lin, and Jiaqi Wang. InternLMXComposer-2.5: A Versatile Large Vision Language Model Supporting Long-Contextual Input and Output. arXiv preprint arXiv:2407.03320 , 2024.
- [101] Qiming Zhang, Jing Zhang, Yufei Xu, and Dacheng Tao. Vision Transformer with Quadrangle Attention. arXiv preprint arXiv:2303.15105 , 2023.
- [102] Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, et al. Opt: Open pre-trained transformer language models. URL https://arxiv. org/abs/2205.01068 , 3:19-0, 2023.
- [103] Xinyi Zheng, Douglas Burdick, Lucian Popa, Xu Zhong, and Nancy Xin Ru Wang. Global table extractor (gte): A framework for joint table identification and cell structure recognition using visual context. In Proceedings of the IEEE/CVF winter conference on applications of computer vision , pages 697-706, 2021.
- [104] Xu Zhong, Elaheh ShafieiBavani, and Antonio Jimeno Yepes. Image-based table recognition: data, model, and evaluation. arXiv preprint arXiv:1911.10683 , 2019.
- [105] Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny. Minigpt-4: Enhancing vision-language understanding with advanced large language models. arXiv preprint arXiv:2304.10592 , 2023.

Multi Task

PubTablelM

SynthFormulaNet

Figure 3: Training datasets. A treemap visualization of dataset sizes contributing to the training of SmolDocling, categorized by type. Each rectangle represents a dataset, with its area proportional to the total dataset size. Each color indicates a dataset type. 'Multi-Task" refers to datasets that include multiple different tasks.

<!-- image -->

## A Additional Datasets Details

## A.1 SynthChartNet

SynthChartNet includes four distinct types of charts: line, pie, bar, and stacked bar, generated using three visualization libraries: Matplotlib [30], Seaborn [87], and Pyecharts [15]. Each data set was rendered through all three libraries, producing a final dataset of 2.5 million visually diverse charts. The rendering process was designed to leverage the full range of features available in these libraries, ensuring visual variety while maintaining the plausibility of the data presented. Figure 4 depicts 10 samples from SynthChartNet.

## A.2 SynthCodeNet

To render the code snippets of SynthCodeNet, we employed two rendering libraries: LaTeX and Pygments [16]. LaTex, in conjunction with its listings package, enabled the generation of code renderings similar to those commonly seen in academic and technical publications, while Pygments facilitated renderings with aesthetics resembling those of IDEs. The rendering process utilized the complete set of features provided by these libraries to enhance visual diversity while preserving the plausibility of the presented data. This included randomizing various stylistic parameters such as font, font size, style, weight, line spacing, tab size, background color, line numbering (including randomizing the starting line number), and programming language-specific syntax highlighting. Figure 5 shows 4 samples from SynthCodeNet.

## A.3 SynthFormulaNet

The extracted formulas of SynthFormulaNet underwent a rigorous normalization procedure to ensure the model was trained only on correct and standardized code. The normalization process consisted of the following major steps:

1. Token Filtering and Removal : Unnecessary or redundant LaTeX tokens were removed or replaced based on predefined policies. This included:
- Eliminating unwanted tokens.
- Replacing tokens with equivalent ones for consistency.

ChartQA

MolGrapher Synthetic 300k WordScape-PocLayNe[nichart

PlotQA

SynthDocNet

FinTabNet

600

500 -

400-

300 -

200

100-

1700

borond suel

1800

1900

Property plant and equipment -

Iccumulated depre..

Cross asset volur

Construction in progress in millions except per share

Total revenues

Income (loss) from continuing

Income from continuing

Net income (loss)

Total assets

Short-term debt

Total debt (including capital

Total stockholders' equity

2000

Enteray Texal

-** nteray New or eani

Enteray Hississippl

Interay Art.

2100

2200

-Ind

2300

12.31

band improvements

Duldings and building

- machinery and equipmen

13.85

16.92

Total Cash Dividends Paid

Toral Cash Widends Paid der Common Share

2011

2012

2013

2014

Total unrecognized

2500 5000 7500 10000 12500 15000 17500 20000

<!-- image -->

1Year

United States loYear

· 5Year|

<!-- image -->

O..................... . ... . . ... ..... ........ .... ·

2075

2100

2125

2150

2175

2200

2225

<!-- image -->

First econd

Third

<!-- image -->

7.69

Figure 4: Samples from SynthChartNet.

<!-- image -->

<!-- image -->

<!-- image -->

<!-- image -->

<!-- image -->

<!-- image -->

24.7%

2250

25.3%

· 3Year

24.4%

25.7%

16..

20.I

#pragma once

#include &lt;sdm/world/solvable\_by\_dp. hpp&gt;

#include &lt;sdm/utils/value\_function/initializer.hpp&gt;

namespace sdm namespace

{

class

{

registry protected:

typedef std:: map&lt;std:: string, std:: shared

\_ptr&lt;

world, Config config) &gt; map\_type;

Initializer &gt; (*) (std:: shared\_ptr&lt;SolvableByDP&gt;

static map\_type container;

initializer sane: Stage the app

A20RE, NEBAT NAVE: Y0218431-T5E-APP...

................

1t: contains (gathub. event.pull\_request labela.*.name, 'stage*)

steps:

- uses: actions/checkout (vl

- name: pm install and build webpack run:

npm instal.

- uses: actions/upload-artifactênain apm run build

with:

path: public/

name: webpack artifacts

Busld-Docker-Image:

runs-on: ubuntu-latest

```
public: static std:: vector<std:: string > available (): static std:: shared_ptr<Initializer > make(std :: string name, std:: shared_ptr<SolvableByDP> world, Config config = {}); } :1 template <class TInitializer > std:: shared_ptr<Initializer > createlnstance (std:: shared_ptr<SolvableByDP> world, Config config) return std:: make_shared<TInitializer >(world, config): package br.com.biot. integra caopagarmeapi.modulos.jwt.utids import br.com.biot. integracaopagarmeapi.config.exception.ValidacaoExceptipn import io.jsonwebtoken.Claims import lombok.extern.slf4j.Slf4j public class JwtCampoUtil{ public static String getCampold(Claims claims) {| tryi return (String) claims.get("id"); loge to tro do tentar recuperar I do JWT: 0'; claims toString): throw new ValidacaoException("Erro ao tentar recuperar o campo ID do Token."): public static String getCampo(String campo, Claims claims) { return (String) claims.get(campo); sagetro euro de entar recuperar o campo f] do IT: 0', campo, claims toString): throw new ValidacaoException( String.format("Erro ao tentar recuperar o campo %s do Token.", campo) 29| needs: build name: Build image and store in GitHub Packages steps: - name: Checkout uses: actions/checkout iva - name: Download built artifact uses: actions/download-arts factinain path: public - name: create inage and store in Packages uses: nattdavsa0351/actions/docker-gpr01.3.0 with: repo-token: 3{ (secrets.GITHUB,TOKEN) ) runneone wountuel tent needs: Build-Docker-Image name: Deploy app container to Azure steps: - name: "Login via Azure CLI" MIRA creda: 91{ SeCretA.AZURE_CREDENTIALS 1) - uses: azure/docker-logan@va with: logan-server: #{(env. IMAGE_REGISTRY_URL)) def main (filename, days) : · array I of buckets 0-8, each representing with that counter value fish_by_counter = [O for i in range (9)] for counter in read_counters (filename): fish_by_counter [counter) += 1 the number of fish for i in range (days): fish_by_counter · spawn (fish_by_counter) print (f"After {days) days: {sum(fish_by_counter)} fish") def spawn (fish_by_counter): # rotate left res = fish_by_counter (1:) + fish_by_counter [:1] # fish that were in bucket O are now new fish in bucket 8. restore the original fish into bucket 6. res [6] += res [8] return res def read_counters (filename) : with open (filename) as £ : return [int (counter) split (',')] for counter in f. readline ().strip(). -_name.. == '__main_.': main('input.txt', 80) main ('input.txt', 256)
```

Figure 5: Samples from SynthCodeNet.

kejnKm kejnKm

kejnKm

=I+II + II.

<!-- formula-not-decoded -->

d ≤ 1

Y

4.30

4.31

4.32

<!-- formula-not-decoded -->

(x)

<!-- formula-not-decoded -->

Figure 6: Samples from SynthFormulaNet.

SmolDocling: An ultra-compact vision-language model for end-to-end multi-modal document conversion

2. Structural Normalization : The equation structure was standardized by:
- Normalizing spacing between tokens.
- Ensuring consistent use of tokens that require one or two braces.
- Standardizing left and right delimiters.
3. Token Simplification : Redundant tokens and patterns were simplified, including:
- Collapsing consecutive redundant tokens (e.g., repeated primes, dots, or excessive spacing).
- Converting special constructs such as \Big into \left and \right for consistency.

The extracted equations were subsequently rendered using LaTex at 120 dpi. Several aspects of the rendering procedure have been randomized to ensure the visual diversity of the dataset. Each formula was rendered using a randomly selected font from a set of 85 possible options, with variations in font size, style, weight, and line spacing. Additionally, with a certain probability, each formula was enclosed within an "align" environment, allowing for the inclusion of a randomly generated equation number on either the left or right side of the equation. The equation number itself was chosen at random from a diverse set of possibilities, including small and large numbers, integers, floating-point values, or letters. Furthermore, it could be enclosed in single or double parentheses, brackets, or curly braces and was sometimes preceded by text labels such as 'Eqn.", 'Eqn", 'Eq.", or 'Equation". Figure 6 depicts 6 samples from SynthFormulaNet.

## A.4 DocTags vocabulary

Here we provide a reference to a complete vocabulary of DocTags for SmolDocling , and their meaning.

We represent tags in an XML-like style of notation. Some tags have an opening and closing part, and wrap around textual content (or other tags) for example: &lt;text&gt;hello world&lt;/text&gt; . Other tags are standalone and do not have a closing tag; they mark a special instruction, for example: &lt;page\_break&gt;

The complete DocTags snippet can represent a page or multiple pages, wrapped into &lt;doctag&gt;...&lt;/doctag&gt; . When content represents multiple pages, we use &lt;page\_break&gt; tag as a separator.

Within the scope of &lt;doctag&gt; we place high-level, tags that wrap around the textual content of certain document blocks and identify the type of the block. These tags are: &lt;text&gt; , &lt;caption&gt; , &lt;footnote&gt; , &lt;formula&gt; , &lt;title&gt; , &lt;page\_footer&gt; , &lt;page\_header&gt; , &lt;picture&gt; , &lt;section\_header&gt; , &lt;document\_index&gt; , &lt;code&gt; , &lt;otsl&gt; , &lt;list\_item&gt; , &lt;ordered\_list&gt; , &lt;unordered\_list&gt;

Each element can nest additional standalone location tags that encode its position on the page as a bounding box, represented in DocTags as &lt;loc\_ x1 &gt;&lt;loc\_ y1 &gt;&lt;loc\_ x2 &gt;&lt;loc\_ y2 &gt; . Every x,y pair of coordinates belongs to a fixed grid. In our case we used integer values in the range from 0 to 500 which proportionally maps to width and height of the page.

&lt;otsl&gt; element contains table representation with following OTSL tags, that are standalone and used to describe structure of a table: &lt;fcel&gt; , &lt;ecel&gt; , &lt;lcel&gt; , &lt;ucel&gt; , &lt;xcel&gt; , &lt;nl&gt; . These tags follow rules as described in [55], we additionally differentiate OTSL notion of a cell into &lt;fcel&gt; - full cell, or cell which contain content, and &lt;ecel&gt; - empty cell. We also augment tabular structure information with extra tags &lt;ched&gt; , &lt;rhed&gt; , &lt;srow&gt; that replace &lt;fcel&gt; to describe cells which belong to column and row headers of the table, as well as table section accordingly. We mark such headers when ground truth about table headers from the underlying dataset is available.

&lt;list\_item&gt; elements are placed within &lt;ordered\_list&gt; or &lt;unordered\_list&gt; and define weather it's enumerated (ordered) list or not.

&lt;picture&gt; and &lt;otsl&gt; elements by themselves can encapsulate &lt;caption&gt; tag if appropriate picture or table has it's own caption. This way we can connect extra descriptive information to illustrations and tables in the document.

&lt;code&gt; elements contain pieces of code as a content, and as such, respect tabulation and line breaks. Code elements also contains special standalone classification tag -&lt;\_ programming-language \_&gt; where programminglanguage is an appropriate programming language name. Supported values (57 in total) for programming language names are: Ada , Awk , Bash , bc , C , C# , C++ , CMake , COBOL , CSS , Ceylon , Clojure , Crystal , Cuda , Cython , D , Dart , dc , Dockerfile , Elixir , Erlang , FORTRAN , Forth , Go , HTML , Haskell , Haxe , Java , JavaScript , Julia , Kotlin , Lisp , Lua , Matlab , MoonScript , Nim , OCaml , ObjectiveC , Octave , PHP , Pascal , Perl , Prolog , Python , Racket , Ruby , Rust , SML , SQL , Scala , Scheme , Swift , TypeScript , unknown , VisualBasic , XML , YAML

Within &lt;picture&gt; elements we include picture classification with extra standalone tags &lt; image\_class&gt; . We classify images from our datasets into following categories: natural\_image , pie\_chart , bar\_chart , line\_chart ,

flow\_chart , scatter\_chart , heatmap , remote\_sensing , chemistry\_molecular\_structure , chemistry\_markush\_structure , icon , logo , signature , stamp , qr\_code , bar\_code , screenshot , map , stratigraphic\_chart , cad\_drawing , electrical\_diagram

## B Additional Details about the SmolDocling

Table 6: Supported Conversion Instructions.

| Instruction                 | Description                                                               |
|-----------------------------|---------------------------------------------------------------------------|
| Full conversion             | Convert this page to docling.                                             |
| Chart                       | Convert chart to table (e.g., <chart> ).                                  |
| Formula                     | Convert formula to LaTeX (e.g., <formula> ).                              |
| Code                        | Convert code to text (e.g., <code> ).                                     |
| Table                       | Convert table to OTSL (e.g., <otsl> ).                                    |
| No-Code Ac- tions/Pipelines | OCR the text in a specific location: <loc_155><loc_233><loc_206><loc_237> |
| No-Code Ac- tions/Pipelines | Identify element at: <loc_247><loc_482><loc_252><loc_486>                 |
| No-Code Ac- tions/Pipelines | Find all 'text' elements on the page, retrieve all section headers.       |
| No-Code Ac- tions/Pipelines | Detect footer elements on the page.                                       |

Input Image

OH

Cl

· 7: Qualitative comparison for molecule image recognition. Predictions are shown for GOT-OCR

2.5-VL-72B and SmolDocling on a simple molecule image.

GOT-OCR 2.0

NH,

Figure 7: Qualitative comparison for molecule image recognition. Predictions are shown for GOT-OCR 2.0, Qwen2.5-VL-72B and SmolDocling on a simple molecule image.

<!-- image -->

## C Additional Qualitative Analysis

## C.1 Molecule Image Recognition Experiment

Extracting molecule images from documents has potential to accelerate research in chemistry and materials discovery [72]. Some recent document understanding models, such as GOT-OCR 2.0 [89] and Qwen2.5-VL [9], claim to perform molecule image recognition in documents. Following this direction, we experimented with training SmolDocling for molecule image recognition on full document pages and cropped images. In order to predict molecule structures (graphs) with SmolDocling, we use the molecule string identifier named SMILES [91].

Datasets. For training on cropped images, we use the MolGrapher-Synthetic-300K dataset [63]. The dataset contains 300K synthetic samples. It is created using real chemical-structures retrieved from the database PubChem [36] which are then synthetically drawn using the library RDKit [40]. For training of full pages, we use Doc2Mol-80K, a subset of the PatCID dataset [64]. Our subset contains 80 000 real patent documents published in the United-States between 2022 and 2024.

Evaluation. Figure 7 shows an example of predicted molecules for a simple molecule image. While some models capture high-level molecular features, they consistently fail to reconstruct its detailed structure. Notably, this molecule is trivial for specialized models such as MolGrapher [63], MolScribe [73] or DECIMER [75], which accurately recognize its structure. Reducing the gap between general document understanding models and specialized models may require incorporating a specialized vocabulary for SMILES tokens, explicitly encoding atoms and bonds, and extending the SMILES sequence with localization tokens for each atom.

## C.2 Layout analysis samples

To illustrate and compare the visual grounding capabilities of SmolDocling, a set of sample predictions with SmolDocling and Qwen2.5-VL from DocLayNet [70] is shown in Table 7. Note that the element location results are independent from the correct reproduction of document content and structure.

Ground-Truth

46"

peR, heath and safety

Final gonn so yoR and convertible loan

tor save did exit) in the Opoon promp pur puse the Enter key:

trollowing display appears.

apposoices, tress later, yange Sample form nurter 3 in the Tet "description' prompt.

Hollowing display appears

Tyle doices, tress toler

Create earlay..

Moneyle Actior: Press the Enter key.

310.000,000 pal during the 2015

Sign untain Change in matters suusequent to

the end of the financial year

40

SmolDocling

2wen2.5-VL

Table 7: Visualizations of layout output from SmolDocling and QwenVL2.5 compared to the DocLayNet ground truth. Examples are chosen to be representative of different layout styles and features. The prediction results however do not represent a generalizable measure of the model's performance on inputs with similar features. (1) Multi-column pages are handled by SmolDocling and Qwen2.5-VL, with some recall errors in the latter. (2) A manual page with terminal output shows poor bounding box recall on SmolDocling and label confusion in Qwen2.5-VL. (3) Lists with nesting are handled well in SmolDocling but confuse Qwen2.5-VL. (4) Both SmolDocling and Qwen2.5-VL reconstruct equations well, however with different annotation conventions on including or excluding the equation index. (5) On a portrait page with colorful elements and gradient background, SmolDocling creates less accurate bounding-boxes and Qwen2.5-VL suffers low recall. (6) On a report page with tables and diagrams, SmolDocling output exhibits some repetition loop, fabricating non-existent text cells (bottom left), while Qwen2.5-VL is confused between tables and pictures. Win following, display appears we want yourse

Mum: F4 To 11S

<!-- image -->

Ground-Truth

301 Workers engarod in cential oe emeriency works pass ntial or emergency works" includes-

gunmpletions, maintenance, repairs, and asset replacement activities.

Quality

*/ Supie (England) Regularions 20164), the Water Supply Nu Qual

Low crane Ixcince tacans the looser of a seucrare Ixcice under sochon 178% of tho

Water Industry Act 1991(e):

we crape services" has the meaning givee in sction 2191) of the Water Industry Actl

48/ We industy ice 19 lar the mating given in seties 17ACT) and 2I9C) of the nak managemcat on bchall of

pay one nvironment Agency, or)

tal tood dinory to three.

Cle the purposes of sub-paragraph l wyneed and "coastal crosion" have the meanings given in section I of the Plood and

Water Management Act 20100h);

pg/w9, management" has the meaning gives in section 3 of that Acci.

229,n29 1) Workers engaged in essential or emergency works- we we stricity interccenector,

regree trict heat network as delined in regulation 2 of the Heat Network (Meicnng and

Billing) Regulations 2014(j).

Billing) Regulations 2014.

put mao ommissicaling, maintenance and repair of industrial machinery for use on al

Рд (1 4/3130. There as Do trialt areading envie:

I canations of the saddle point are thie to lowing)

(11))

Mll9. C is a Lagrange multiplier enforcing the constraint (9). These equations are valid on the support of p. We can prove that these equations lead to a singular support for p. We

do this by contradiction.

woRd then that

397199e first two terms obviously are cancelled. Notice that

3'iMat away from 2' - O we have that

91a + 03) - Pª (log(=*) + log(=*)) - 0|

peese the function is harmonic: a sum of i hodonsorplise plus an antiliolomorphic plece:

RI, is is casy to show that (F-) log(a - 6) o d°(a - 6). With these, we would find that p = 0. This argument contradicts that fp = 1, so the assumption that p is smooth does

not work. This argument has boen given before in (5, 23), and we are presenting it bere for completeness

siddhat this indicates is that the demity distribution of narticks le sinoular. Since tot

9 = 0 the original probability distribution is spherically symmetric, it is natural to assume that the associated distribution is spherically summetric. The radius was computed in 118

Pitchas also been chocked macrically in 6. 7. A geocralization to all Calabi- Yau conc conforinal field tboory singularities for this result can be found in (23), where it was seen that

this procedure matches vurious otber supergravity calculations in the abeence of symmetry.

anger q &gt; 0 but very small, we expect that the distribution does not change much, except in the region :' ≥ 0. Thus we expoct that the distribution gives a five dimensional manifold of

R°, possibly with a boundary, and that the region bear 2' = O has no density of eigenvalues present. After all, the effoctive potential repels all particles from the locus :' = 0. This

smolDocling

Y/I"Woekers engaged in essential or emergensy works.

K/""ential or emergency works" inclades

Greedons, maintenance, repairs, and asset replacement activities,

2016(a).

Repain 2 a. he lies Super it Quie)

Supply (Water

Quality

Regulations 2016(b), the Private Water Supplies (Wales) Regulations 2017(c), or the

Qwen2.5-VL

21. (1) Workers encaned in cascatial or emergency works—

"O) "emal or emergency works" includes-

0) mspoctos, mamienance, repam, and asset replacement actioes and analysis of walcr

Watch

Regulations 2016(b), the Private Water Supplies (Wales) Regulations 2017(c), or the

2016(a).

Supply (Wa

Table 7: Visualizations of layout output from SmolDocling and QwenVL2.5 compared to the DocLayNet ground truth. Examples are chosen to be representative of different layout styles and features. The prediction results however do not represent a generalizable measure of the model's performance on inputs with similar features. (1) Multi-column pages are handled by SmolDocling and Qwen2.5-VL, with some recall errors in the latter. (2) A manual page with terminal output shows poor bounding box recall on SmolDocling and label confusion in Qwen2.5-VL. (3) Lists with nesting are handled well in SmolDocling but confuse Qwen2.5-VL. (4) Both SmolDocling and Qwen2.5-VL reconstruct equations well, however with different annotation conventions on including or excluding the equation index. (5) On a portrait page with colorful elements and gradient background, SmolDocling creates less accurate bounding-boxes and Qwen2.5-VL suffers low recall. (6) On a report page with tables and diagrams, SmolDocling output exhibits some repetition loop, fabricating non-existent text cells (bottom left), while Qwen2.5-VL is confused between tables and pictures. "Wille C is a Lagrange multiplier enforcing the constraint (9). These equations are valid on tbe suppost of p. We can prove that these equations lead to a singular support for p. We do this by contradiction.

do this by contradiction.

<!-- image -->

DEVOTION TO SERVICE

Net incorre heinrat let tormente

Total astets

Working capital

Shareholders equity

Common shares outstanding

$1171

$ 875.228

$ 265,375

$ 436,616

15.971

38,104

$ 1.197,188

$ 295,648

$ 451.169

36.520

Giving back to the and where our

employees live, work, and care for their

families is a serious and dedicated

commitment.

FINANCIAL HIGHLIGHTS

VISA's ongoing commitment to investing in core products and focus on managing costs helped generate a strong finish in 2011.

CROCURR TUAL SALES

BY CORE PRODUCT GROUP

© Fund Gin A Fume Detectior

· Poutis Car Outection

@ Other hodets

60152

1.91

S1015092

$ 287,079

$ 433,666

36,493

DEVOTION TO SERVICE

"Giving back to the and where our

-employees live, work, and care for their

families is a serious

Giving back to the and where our

employees live, work, and care for their

families is a serious

Table 7: Visualizations of layout output from SmolDocling and QwenVL2.5 compared to the DocLayNet ground truth. Examples are chosen to be representative of different layout styles and features. The prediction results however do not represent a generalizable measure of the model's performance on inputs with similar features. (1) Multi-column pages are handled by SmolDocling and Qwen2.5-VL, with some recall errors in the latter. (2) A manual page with terminal output shows poor bounding box recall on SmolDocling and label confusion in Qwen2.5-VL. (3) Lists with nesting are handled well in SmolDocling but confuse Qwen2.5-VL. (4) Both SmolDocling and Qwen2.5-VL reconstruct equations well, however with different annotation conventions on including or excluding the equation index. (5) On a portrait page with colorful elements and gradient background, SmolDocling creates less accurate bounding-boxes and Qwen2.5-VL suffers low recall. (6) On a report page with tables and diagrams, SmolDocling output exhibits some repetition loop, fabricating non-existent text cells (bottom left), while Qwen2.5-VL is confused between tables and pictures.

340.9

DEVOTION TO SERVICE

22%

<!-- image -->

content_html:
<!DOCTYPE html>
<html>
<head>
<meta charset="UTF-8"/>
<title>75483ae0-2151-44da-b354-9bc91d6953d5_2503.11576v1</title>
<meta name="generator" content="Docling HTML Serializer"/>
<style>
    html {
        background-color: #f5f5f5;
        font-family: Arial, sans-serif;
        line-height: 1.6;
    }
    body {
        max-width: 800px;
        margin: 0 auto;
        padding: 2rem;
        background-color: white;
        box-shadow: 0 0 10px rgba(0,0,0,0.1);
    }
    h1, h2, h3, h4, h5, h6 {
        color: #333;
        margin-top: 1.5em;
        margin-bottom: 0.5em;
    }
    h1 {
        font-size: 2em;
        border-bottom: 1px solid #eee;
        padding-bottom: 0.3em;
    }
    table {
        border-collapse: collapse;
        margin: 1em 0;
        width: 100%;
    }
    th, td {
        border: 1px solid #ddd;
        padding: 8px;
        text-align: left;
    }
    th {
        background-color: #f2f2f2;
        font-weight: bold;
    }
    figure {
        margin: 1.5em 0;
        text-align: center;
    }
    figcaption {
        color: #666;
        font-style: italic;
        margin-top: 0.5em;
    }
    img {
        max-width: 100%;
        height: auto;
    }
    pre {
        background-color: #f6f8fa;
        border-radius: 3px;
        padding: 1em;
        overflow: auto;
    }
    code {
        font-family: monospace;
        background-color: #f6f8fa;
        padding: 0.2em 0.4em;
        border-radius: 3px;
    }
    pre code {
        background-color: transparent;
        padding: 0;
    }
    .formula {
        text-align: center;
        padding: 0.5em;
        margin: 1em 0;
        background-color: #f9f9f9;
    }
    .formula-not-decoded {
        text-align: center;
        padding: 0.5em;
        margin: 1em 0;
        background: repeating-linear-gradient(
            45deg,
            #f0f0f0,
            #f0f0f0 10px,
            #f9f9f9 10px,
            #f9f9f9 20px
        );
    }
    .page-break {
        page-break-after: always;
        border-top: 1px dashed #ccc;
        margin: 2em 0;
    }
    .key-value-region {
        background-color: #f9f9f9;
        padding: 1em;
        border-radius: 4px;
        margin: 1em 0;
    }
    .key-value-region dt {
        font-weight: bold;
    }
    .key-value-region dd {
        margin-left: 1em;
        margin-bottom: 0.5em;
    }
    .form-container {
        border: 1px solid #ddd;
        padding: 1em;
        border-radius: 4px;
        margin: 1em 0;
    }
    .form-item {
        margin-bottom: 0.5em;
    }
    .image-classification {
        font-size: 0.9em;
        color: #666;
        margin-top: 0.5em;
    }
</style>
</head>
<body>
<div class='page'>
<h2>SmolDocling: An ultra-compact vision-language model for end-to-end multi-modal document conversion</h2>
<p>Ahmed Nassar 1 , Andres Marafioti 2 , Matteo Omenetti 1 , Maksym Lysak 1 , Nikolaos Livathinos 1 , Christoph Auer 1 , Lucas Morin 1 , Rafael Teixeira de Lima 1 , Yusik Kim 1 , A. Said Gurbuz 1 , Michele Dolfi 1 , Miquel Farré 2 , Peter W. J. Staar 1</p>
<p>IBM Research 1 , HuggingFace 2</p>
<p>https://huggingface.co/ds4sd/SmolDocling-256M-preview Version 1.0, March 14 th 2025</p>
<h2>Abstract</h2>
<p>Weintroduce SmolDocling, an ultra-compact vision-language model targeting end-to-end document conversion. Our model comprehensively processes entire pages by generating DocTags, a new universal markup format that captures all page elements in their full context with location. Unlike existing approaches that rely on large foundational models, or ensemble solutions that rely on handcrafted pipelines of multiple specialized models, SmolDocling offers an end-to-end conversion for accurately capturing content, structure and spatial location of document elements in a 256M parameters vision-language model. SmolDocling exhibits robust performance in correctly reproducing document features such as code listings, tables, equations, charts, lists, and more across a diverse range of document types including business documents, academic papers, technical reports, patents, and forms - significantly extending beyond the commonly observed focus on scientific papers. Additionally, we contribute novel publicly sourced datasets for charts, tables, equations, and code recognition. Experimental results demonstrate that SmolDocling competes with other Vision Language Models that are up to 27 times larger in size, while reducing computational requirements substantially. The model is currently available, datasets will be publicly available soon.</p>
<h2>1 Introduction</h2>
<p>For decades, converting complex digital documents into a structured, machine-processable format has been a significant technical challenge. This challenge primarily stems from the substantial variability in document layouts and styles, as well as the inherently opaque nature of the widely used PDF format, which is optimized for printing rather than semantic parsing. Intricate layout styles and visually challenging elements such as forms, tables, and complex charts can significantly impact the reading order and general understanding of documents. These problems have driven extensive research and development across multiple domains of computer science. On one hand, sophisticated ensemble systems emerged, which decompose the conversion problem into several sub-tasks (e.g. OCR, layout analysis, table structure recognition, classification) and tackle each sub-task independently. Although such systems can achieve high-quality results for many document types while maintaining relatively low computational demands, they are often difficult to tune and generalize.</p>
<p>On the other hand, in the recent past, great interest has developed around large foundational multimodal models that can solve the whole conversion task in one shot, while simultaneously offering flexible querying and parametrization through prompts. This approach has been made possible by the advent of multimodal pre-training of large visionlanguage models (LVLMs), which opened up a vast array of opportunities for leveraging diverse data sources, including PDF documents. However, the literature on this topic highlights a significant gap in the availability of high-quality and open-access datasets suitable for training robust multi-modal models for the task of document understanding. Furthermore, relying on LVLMs may introduce common issues associated with such models, including hallucinations and the use of significant computational resources, making them impractical from both a quality and cost perspective.</p>
<figure><figcaption><div class="caption">Figure 1: SmolDocling/SmolVLM architecture. SmolDocling converts images of document pages to DocTags sequences. First, input images are encoded using a vision encoder and reshaped via projection and pooling. Then, the projected embeddings are concatenated with the text embeddings of the user prompt, possibly with interleaving. Finally, the sequence is used by an LLM to autoregressively predict the DocTags sequence.</div></figcaption></figure>
<p>In this work, we outline how we close the gaps left by publicly available datasets and establish a training approach to achieve end-to-end, full-featured document conversion through a vision-language model, which we name SmolDocling . Our main contributions can be summarized as follows:</p>
<ul>
<li style="list-style-type: '· ';">SmolDocling: An ultra-compact VLM for end-to-end document conversion, based on Hugging Face's most recent SmolVLM-256M [56], which is between 5 and 10 times smaller in parameters than comparable VLMs tuned on document understanding tasks. Hence, we reduce the computational complexity to roughly the same order of magnitude as typical representatives of ensemble methods. Uniquely, SmolDocling learns and produces a unified document representation that captures content, structure and spatial location of all elements in a page.</li>
<li style="list-style-type: '· ';">We augment existing document pre-training datasets with additional feature annotations, and create new datasets for tasks with insufficient coverage in open multi-modal training datasets, including code listings, equations, and chart understanding. Importantly, we construct full-page ground truth data that combines key annotation features such as layout, table structure, code listings, charts, and formulas in their natural context, and derive instruction datasets from all training datasets used.</li>
<li style="list-style-type: '· ';">We introduce DocTags , a document markup format optimized to efficiently represent the full content and layout characteristics of a document, inspired by [55].</li>
<li style="list-style-type: '· ';">We evaluate the achieved prediction quality of SmolDocling on several tasks, including comparisons to popular community models.</li>
</ul>
<h2>2 Related Work</h2>
<h2>2.1 Large Vision-Language Models (LVLMs)</h2>
<p>With the advent of large language models (LLMs) [74, 66, 82, 32, 90, 4, 8, 25, 2] characterized by their powerful contextual understanding and adaptability, several approaches have emerged to extend them into large visionlanguage models. Some proprietary, closed-source LLMs have been adapted into multimodal large language models (MLLMs), such as GPT-4o [65], Gemini [80], and Claude 3.5 [5], demonstrating exceptional capabilities across various modalities, including vision. Open-source methods are actively advancing to match the capabilities of proprietary MLLMs. BLIP-2 [47] is one of the earliest works combining a vision encoder with a frozen LLM (OPT [102] or FlanT5 [14]) using a lightweight transformer (Q-former [101]). Building upon BLIP-2, MiniGPT-4 [105] integrated a frozen visual encoder with a frozen LLM (Vicuna [85]) using a Q-Former network and a single projection layer. LLaVA [52, 51] employs a similar architecture but uses a minimal adapter layer. LLaVA-OneVision [45] and LLaVA-NeXTInterleave [46] build upon LLaVA to support multi-image, higher resolution, and video understanding. For handling high resolutions and text-image compositions and comprehension, InternLM-XComposer introduces a family of models [99, 100, 20, 19] designed specifically for these tasks. Qwen-VL [8] introduces a position-aware adapter to address efficiency issues caused by the vision encoder generating long image feature sequences. Qwen2.5-VL [9]</p>
<p>employs windowed attention with 2D rotary positional embeddings to process native-resolution inputs efficiently and integrates an vision-language merger for dynamic feature compression.</p>
<p>In this work, we build upon the SmolVLM architecture approach [56]. Precisely, we adopt SmolVLM-256M, a compact variant of base SmolVLM-2.2B model, inspired by the architecture of Idefics3 [41] which employs the shape-optimized SigLIP encoder as its visual backbone. It applies an aggressive pixel shuffle strategy to compress visual features, reduces the number of image hidden states and introduces new special tokens to separate sub-images and improve tokenization efficiency.</p>
<h2>2.2 State of the Art in Document Understanding</h2>
<p>Several powerful document understanding solutions have emerged on the market in the past decade as commercial cloud offerings on hyperscalers [24, 62, 3, 6], frontier models such as GPT-4o [65] or Claude [5], or as open-source libraries such as Docling [53], Grobid [1], Marker [67], MinerU [86] or Unstructured [84]. Typical tasks that document understanding encompasses are document classification [96, 29], OCR [48], layout analysis [95, 70], table recognition [103, 55, 78], key-value extraction [29, 35, 31], chart understanding [49, 57, 50, 26, 60, 93, 94, 13, 58, 33, 34], equation recognition [77, 68, 18, 97, 23], and more.</p>
<p>Ensemble systems, such as the aforementioned open-source libraries [53, 1, 67, 86], implement a pipeline in source code, which conditionally applies specialized, single-task models on a given input document, composing the prediction results into a meaningful document representation. Typically, each task entails human-crafted handling of preprocessing and post-processing logic (i.e. setting confidence thresholds for layout detections, matching layout elements to text cells, conditionally applying models).</p>
<p>Conversely, multi-task models aim at providing a single model capable of handling various document-understanding related tasks simultaneously, benefiting from shared context and shared representations learned across these different tasks. OCR-reliant methods, such as LayoutLM [96, 95, 29] and UDOP [79], use text extracted from an external OCR engine, along with image and text bounding box locations, as input. In contrast, OCR-free methods such as Donut [35], Dessurt [17], DocParser [76], Pix2Struct [43] are transformer-based models trained end-to-end to take an image as input and directly output text. Large Vision Language Models (LVLMs) such as LLaVA [51], LLaVAOneVision [45], UReader [98], Kosmos-2 [69], and Qwen-VL [8] all leverage various vision encoders, projection adapters, and LLMs. While they may use different image patching techniques and instruction-tuning datasets to handle a range of vision tasks, they commonly evaluate their document understanding capabilities on datasets like DocVQA [59] and mPLUG-DocOwl 1.5 datasets [27], primarily focusing on question answering and reasoning.</p>
<p>Our work focuses primarily on document understanding tasks related to conversion and structural recognition, aiming to represent documents with high accuracy and completeness. The closest works to our approach are Nougat [12], DocOwl 2 [28], GOT [89], and Qwen2.5-VL [9]. DocOwl 2 and GOT are dedicated to precise conversion and recognition of document structures. On one hand, DocOwl 2 employs a dynamic shape-adaptive cropping module to process high-resolution images, feeding them into a ViT vision encoder. A dedicated module compresses the processed visual features before feeding them into a LLaMa-based LLM. On the other hand, GOT focuses on converting diverse elements-such as text, formulas, molecular diagrams, tables, charts, sheet music, and geometric shapes-into structured formats. It achieves a lightweight architecture by utilizing a SAM vision encoder [37], a linear projection layer, and a Qwen-0.5B LLM. Building on these approaches, our work not only extends the range of tasks but also broadens the types of documents handled, moving beyond a strictly scientific focus to support a more diverse array of document formats. In parallel, Qwen2.5-VL introduces the Omni-Parsing strategy, that integrates multiple document elements into a unified HTML-based representation. This format encodes layout bounding box information and reading order for various document elements such as paragraphs and charts.</p>
<h2>3 SmolDocling</h2>
<p>Building on Hugging Face's SmolVLM [56], SmolDocling converts document page images into sequences representing their content and structure. It produces a unified page representation (DocTags markup) that is optimized for the LLM backbone and interpretable by Docling [53]. This new DocTags format covers the reproduction of captions, charts, forms, code, equations, tables, footnotes, lists, page footers and headers, section headings, and text. SmolDocling both recognizes the type and location of these elements as well as performs OCR within them. Our approach enables full-page conversion with reading-order and hierarchical linking between elements (e.g., linking captions with figures and tables), providing bounding boxes for each component, supporting isolated predictions on individual elements (e.g., cropped tables) and grouping for lists and nested lists.</p>
<p>Fig. 3. OTSL description of table structure: A - table example; B - graphical repre- sentation of table structure; C - mapping structure on a grid; D - OTSL structure</p>
<p>ucccc c c</p>
<p>c c</p>
<p>2 - horizonta merces. C.</p>
<p>I - vertical merges: "C", U</p>
<p>· c</p>
<p>Table 2. TSR and cell detection results compared between OTSL and HTML on the PubTabNet (22], FinTabNet [21] and PubTables-IM [14] data sets using Table-</p>
<p>Former [9) (with enc=6, dec=6, heads=8).</p>
<p>Data set</p>
<p>Language</p>
<p>PubTabNet</p>
<p>FinTabNet</p>
<p>Pub'Tables-1M</p>
<p>OTSL</p>
<p>HTML</p>
<p>OTSL</p>
<p>HTML</p>
<p>OTSL</p>
<p>HTMLI</p>
<p>5.3</p>
<p>15.3 Qualitative Results</p>
<p>The OTS: vocabulary is comprised of the following tokens.</p>
<p>"C" cell - a new table cell that either has or does not have cell content</p>
<p>"L" cell - left-looking cell, merging with the left neighbor cell to create a</p>
<p>"U" cell - up-looking cell, merging with the upper neighbor cell to create a span</p>
<p>"X" cell - cross cell, to merge with both left and upper neighbor cells</p>
<p>"NL" - new-line, switch to the next row.</p>
<p>A notable attribute of OTSL is that it has the capability of achieving lossless</p>
<p>D</p>
<p>expression using a data frame called dif in R where you want to summarize x by month:....</p>
<p>require(plyr)</p>
<p>df &lt;- data. frame(|</p>
<p>x = runif(120, 1, 168),</p>
<p>2 = runif(120, 1.7, 20.7), y = runif(120, 7, 334).</p>
<p>month = rep(c(5,6,7,8),30), week = sample(1:4, 120, TRUE)</p>
<p>ddply(df, · (month, week), sumarize, sd = round(sd(x), 2))</p>
<p>‹picture&gt;&lt;loc_102&gt;&lt;loc_85&gt;&lt;loc_416&gt;&lt;loc_160&gt;~</p>
<p>&lt;flow_chart&gt;~</p>
<p>‹/picture&gt;~</p>
<p>&lt;otsl&gt;&lt;loc_63&gt;&lt;loc_78&gt;&lt;loc_436&gt;&lt;loc_158&gt;*|</p>
<p>‹ched›Data set. &lt;ched&gt;Language&lt;ched&gt;TEDs. &lt;Icel&gt;.</p>
<p>mean = round(mean(x), 2),</p>
<p>Figure 2: DocTags format describes key features of document elements: type of an element (text, picture, table, code, etc.), location on the page, and content. Nested tags convey additional information: pictures and tables may nest captions, table structure is represented in OTSL tags, lists nest list items, code and pictures carry a classification. All DocTags output is taken from SmolDocling predictions, artificial line-breaks and dots (...) were inserted for better readability. A, B, C - snippets of various pages from [55], D - from [92]</p>
<h2>3.1 Model Architecture</h2>
<p>The model architecture is illustrated in Figure 1. SmolVLM-256M relies on SigLIP base patch-16/512 (93M) as visual backbone and compared to the 2.2B version of the same model, its training data was rebalanced to emphasize document understanding (41%) and image captioning (14%), combining The Cauldron [42], Docmatix [41] datasets with the addition of MathWriting [23]. It uses lightweight variant of the SmolLM-2 family (135M) [2] as the language backbone and employs a radical pixel shuffle method that compresses each 512x512 image patch into 64 visual tokens. Last but not least, tokenization efficiency was also improved in SmolVLM-256M by increasing the pixel-to-token ratio to 4096 pixels per token and introducing special tokens for sub-image separators.</p>
<h2>3.2 DocTags</h2>
<p>To train VLMs for standardized document conversion, we introduce the DocTags markup format, illustrated in Figure 3. Inspired by OTSL [55], DocTags define a structured vocabulary of unambiguous tags and rules that explicitly separate textual content from document structure. This disentanglement improves performances of Image-to-Sequence models by minimizing confusions. In contrast, direct conversion to standard formats such as HTML or Markdown is often lossy and ambiguous, lacking explicit structural markup, failing to preserve page layout context, and increasing total tokens count. Pieces of text content that represent basic elements in DocTags are enclosed in XML-style tags. Tags define document block types such as: text, caption, footnote, formula, title, list_item, page_footer, page_header, picture, section_header, otsl, code, document_index, etc. Each element can nest additional location tags encoding its position on the page as a bounding box, represented in DocTags as &lt;loc_ x1 &gt;&lt;loc_ y1 &gt;&lt;loc_ x2 &gt;&lt;loc_ y2 &gt; .</p>
<p>Special blocks, such as tables and images, nest additional descriptors for captions, tabular structure, or image classes. To encode table structures, we fully include the OTSL vocabulary into DocTags . OTSL has the advantage of being minimalist and supporting cell spans and header information. Its tokens are interleaved with text, allowing</p>
<p>,&lt;ched&gt;mAP(0.75)&lt;ched&gt;Inference..&lt;nl&gt;~</p>
<p>SmolDocling: An ultra-compact vision-language model for end-to-end multi-modal document conversion simultaneous encoding of both table structure and content. In order to promote robust visual-semantic alignment in our document understanding pipeline, we maintain a uniform DocTags representation for cropped page elements (e.g., tables, code, equations) that is identical to their full-page counterparts. This consistency ensures that the model can leverage similar visual cues whether an element is presented in isolation or in the context of an entire page, thereby strengthening the learning signal for both computer vision and downstream machine learning tasks.</p>
<p>For a complete list of tags and detailed description of the format, consult the appendix.</p>
<h2>3.3 Model Training</h2>
<p>We employ a curriculum learning approach to progressively align our model for document conversion, ensuring faster convergence. As an initial step, we incorporate DocTags as tokens in our tokenizer. To align the LLM part we freeze the vision encoder and train only the remaining network to adapt it to the new output format which it hasn't seen before. To ensure comprehensive coverage of all DocTags, we maintain a balanced mix of tasks and data types during training. Next, we unfreeze the vision encoder and train the model on our pretraining datasets (see Sec. 4.1), along with all task-specific conversion datasets, including tables, code, equations, and charts. Finally, we fine-tune using all available datasets.</p>
<h2>4 Data</h2>
<p>For document understanding tasks, each page image represents the visual layout of textual elements-such as paragraphs, formulas, tables, code, or figures with embedded textual content. Publicly available datasets that unify all these characteristics remain limited, and existing corpora often suffer from small sample sizes or fragmented formats. In this section, we explain how we mitigate these issues in both pre-training and task-specific training scenarios.</p>
<h2>4.1 Pre-training datasets</h2>
<p>In the domain of document understanding, currently available open-access document pre-training datasets include OCR-IDL [10], IIT-CDIP [44], CCpdf [83], and WordScape [88]. OCR-IDL and IIT-CDIP, industry-sourced from the 1990s, provide only page images and OCR text. CCpdf and WordScape, derived from Common Crawl, focus on raw PDFs and Word documents, respectively. While WordScape enriches documents with layout, table structure, and topic information via XML markup, its visual variability is limited. Notably, none of these datasets offer rich annotations for all page elements.</p>
<p>DocLayNet-PT. Given the lack of public multimodal document pre-training data with sufficient annotation features, we constructed a new document pre-training dataset named DocLayNet-PT. DocLayNet-PT is a 1.4M pages dataset extracted from the DocFM dataset [25] of unique PDF documents, sourced from CommonCrawl, Wikipedia, and business-related documents. We focused on this dataset to look for documents including visually distinct content such as equations, tables, code and charts with colorful layouts. This dataset was augmented with weak annotations through a series of processing steps. First, we applied PDF parsing to all pages to obtain the text layout including positional information using Docling [7, 53]. Next, we performed an enrichment step to obtain an enhanced, machine-friendly representation of each page. Specifically, we provide annotations for layout elements, table structure, language, topic, and figure classification on each page in DocTag format.</p>
<p>Docmatix. To preserve SmolVLM's original DocVQA capabilities, we adapted the same weak annotation strategy used for DocLayNet-PT and applied it to the 1.3M documents inside Docmatix dataset [41]. In addition to the 9.5 million DocVQA questions, we introduced another instruction requiring the full conversion of multi-page documents to DocTags.</p>
<h2>4.2 Task-specific Datasets</h2>
<p>Layout. To optimize prediction quality for document layout and table structure, we sampled 76K pages from DocLayNet-PT. Each page underwent human annotation and rigorous quality review, resulting in a high-quality ground-truth dataset. Of these, 60K pages were utilized for fine-tuning, constituting what we refer to as DocLayNet v2. Additionally, we extracted 63K pages from WordScape, specifically filtering for pages containing both text and tables, and utilized their inherently structure-preserving format as a reliable source of ground truth. To further enhance the model's adaptability to diverse layouts, colors, and fonts, we synthetically generated 250K pages using content from Wikipedia, creating the dataset known as SynthDocNet. This synthetic dataset enables the controlled generation of various document types with precise annotations. All datasets share a unified annotation format and class definitions, ensuring consistency and comprehensive document representation.</p>
<p>Tables. For the table structure recognition task we trained our model on public datasets such as PubTables-1M [78], FinTabNet [103], WikiTableSet [54] and tabular information extracted from WordScape documents. We transformed table structure information from the original ground-truth to OTSL format [55]. Then, we interleaved each cell tag with ground-truth text. This yields a condensed sequence which represents the structure of the table (including merged cells, and header declaration) along with its textual content.</p>
<p>Charts. For chart reconstruction, several datasets are publicly available [58, 33, 34, 93] that consist of both synthetic and web-scraped charts. However, these datasets are limited by either the quantity of real-world data or the visual diversity of synthetic examples. This has prompted recent studies [50, 26, 60, 94, 93, 13, 57] to crawl or render additional datasets, which unfortunately are rarely shared publicly. To address these gaps, we generated our own chart dataset comprising four types of charts: line, pie, bar, and stacked bar. Using data from 90,000 tables sourced from the FinTabNet dataset [103], we generated a comprehensive dataset containing 5,000 line charts, 380,000 pie charts, 380,000 bar charts, and 77,000 stacked bar charts. Each data point was rendered using three different visualization libraries (details provided in the Appendix), yielding a total of 2.5 million visually diverse charts.</p>
<p>Code. Technical books and scientific documents frequently include code snippets, making it essential for documentprocessing models to accurately interpret code structure, particularly indentation, which carries semantic significance in certain programming languages. Several publicly available datasets provide extensive collections of code snippets with permissive licenses in text format [38, 71], typically aggregated from sources like GitHub. However, a common limitation is that these datasets contain only text-based code snippets, lacking any image-based representation. To address this gap, we leveraged LaTeX and Pygments [16] to generate visually diverse renderings of code (details provided in the Appendix). Our dataset includes 9.3M million rendered code snippets generated at 120 dpi, covering 56 programming languages.</p>
<p>Equations. Accurately parsing mathematical formulas is crucial for document-processing models, as formulas often contain nuanced structures and semantics critical for scientific understanding. To effectively train our model for formula transcription, we combined publicly available datasets with a custom-rendered dataset. The public datasets [18, 22, 11, 23] collectively comprise approximately 730k unique formulas. In addition, we extracted 4.7 million formulas from arXiv by applying a regular expression to the source LaTeX code. The extracted formulas underwent a rigorous normalization procedure to ensure the model was trained only on correct and standardized LaTex code. Subsequently, these normalized equations were visually rendered using LaTeX (details provided in the Appendix). Overall, our dataset includes 5.5 million unique formulas, each rendered at a resolution of 120 dpi.</p>
<table><caption><div class="caption">Table 1: Datasets. Overview of the datasets used for the training and evaluation of SmolDocling. Datasets we contribute as part of this publication are denoted with (*).</div></caption><tbody><tr><th>Dataset Name</th><th>Size</th><th>Type</th></tr><tr><th>TheCauldron [42]</th><td>2.63M</td><td></td></tr><tr><th>DocLayNet-PT-Instruct</th><td>1.62M</td><td>Multi Task</td></tr><tr><th>DocMatix w/ Doc Conversion* [41]</th><td>952K</td><td></td></tr><tr><th>SynthChartNet*</th><td>2.5M</td><td></td></tr><tr><th>PlotQA [61]</th><td>225K</td><td></td></tr><tr><th>FigureQA [33]</th><td>100K</td><td>Chart</td></tr><tr><th>Unichart [57]</th><td>600K</td><td></td></tr><tr><th>ChartQA [58]</th><td>28K</td><td></td></tr><tr><th>DocLayNet-PT</th><td>1.40M</td><td></td></tr><tr><th>Doc4MLlaVa - RVLCDIP</th><td>742K</td><td></td></tr><tr><th>SynthDocNet</th><td>375K</td><td>Conversion</td></tr><tr><th>DocLayNet [70]</th><td>81K</td><td></td></tr><tr><th>DocLayNet v2</th><td>76K</td><td></td></tr><tr><th>WordScape-PT</th><td>63K</td><td></td></tr><tr><th>PubTable1M [78]</th><td>1M</td><td></td></tr><tr><th>WordScape Tables [88]</th><td>207K</td><td>Table</td></tr><tr><th>WikiTableSet (EN) [54]</th><td>204K</td><td></td></tr><tr><th>FinTabNet [103]</th><td>90K</td><td></td></tr><tr><th>SynthFormulaNet*</th><td>5.5M</td><td>Formula</td></tr><tr><th>SynthCodeNet*</th><td>9.3M</td><td>Code</td></tr></tbody></table>
<h2>4.3 Document Instruction Tuning datasets</h2>
<p>To reinforce the recognition of different page elements and introduce document-related features and no-code pipelines, we leveraged rule-based techniques and the Granite-3.1-2b-instruct [25] LLM. Using samples from DocLayNet-PT pages, we generated one instruction by randomly sampling layout elements from a page. These instructions included tasks such as 'Perform OCR at bbox,' 'Identify page element type at bbox,' and 'Extract all section headers from the page.' In addition, we train with Cauldron [42] to avoid catastrophic forgetting with the amount of conversation datasets introduced.</p>
<table><tbody><tr><th>Method</th><th>Size</th><th>Edit Distance ↓</th><th>F1-score ↑</th><th>Precision ↑</th><th>Recall ↑</th><th>BLEU ↑</th><th>METEOR ↑</th></tr><tr><th>Full-page</th><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>Qwen2.5 VL [9]</th><td>7B</td><td>0.56</td><td>0.72</td><td>0.80</td><td>0.70</td><td>0.46</td><td>0.57</td></tr><tr><th>GOT [89]</th><td>580M</td><td>0.61</td><td>0.69</td><td>0.71</td><td>0.73</td><td>0.48</td><td>0.59</td></tr><tr><th>Nougat (base) [12]</th><td>350M</td><td>0.62</td><td>0.66</td><td>0.72</td><td>0.67</td><td>0.44</td><td>0.54</td></tr><tr><th>SmolDocling (Ours)</th><td>256M</td><td>0.48</td><td>0.80</td><td>0.89</td><td>0.79</td><td>0.58</td><td>0.67</td></tr><tr><th>Code listings</th><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>SmolDocling (Ours)</th><td>256M</td><td>0.11</td><td>0.92</td><td>0.94</td><td>0.91</td><td>0.87</td><td>0.89</td></tr><tr><th>Equations</th><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>Qwen2.5 VL [9]</th><td>7B</td><td>0.22</td><td>0.89</td><td>0.91</td><td>0.87</td><td>0.68</td><td>0.77</td></tr><tr><th>GOT [89]</th><td>580M</td><td>0.11</td><td>0.95</td><td>0.95</td><td>0.96</td><td>0.85</td><td>0.91</td></tr><tr><th>Nougat (base) [12]</th><td>350M</td><td>0.62</td><td>0.60</td><td>0.60</td><td>0.53</td><td>0.33</td><td>0.41</td></tr><tr><th>SmolDocling (Ours)</th><td>256M</td><td>0.11</td><td>0.95</td><td>0.96</td><td>0.95</td><td>0.83</td><td>0.89</td></tr></tbody></table>
<p>Table 2: Structured document text recognition. We evaluate OCR performance and document formatting on DocLayNet, focusing on textual elements, excluding tables. Text accuracy is measured through multiple metrics (Edit Distance, F1-Score, Precision, Recall, BLEU and METEOR) as proposed in [12].</p>
<h2>5 Experiments</h2>
<p>In this section, we assess the performance of SmolDocling on document understanding tasks, and compare the results to previously published models and baselines. Capturing the qualities and defects of SmolDocling and other models in comparable numbers is a non-trivial effort for reasons we outline further below. Therefore, we report results on standard evaluation tasks and metrics independently (see section 5.2), but also provide a qualitative assessment of output characteristics (see section 5.3).</p>
<h2>5.1 Implementation Details</h2>
<p>Our training was conducted using 64 NVIDIA A100 80GB GPUs, with each epoch requiring 38 hours. The model was trained for a total of 4 epochs using the AdamW optimizer with a learning rate of 2 × 10 -4 and 2 × 10 -6 for the vision encoder once unfrozen. We also employed gradient clipping to 1.0 for stability and a warmup ratio of 0.03. For inference, using VLLM [39] on an A100 GPU, SmolDocling achieves a page conversion time of 0.35 seconds while occupying only 0.489 GB of VRAM. The model supports a maximum sequence length of 8,192 tokens and is trained to process up to three pages at a time.</p>
<h2>5.2 Quantitative Results</h2>
<p>To enable valid cross-model comparisons with SmolDocling, we addressed multiple dataset and harmonization challenges:</p>
<ul>
<li style="list-style-type: '· ';">Public evaluation datasets lack comprehensive multi-task annotations with diverse layouts. We augmented DocLayNet [70] with text content and reading-order to enable evaluation of both layout analysis and text recognition. Table structure, code listing, formula and chart recognition are evaluated on specific datasets.</li>
<li style="list-style-type: '· ';">Different models each follow their own conventions for output markup (e.g., Markdown, HTML, DocTags) and produce annotations which do not fully align in semantics. For example, SmolDocling is trained to produce 14 distinct layout-class tags via DocTags , while Qwen2.5-VL combines standard HTML tags and a few explicit class attributes with only partially compatible definitions. GOT and Nougat output Markdown, which can express paragraphs, lists and tables without location annotation.</li>
</ul>
<p>SmolDocling: An ultra-compact vision-language model for end-to-end multi-modal document conversion</p>
<ul>
<li style="list-style-type: '· ';">Input resolution of page images can affect model performance, but is rarely specified in literature. We standardized at 144 DPI (dots per inch), which reproduces enough detail to be perfectly legible by humans while keeping resource demand for inference reasonable.</li>
</ul>
<p>Text Recognition (OCR). We first evaluate text recognition accuracy of SmolDocling, compared to Nougat, GOT and Qwen2.5-VL (Table 2), to verify accurate character transcription and reading order. The text-similarity metrics, adopted from [12] require plain formatting without non-textual elements. For different tasks, we conduct evaluations on diverse content types: full-page documents (from DocLayNet test set) in Markdown format, code snippets (from our SynthCodeNet dataset) in plaintext, and equations (from Im2Latex-230k [21]) in LaTeX format. For Qwen2.5-VL and SmolDocling, we convert HTML or DocTags output to Markdown while preserving formatting where possible.</p>
<p>Tables are omitted from the full-page output since they do not compare well through Markdown, due to loss of row and column spans. Code snippets must be reproduced with intact line breaks and indentation for accurate scoring. Formulas are evaluated separately in LaTeX format, which is natively produced by all the models tested in this work. The LaTeX outputs from all models, including the ground truth, undergo the same normalization procedure used for generating the SynthFormulaNet dataset. This ensures consistency and fairness in model comparisons.</p>
<p>SmolDocling significantly outperforms GOT, Nougat and also Qwen2.5-VL, which is 27 times larger, on every metric in full-page transcription (see Table 2. For the code parsing task, we report results exclusively for SmolDocling, as we introduce this task for the first time, and the other models were not explicitly trained for it. SmolDocling also demonstrates superior performance compared to Nougat and Qwen2.5-VL in the formula recognition task, closely matching GOT's results.</p>
<p>Layout. We evaluate element localization accuracy using the DocLayNet test set (Table 3), measuring the ability to reconstruct document representations that support content grounding and visual feature extraction. SmolDocling is compared against Qwen2.5-VL-7b [9], currently the only other VLM addressing this task. For a fair comparison, we map each model's label outputs and the ground-truth to six compatible classes: Text, Section Heading, List Item, Table, Picture, and Formula. Overall, SmolDocling outperforms Qwen2.5-VL-7b by a significant margin, however both models score far below a human baseline. This can be partially explained through inherent difficulty of the dataset, as indicated by relatively low human agreement scores. The scores are primarily impacted by moderate recall of element bounding boxes and different degrees of label confusion in both SmolDocling and Qwen2.5-VL. A majority of samples show accurate element bounding boxes as seen in the Appendix.</p>
<table><tbody><tr><td></td><th>SmolDocling</th><th>Qwen2.5-VL</th><th>Human</th></tr><tr><th>Formula</th><td>0.267</td><td>0.059</td><td>0.84</td></tr><tr><th>Table</th><td>0.266</td><td>0.262</td><td>0.79</td></tr><tr><th>List Item</th><td>0.296</td><td>0.090</td><td>0.87</td></tr><tr><th>Section Header</th><td>0.289</td><td>0.129</td><td>0.83</td></tr><tr><th>Text</th><td>0.204</td><td>0.180</td><td>0.85</td></tr><tr><th>Picture</th><td>0.066</td><td>0.078</td><td>0.70</td></tr><tr><th>Overall</th><td>0.231</td><td>0.133</td><td>0.82</td></tr></tbody></table>
<p>Table 3: Layout analysis comparison. Evaluation of layout analysis task on DocLayNet with a subset of 6 class labels in mAP[0.5:0.95] metric. Additional reference points are provided for an estimate of human performance [70].</p>
<p>Table Structure Recognition. We evaluate the accuracy of recovering table structures, i.e. shaping columns and rows with the correct text content and spans, through the TEDS metric [104], once including table text content and once on structure only with ommitted text (see Table 4). For isolated assessment, we provide cropped table images from FinTabNet [103] and PubTables-1M [78] test sets, which have poor image quality (72 dpi, compression artifacts). SmolDocling performs competitively against significantly larger models, despite challenges with text transcription from the low-resolution image crops, a limitation likely attributable to insufficient training at such resolutions. Scores for structure only TEDS evaluation are therefore much stronger.</p>
<p>Chart Extraction. In this task, we evaluate the ability to convert cropped sections of charts into datapoint tables. All table ouptut is transformed into HTML and compared against the ground-truth HTML using TEDS score, as reported in Table 5. We attribute SmolDocling's performance on charts to the variability in ground-truth styles and the differing interpretations of representations across datasets, which we observe leads to inconsistencies in the model's predictions. Despite its significantly smaller size, SmolDocling achieves a competitive TEDs score.</p>
<p>Overall, SmolDocling achieves leading performance across a wide range of document conversion tasks and outperforms significantly larger models in text recognition, layout analysis and structure extraction. Furthermore, the model's</p>
<p>SmolDocling: An ultra-compact vision-language model for end-to-end multi-modal document conversion</p>
<table><tbody><tr><th>Method</th><th>Model Size</th><th>FinTabNet</th><th>PubTables-1M</th></tr><tr><th>SmolVLM [56]</th><td>2.2B</td><td>0.18</td><td>-</td></tr><tr><th>Granite Vision [81]</th><td>2.98B</td><td>0.54</td><td>-</td></tr><tr><th>TableFormer† [55]</th><td>52M</td><td>0.89</td><td>0.84</td></tr><tr><th>SmolDocling (Ours)</th><td>256M</td><td>0.52 (0.81)</td><td>0.65 (0.88)</td></tr></tbody></table>
<p>Table 4: Table structure and cell content reconstruction. Numbers are given in TEDS metric including text content, with structure-only score in brackets where available. We produced results for SmolDocling and TableFormer, other results are reflected here as published. †TableFormer extracts the content through external OCR.</p>
<table><tbody><tr><td>Model</td><th>Size</th><th>TEDs ↑</th></tr><tr><th>Phi-3.5-vision</th><td>4B</td><td>0.40</td></tr><tr><th>Granite Vision [81]</th><td>3B</td><td>0 . 95</td></tr><tr><th>SmolVLM [56]</th><td>2.2B</td><td>0.02</td></tr><tr><th>Molmo-E</th><td>1B</td><td>0.54</td></tr><tr><th>SmolDocling (Ours)</th><td>256M</td><td>0.75</td></tr></tbody></table>
<p>Table 5: Chart analysis. We compare SmolDocling to other small-sized VLMs on the chart-to-table task using the TEDS metric. The results in this table are based on the work reported in [25].</p>
<p>high performance in downstream tasks, such as code listing and formula recognition, demonstrates its versatility and real-world applicability. Notably, we establish benchmark results for the code listing recognition, setting a standard for future evaluations.</p>
<h2>5.3 Qualitative Results</h2>
<p>Several characteristics of SmolDocling output are not evident from task-based metrics alone. In this section, we highlight different output characteristics observed in practice. More illustrated samples can be found in the Appendix.</p>
<p>As presented in Figure 3, SmolDocling outputs a sequence of DocTags to encode content, structure and layout. A few notable traits beyond straight content reproduction include: 1) Labeling of page headers and footers, which allows ignoring these repetitive elements in downstream applications. 2) Linking of information, such as captions to tables or pictures, or list elements to parent list through nesting of tags. 3) Preserved line breaks and indentation for code snippets, while line wraps in paragraphs are correctly removed.</p>
<p>Among typical defects found in the output of SmolDocling are missing tags (e.g. absent location tags), malformed structure (such as missing closing tags), and endless repetition of tokens from some point in a page. Both of these phenomena are commonly seen in auto-regressive models and may prevent parsing or rendering the output correctly (see Appendix). Despite these limitations, SmolDocling demonstrates unique robustness advantages compared to ensemble systems like Docling, since conversion output is inferred in a single pass. In the latter, errors may accumulate throughout the ensemble model pipeline. For example, a mislocated table detection will lead to missing or distorted output of table structure and content, since this depends on correct table localization. In contrast, SmolDocling can reproduce the tables correctly, even with incorrectly determined location.</p>
<h2>6 Conclusion</h2>
<p>In this work, we introduce SmolDocling, an efficient and compact VLM optimized for document conversion while providing rich output representation. We also present a suite of new datasets with a unified format for document conversion, including the novel task of code listing transcription. We identify page element localization as a critical area requiring further refinement, where targeted techniques will significantly enhance performance in future iterations. Our results conclusively demonstrate that smaller models with unified, optimized output formats such as DocTags can effectively compete with significantly larger models, establishing a clear pathway for resource-efficient multi-task document understanding models.</p>
<h2>References</h2>
<ol>
<li style="list-style-type: '[1] ';">Grobid. https://github.com/kermitt2/grobid , 2008-2025.</li>
<li style="list-style-type: '[2] ';">Loubna Ben Allal, Anton Lozhkov, Elie Bakouch, Gabriel Martín Blázquez, Guilherme Penedo, Lewis Tunstall, Andrés Marafioti, Hynek Kydlíček, Agustín Piqueres Lajarín, Vaibhav Srivastav, Joshua Lochner, Caleb Fahlgren, Xuan-Son Nguyen, Clémentine Fourrier, Ben Burtenshaw, Hugo Larcher, Haojun Zhao, Cyril Zakka, Mathieu Morlon, Colin Raffel, Leandro von Werra, and Thomas Wolf. SmolLM2: When Smol Goes Big - Data-Centric Training of a Small Language Model, 2025.</li>
<li style="list-style-type: '[3] ';">Amazon Web Services. Amazon Textract. https://aws.amazon.com/textract/ , 2024. Accessed: 2024-11-11.</li>
<li style="list-style-type: '[4] ';">Rohan Anil, Andrew M Dai, Orhan Firat, Melvin Johnson, Dmitry Lepikhin, Alexandre Passos, Siamak Shakeri, Emanuel Taropa, Paige Bailey, Zhifeng Chen, et al. Palm 2 technical report. arXiv preprint arXiv:2305.10403 , 2023.</li>
<li style="list-style-type: '[5] ';">Anthropic. Claude-3.5. https://www.anthropic.com/news/claude-3-5-sonnet , 2024. Accessed: 202402-11.</li>
<li style="list-style-type: '[6] ';">Christoph Auer, Michele Dolfi, André Carvalho, Cesar Berrospi Ramis, and Peter WJ Staar. Delivering Document Conversion as a Cloud Service with High Throughput and Responsiveness. In 2022 IEEE 15th International Conference on Cloud Computing (CLOUD) , pages 363-373. IEEE, 2022.</li>
<li style="list-style-type: '[7] ';">Christoph Auer, Maksym Lysak, Ahmed Nassar, Michele Dolfi, Nikolaos Livathinos, Panos Vagenas, Cesar Berrospi Ramis, Matteo Omenetti, Fabian Lindlbauer, Kasper Dinkla, et al. Docling Technical Report. arXiv preprint arXiv:2408.09869 , 2024.</li>
<li style="list-style-type: '[8] ';">Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, et al. Qwen technical report. arXiv preprint arXiv:2309.16609 , 2023.</li>
<li style="list-style-type: '[9] ';">Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang, Humen Zhong, Yuanzhi Zhu, Mingkun Yang, Zhaohai Li, Jianqiang Wan, Pengfei Wang, Wei Ding, Zheren Fu, Yiheng Xu, Jiabo Ye, Xi Zhang, Tianbao Xie, Zesen Cheng, Hang Zhang, Zhibo Yang, Haiyang Xu, and Junyang Lin. Qwen2.5-VL Technical Report, 2025.</li>
<li style="list-style-type: '[10] ';">Ali Furkan Biten, Rubèn Tito, Lluis Gomez, Ernest Valveny, and Dimosthenis Karatzas. Ocr-idl: Ocr annotations for industry document library dataset. In European Conference on Computer Vision , pages 241-252. Springer, 2022.</li>
<li style="list-style-type: '[11] ';">Lukas Blecher. LaTeX-OCR, 2024. Accessed: 2024-11-09.</li>
<li style="list-style-type: '[12] ';">Lukas Blecher, Guillem Cucurull, Thomas Scialom, and Robert Stojnic. Nougat: Neural optical understanding for academic documents. arXiv preprint arXiv:2308.13418 , 2023.</li>
<li style="list-style-type: '[13] ';">Jinyue Chen, Lingyu Kong, Haoran Wei, Chenglong Liu, Zheng Ge, Liang Zhao, Jianjian Sun, Chunrui Han, and Xiangyu Zhang. OneChart: Purify the Chart Structural Extraction via One Auxiliary Token. arXiv preprint arXiv:2404.09987 , 2024.</li>
<li style="list-style-type: '[14] ';">Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al. Scaling instruction-finetuned language models. Journal of Machine Learning Research , 25(70):1-53, 2024.</li>
<li style="list-style-type: '[15] ';">Pyecharts Contributors. pyecharts: Python library for Echarts, 2024. Accessed: 2024-11-06.</li>
<li style="list-style-type: '[16] ';">Pygments Contributors. Pygments: Python syntax highlighter, 2024. Accessed: 2024-11-06.</li>
<li style="list-style-type: '[17] ';">Brian Davis, Bryan Morse, Brian Price, Chris Tensmeyer, Curtis Wigington, and Vlad Morariu. End-to-end document recognition and understanding with dessurt. In European Conference on Computer Vision , pages 280-296. Springer, 2022.</li>
<li style="list-style-type: '[18] ';">Yuntian Deng, Anssi Kanervisto, Jeffrey Ling, and Alexander M Rush. Image-to-markup generation with coarse-to-fine attention. In International Conference on Machine Learning , pages 980-989. PMLR, 2017.</li>
<li style="list-style-type: '[19] ';">Xiaoyi Dong, Pan Zhang, Yuhang Zang, Yuhang Cao, Bin Wang, Linke Ouyang, Xilin Wei, Songyang Zhang, Haodong Duan, Maosong Cao, Wenwei Zhang, Yining Li, Hang Yan, Yang Gao, Xinyue Zhang, Wei Li, Jingwen Li, Kai Chen, Conghui He, Xingcheng Zhang, Yu Qiao, Dahua Lin, and Jiaqi Wang. InternLM-XComposer2: Mastering Free-form Text-Image Composition and Comprehension in Vision-Language Large Model. arXiv preprint arXiv:2401.16420 , 2024.</li>
<li style="list-style-type: '[20] ';">Xiaoyi Dong, Pan Zhang, Yuhang Zang, Yuhang Cao, Bin Wang, Linke Ouyang, Songyang Zhang, Haodong Duan, Wenwei Zhang, Yining Li, Hang Yan, Yang Gao, Zhe Chen, Xinyue Zhang, Wei Li, Jingwen Li, Wenhai Wang, Kai Chen, Conghui He, Xingcheng Zhang, Jifeng Dai, Yu Qiao, Dahua Lin, and Jiaqi Wang. InternLM-XComposer2-4KHD: A Pioneering Large Vision-Language Model Handling Resolutions from 336 Pixels to 4K HD. arXiv preprint arXiv:2404.06512 , 2024.</li>
<li style="list-style-type: '[21] ';">Gregory Eritsyan. Im2LaTeX 230K. https://www.kaggle.com/datasets/gregoryeritsyan/ im2latex-230k , 2023. Accessed: 2025-03-06.</li>
<li style="list-style-type: '[22] ';">Hugging Face. latex-formulas Dataset, 2024. Accessed: 2024-11-09.</li>
<li style="list-style-type: '[23] ';">Philippe Gervais, Asya Fadeeva, and Andrii Maksai. Mathwriting: A dataset for handwritten mathematical expression recognition. URL https://arxiv. org/abs/2404.10690 , 2024.</li>
<li style="list-style-type: '[24] ';">Google Cloud. Document AI. https://cloud.google.com/document-ai , 2024. Accessed: 2024-11-11.</li>
<li style="list-style-type: '[25] ';">IBM Granite Team. Granite 3.0 Language Models, 2024.</li>
</ol>
<ol>
<li style="list-style-type: '[26] ';">Yucheng Han, Chi Zhang, Xin Chen, Xu Yang, Zhibin Wang, Gang Yu, Bin Fu, and Hanwang Zhang. Chartllama: A multimodal llm for chart understanding and generation. arXiv preprint arXiv:2311.16483 , 2023.</li>
<li style="list-style-type: '[27] ';">Anwen Hu, Haiyang Xu, Jiabo Ye, Ming Yan, Liang Zhang, Bo Zhang, Chen Li, Ji Zhang, Qin Jin, Fei Huang, et al. mplug-docowl 1.5: Unified structure learning for ocr-free document understanding. arXiv preprint arXiv:2403.12895 , 2024.</li>
<li style="list-style-type: '[28] ';">Anwen Hu, Haiyang Xu, Liang Zhang, Jiabo Ye, Ming Yan, Ji Zhang, Qin Jin, Fei Huang, and Jingren Zhou. mplug-docowl2: High-resolution compressing for ocr-free multi-page document understanding. arXiv preprint arXiv:2409.03420 , 2024.</li>
<li style="list-style-type: '[29] ';">Yupan Huang, Tengchao Lv, Lei Cui, Yutong Lu, and Furu Wei. Layoutlmv3: Pre-training for document ai with unified text and image masking. In Proceedings of the 30th ACM International Conference on Multimedia , pages 4083-4091, 2022.</li>
<li style="list-style-type: '[30] ';">J. D. Hunter. Matplotlib: A 2D graphics environment. Computing in Science &amp; Engineering , 9(3):90-95, 2007.</li>
<li style="list-style-type: '[31] ';">Guillaume Jaume, Hazim Kemal Ekenel, and Jean-Philippe Thiran. Funsd: A dataset for form understanding in noisy scanned documents. In 2019 International Conference on Document Analysis and Recognition Workshops (ICDARW) , volume 2, pages 1-6. IEEE, 2019.</li>
<li style="list-style-type: '[32] ';">Albert Q Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, et al. Mixtral of experts. arXiv preprint arXiv:2401.04088 , 2024.</li>
<li style="list-style-type: '[33] ';">Samira Ebrahimi Kahou, Vincent Michalski, Adam Atkinson, Ákos Kádár, Adam Trischler, and Yoshua Bengio. Figureqa: An annotated figure dataset for visual reasoning. arXiv preprint arXiv:1710.07300 , 2017.</li>
<li style="list-style-type: '[34] ';">Shankar Kantharaj, Rixie Tiffany Ko Leong, Xiang Lin, Ahmed Masry, Megh Thakkar, Enamul Hoque, and Shafiq Joty. Chart-to-text: A large-scale benchmark for chart summarization. arXiv preprint arXiv:2203.06486 , 2022.</li>
<li style="list-style-type: '[35] ';">Geewook Kim, Teakgyu Hong, Moonbin Yim, Jinyoung Park, Jinyeong Yim, Wonseok Hwang, Sangdoo Yun, Dongyoon Han, and Seunghyun Park. Donut: Document understanding transformer without ocr. arXiv preprint arXiv:2111.15664 , 7(15):2, 2021.</li>
<li style="list-style-type: '[36] ';">Sunghwan Kim, Jie Chen, Tiejun Cheng, Asta Gindulyte, Jia He, Siqian He, Qingliang Li, Benjamin A Shoemaker, Paul A Thiessen, Bo Yu, Leonid Zaslavsky, Jian Zhang, and Evan E Bolton. PubChem 2019 update: improved access to chemical data. Nucleic Acids Research , 47(D1):D1102-D1109, 10 2018.</li>
<li style="list-style-type: '[37] ';">Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al. Segment anything. In Proceedings of the IEEE/CVF International Conference on Computer Vision , pages 4015-4026, 2023.</li>
<li style="list-style-type: '[38] ';">Denis Kocetkov, Raymond Li, Loubna Ben Allal, Jia Li, Chenghao Mou, Carlos Muñoz Ferrandis, Yacine Jernite, Margaret Mitchell, Sean Hughes, Thomas Wolf, Dzmitry Bahdanau, Leandro von Werra, and Harm de Vries. The Stack: 3 TB of permissively licensed source code. Preprint , 2022.</li>
<li style="list-style-type: '[39] ';">Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph E. Gonzalez, Hao Zhang, and Ion Stoica. Efficient Memory Management for Large Language Model Serving with PagedAttention. In Proceedings of the ACM SIGOPS 29th Symposium on Operating Systems Principles , 2023.</li>
<li style="list-style-type: '[40] ';">Greg Landrum. RDKit: Open-Source Cheminformatics Software. http://www.rdkit.org/. Accessed: 1 January 2023.</li>
<li style="list-style-type: '[41] ';">Hugo Laurençon, Andrés Marafioti, Victor Sanh, and Léo Tronchon. Building and better understanding vision-language models: insights and future directions., 2024.</li>
<li style="list-style-type: '[42] ';">Hugo Laurençon, Léo Tronchon, Matthieu Cord, and Victor Sanh. What matters when building vision-language models?, 2024.</li>
<li style="list-style-type: '[43] ';">Kenton Lee, Mandar Joshi, Iulia Raluca Turc, Hexiang Hu, Fangyu Liu, Julian Martin Eisenschlos, Urvashi Khandelwal, Peter Shaw, Ming-Wei Chang, and Kristina Toutanova. Pix2struct: Screenshot parsing as pretraining for visual language understanding. In International Conference on Machine Learning , pages 18893-18912. PMLR, 2023.</li>
<li style="list-style-type: '[44] ';">David Lewis, Gady Agam, Shlomo Argamon, Ophir Frieder, David Grossman, and Jefferson Heard. Building a test collection for complex document information processing. In Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval , pages 665-666, 2006.</li>
<li style="list-style-type: '[45] ';">Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Yanwei Li, Ziwei Liu, and Chunyuan Li. Llava-onevision: Easy visual task transfer. arXiv preprint arXiv:2408.03326 , 2024.</li>
<li style="list-style-type: '[46] ';">Feng Li, Renrui Zhang, Hao Zhang, Yuanhan Zhang, Bo Li, Wei Li, Zejun Ma, and Chunyuan Li. Llava-next-interleave: Tackling multi-image, video, and 3d in large multimodal models. arXiv preprint arXiv:2407.07895 , 2024.</li>
<li style="list-style-type: '[47] ';">Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. In International conference on machine learning , pages 19730-19742. PMLR, 2023.</li>
<li style="list-style-type: '[48] ';">Han Lin, Peng Yang, and Fanlong Zhang. Review of scene text detection and recognition. Archives of computational methods in engineering , 27(2):433-454, April 2020.</li>
<li style="list-style-type: '[49] ';">Fangyu Liu, Julian Martin Eisenschlos, Francesco Piccinno, Syrine Krichene, Chenxi Pang, Kenton Lee, Mandar Joshi, Wenhu Chen, Nigel Collier, and Yasemin Altun. Deplot: One-shot visual language reasoning by plot-to-table translation. arXiv preprint arXiv:2212.10505 , 2022.</li>
</ol>
<ol>
<li style="list-style-type: '[50] ';">Fangyu Liu, Francesco Piccinno, Syrine Krichene, Chenxi Pang, Kenton Lee, Mandar Joshi, Yasemin Altun, Nigel Collier, and Julian Martin Eisenschlos. Matcha: Enhancing visual language pretraining with math reasoning and chart derendering. arXiv preprint arXiv:2212.09662 , 2022.</li>
<li style="list-style-type: '[51] ';">Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee. Improved Baselines with Visual Instruction Tuning, 2023.</li>
<li style="list-style-type: '[52] ';">Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual Instruction Tuning. In NeurIPS , 2023.</li>
<li style="list-style-type: '[53] ';">Nikolaos Livathinos, Christoph Auer, Maksym Lysak, Ahmed Nassar, Michele Dolfi, Panos Vagenas, Cesar Berrospi Ramis, Matteo Omenetti, Kasper Dinkla, Yusik Kim, Shubham Gupta, Rafael Teixeira de Lima, Valery Weber, Lucas Morin, Ingmar Meijer, Viktor Kuropiatnyk, and Peter W. J. Staar. Docling: An Efficient Open-Source Toolkit for AI-driven Document Conversion, 2025.</li>
<li style="list-style-type: '[54] ';">Nam Ly., Atsuhiro Takasu., Phuc Nguyen., and Hideaki Takeda. Rethinking Image-Based Table Recognition Using Weakly Supervised Methods. pages 872-880, 2023.</li>
<li style="list-style-type: '[55] ';">Maksym Lysak, Ahmed Nassar, Nikolaos Livathinos, Christoph Auer, and Peter Staar. Optimized Table Tokenization for Table Structure Recognition. In Gernot A. Fink, Rajiv Jain, Koichi Kise, and Richard Zanibbi, editors, Document Analysis and Recognition - ICDAR 2023 , pages 37-50, Cham, 2023. Springer Nature Switzerland.</li>
<li style="list-style-type: '[56] ';">Andrés Marafioti, Orr Zohar, Miquel Farré, Merve Noyan, Elie Bakouch, Pedro Cuenca, Cyril Zakka, Loubna Ben Allal, Anton Lozhkov, Nouamane Tazi, Vaibhav Srivastav, Joshua Lochner, Hugo Larcher, Mathieu Morlon, Lewis Tunstall, Leandro von Werra, and Thomas Wolf. SmolVLM: Redefining small and efficient multimodal models. 2025.</li>
<li style="list-style-type: '[57] ';">Ahmed Masry, Parsa Kavehzadeh, Xuan Long Do, Enamul Hoque, and Shafiq Joty. Unichart: A universal vision-language pretrained model for chart comprehension and reasoning. arXiv preprint arXiv:2305.14761 , 2023.</li>
<li style="list-style-type: '[58] ';">Ahmed Masry, Do Xuan Long, Jia Qing Tan, Shafiq Joty, and Enamul Hoque. Chartqa: A benchmark for question answering about charts with visual and logical reasoning. arXiv preprint arXiv:2203.10244 , 2022.</li>
<li style="list-style-type: '[59] ';">Minesh Mathew, Dimosthenis Karatzas, and CV Jawahar. Docvqa: A dataset for vqa on document images. In Proceedings of the IEEE/CVF winter conference on applications of computer vision , pages 2200-2209, 2021.</li>
<li style="list-style-type: '[60] ';">Fanqing Meng, Wenqi Shao, Quanfeng Lu, Peng Gao, Kaipeng Zhang, Yu Qiao, and Ping Luo. Chartassisstant: A universal chart multimodal language model via chart-to-table pre-training and multitask instruction tuning. arXiv preprint arXiv:2401.02384 , 2024.</li>
<li style="list-style-type: '[61] ';">Nitesh Methani, Pritha Ganguly, Mitesh M. Khapra, and Pratyush Kumar. PlotQA: Reasoning over Scientific Plots. In The IEEE Winter Conference on Applications of Computer Vision (WACV) , March 2020.</li>
<li style="list-style-type: '[62] ';">Microsoft Azure. AI Document Intelligence. https://azure.microsoft.com/en-us/products/ ai-services/ai-document-intelligence , 2024. Accessed: 2024-11-11.</li>
<li style="list-style-type: '[63] ';">Lucas Morin, Martin Danelljan, Maria Isabel Agea, Ahmed Nassar, Valery Weber, Ingmar Meijer, Peter Staar, and Fisher Yu. MolGrapher: Graph-based Visual Recognition of Chemical Structures. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , pages 19552-19561, October 2023.</li>
<li style="list-style-type: '[64] ';">Lucas Morin, Valéry Weber, Gerhard Ingmar Meijer, Fisher Yu, and Peter W. J. Staar. PatCID: an open-access dataset of chemical structures in patent documents. Nature Communications , 15(1):6532, Aug 2024.</li>
<li style="list-style-type: '[65] ';">OpenAI. Hello GPT-4O. https://openai.com/index/hello-gpt-4o/ , 2024. Accessed: 2024-02-09, 2024-0211, 2024-02-12.</li>
<li style="list-style-type: '[66] ';">R OpenAI. Gpt-4 technical report. arxiv 2303.08774. View in Article , 2(5), 2023.</li>
<li style="list-style-type: '[67] ';">Vik Paruchuri. Marker: Convert PDF to Markdown Quickly with High Accuracy. https://github.com/VikParuchuri/marker, 2024.</li>
<li style="list-style-type: '[68] ';">Vik Paruchuri. Texify. https://github.com/VikParuchuri/texify , 2024. Accessed: 2024-11-11.</li>
<li style="list-style-type: '[69] ';">Zhiliang Peng, Wenhui Wang, Li Dong, Yaru Hao, Shaohan Huang, Shuming Ma, and Furu Wei. Kosmos-2: Grounding multimodal large language models to the world. arXiv preprint arXiv:2306.14824 , 2023.</li>
<li style="list-style-type: '[70] ';">Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. DocLayNet: A Large Human-Annotated Dataset for Document-Layout Segmentation. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , KDD '22, page 3743-3751, New York, NY, USA, 2022. Association for Computing Machinery.</li>
<li style="list-style-type: '[71] ';">Ruchir Puri, David Kung, Geert Janssen, Wei Zhang, Giacomo Domeniconi, Vladmir Zolotov, Julian Dolby, Jie Chen, Mihir Choudhury, Lindsey Decker, Veronika Thost, Luca Buratti, Saurabh Pujar, Shyam Ramji, Ulrich Finkler, Susan Malaika, and Frederick Reiss. CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks, 2021.</li>
<li style="list-style-type: '[72] ';">Edward O. Pyzer-Knapp, Matteo Manica, Peter Staar, Lucas Morin, Patrick Ruch, Teodoro Laino, John R. Smith, and Alessandro Curioni. Foundation models for materials discovery - current state and future directions. npj Computational Materials , 11(1):61, Mar 2025.</li>
<li style="list-style-type: '[73] ';">Yujie Qian, Jiang Guo, Zhengkai Tu, Zhening Li, Connor W. Coley, and Regina Barzilay. MolScribe: Robust Molecular Structure Recognition with Image-to-Graph Generation. Journal of Chemical Information and Modeling , 63(7):1925-1934, Apr 2023.</li>
</ol>
<ol>
<li style="list-style-type: '[74] ';">Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of machine learning research , 21(140):1-67, 2020.</li>
<li style="list-style-type: '[75] ';">Kohulan Rajan, Henning Otto Brinkhaus, M. Isabel Agea, Achim Zielesny, and Christoph Steinbeck. DECIMER.ai: an open platform for automated optical chemical structure identification, segmentation and recognition in scientific publications. Nature Communications , 14(1):5045, Aug 2023.</li>
<li style="list-style-type: '[76] ';">Johannes Rausch, Octavio Martinez, Fabian Bissig, Ce Zhang, and Stefan Feuerriegel. Docparser: Hierarchical document structure parsing from renderings. In Proceedings of the AAAI Conference on Artificial Intelligence , volume 35, pages 4328-4338, 2021.</li>
<li style="list-style-type: '[77] ';">Felix M Schmitt-Koopmann, Elaine M Huang, Hans-Peter Hutter, Thilo Stadelmann, and Alireza Darvishy. MathNet: A Data-Centric Approach for Printed Mathematical Expression Recognition. IEEE Access , 2024.</li>
<li style="list-style-type: '[78] ';">Brandon Smock, Rohith Pesala, and Robin Abraham. PubTables-1M: Towards comprehensive table extraction from unstructured documents. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages 4624-4632, 2022.</li>
<li style="list-style-type: '[79] ';">Zineng Tang, Ziyi Yang, Guoxin Wang, Yuwei Fang, Yang Liu, Chenguang Zhu, Michael Zeng, Cha Zhang, and Mohit Bansal. Unifying vision, text, and layout for universal document processing. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 19254-19264, 2023.</li>
<li style="list-style-type: '[80] ';">Gemini Team, Rohan Anil, Sebastian Borgeaud, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, Katie Millican, et al. Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312.11805 , 2023.</li>
<li style="list-style-type: '[81] ';">Granite Vision Team, Leonid Karlinsky, Assaf Arbelle, Abraham Daniels, Ahmed Nassar, Amit Alfassi, Bo Wu, Eli Schwartz, Dhiraj Joshi, Jovana Kondic, Nimrod Shabtay, Pengyuan Li, Roei Herzig, Shafiq Abedin, Shaked Perek, Sivan Harary, Udi Barzelay, Adi Raz Goldfarb, Aude Oliva, Ben Wieles, Bishwaranjan Bhattacharjee, Brandon Huang, Christoph Auer, Dan Gutfreund, David Beymer, David Wood, Hilde Kuehne, Jacob Hansen, Joseph Shtok, Ken Wong, Luis Angel Bathen, Mayank Mishra, Maksym Lysak, Michele Dolfi, Mikhail Yurochkin, Nikolaos Livathinos, Nimrod Harel, Ophir Azulai, Oshri Naparstek, Rafael Teixeira de Lima, Rameswar Panda, Sivan Doveh, Shubham Gupta, Subhro Das, Syed Zawad, Yusik Kim, Zexue He, Alexander Brooks, Gabe Goodhart, Anita Govindjee, Derek Leist, Ibrahim Ibrahim, Aya Soffer, David Cox, Kate Soule, Luis Lastras, Nirmit Desai, Shila Ofek-koifman, Sriram Raghavan, Tanveer Syeda-Mahmood, Peter Staar, Tal Drory, and Rogerio Feris. Granite Vision: a lightweight, open-source multimodal model for enterprise Intelligence, 2025.</li>
<li style="list-style-type: '[82] ';">Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 , 2023.</li>
<li style="list-style-type: '[83] ';">Michał Turski, Tomasz Stanisławek, Karol Kaczmarek, Paweł Dyda, and Filip Graliński. CCpdf: Building a High Quality Corpus for Visually Rich Documents from Web Crawl Data. In Gernot A. Fink, Rajiv Jain, Koichi Kise, and Richard Zanibbi, editors, Document Analysis and Recognition - ICDAR 2023 , pages 348-365, Cham, 2023. Springer Nature Switzerland.</li>
<li style="list-style-type: '[84] ';">Unstructured.io Team. Unstructured.io: Open-Source Pre-Processing Tools for Unstructured Data. https://unstructured.io, 2024. Accessed: 2024-11-19.</li>
<li style="list-style-type: '[85] ';">Vicuna. Vicuna: An open-source chatbot impressing GPT-4 with 90%* ChatGPT quality. https://vicuna.lmsys. org/ , 2023.</li>
<li style="list-style-type: '[86] ';">Bin Wang, Chao Xu, Xiaomeng Zhao, Linke Ouyang, Fan Wu, Zhiyuan Zhao, Rui Xu, Kaiwen Liu, Yuan Qu, Fukai Shang, Bo Zhang, Liqun Wei, Zhihao Sui, Wei Li, Botian Shi, Yu Qiao, Dahua Lin, and Conghui He. MinerU: An Open-Source Solution for Precise Document Content Extraction, 2024.</li>
<li style="list-style-type: '[87] ';">Michael L Waskom. Seaborn: statistical data visualization. Journal of Open Source Software , 6(60):3021, 2021.</li>
<li style="list-style-type: '[88] ';">Maurice Weber, Carlo Siebenschuh, Rory Butler, Anton Alexandrov, Valdemar Thanner, Georgios Tsolakis, Haris Jabbar, Ian Foster, Bo Li, Rick Stevens, et al. WordScape: a Pipeline to extract multilingual, visually rich Documents with Layout Annotations from Web Crawl Data. Advances in Neural Information Processing Systems , 36, 2024.</li>
<li style="list-style-type: '[89] ';">Haoran Wei, Chenglong Liu, Jinyue Chen, Jia Wang, Lingyu Kong, Yanming Xu, Zheng Ge, Liang Zhao, Jianjian Sun, Yuang Peng, et al. General OCR Theory: Towards OCR-2.0 via a Unified End-to-end Model. arXiv preprint arXiv:2409.01704 , 2024.</li>
<li style="list-style-type: '[90] ';">Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems , 35:24824-24837, 2022.</li>
<li style="list-style-type: '[91] ';">David Weininger. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. Journal of Chemical Information and Computer Sciences , 28(1):31-36, Feb 1988.</li>
<li style="list-style-type: '[92] ';">Wes McKinney and the Pandas Development Team. Pandas: powerful Python data analysis toolkit, Release 1.4.4. https: //pandas.pydata.org/pandas-docs/version/1.4/pandas.pdf , 2022.</li>
<li style="list-style-type: '[93] ';">Renqiu Xia, Bo Zhang, Haoyang Peng, Hancheng Ye, Xiangchao Yan, Peng Ye, Botian Shi, Junchi Yan, and Yu Qiao. StructChart: Perception, Structuring, Reasoning for Visual Chart Understanding. arXiv preprint arXiv:2309.11268 , 2023.</li>
</ol>
<ol>
<li style="list-style-type: '[94] ';">Renqiu Xia, Bo Zhang, Hancheng Ye, Xiangchao Yan, Qi Liu, Hongbin Zhou, Zijun Chen, Min Dou, Botian Shi, Junchi Yan, et al. Chartx &amp; chartvlm: A versatile benchmark and foundation model for complicated chart reasoning. arXiv preprint arXiv:2402.12185 , 2024.</li>
<li style="list-style-type: '[95] ';">Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, et al. Layoutlmv2: Multi-modal pre-training for visually-rich document understanding. arXiv preprint arXiv:2012.14740 , 2020.</li>
<li style="list-style-type: '[96] ';">Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, and Ming Zhou. Layoutlm: Pre-training of text and layout for document image understanding. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery &amp; data mining , pages 1192-1200, 2020.</li>
<li style="list-style-type: '[97] ';">Zuoyu Yan, Xiaode Zhang, Liangcai Gao, Ke Yuan, and Zhi Tang. ConvMath: a convolutional sequence network for mathematical expression recognition. In 2020 25th International Conference on Pattern Recognition (ICPR) , pages 4566-4572. IEEE, 2021.</li>
<li style="list-style-type: '[98] ';">Jiabo Ye, Anwen Hu, Haiyang Xu, Qinghao Ye, Ming Yan, Guohai Xu, Chenliang Li, Junfeng Tian, Qi Qian, Ji Zhang, et al. Ureader: Universal ocr-free visually-situated language understanding with multimodal large language model. arXiv preprint arXiv:2310.05126 , 2023.</li>
<li style="list-style-type: '[99] ';">Pan Zhang, Xiaoyi Dong, Bin Wang, Yuhang Cao, Chao Xu, Linke Ouyang, Zhiyuan Zhao, Shuangrui Ding, Songyang Zhang, Haodong Duan, Wenwei Zhang, Hang Yan, Xinyue Zhang, Wei Li, Jingwen Li, Kai Chen, Conghui He, Xingcheng Zhang, Yu Qiao, Dahua Lin, and Jiaqi Wang. InternLM-XComposer: A Vision-Language Large Model for Advanced Text-image Comprehension and Composition. arXiv preprint arXiv:2309.15112 , 2023.</li>
<li style="list-style-type: '[100] ';">Pan Zhang, Xiaoyi Dong, Yuhang Zang, Yuhang Cao, Rui Qian, Lin Chen, Qipeng Guo, Haodong Duan, Bin Wang, Linke Ouyang, Songyang Zhang, Wenwei Zhang, Yining Li, Yang Gao, Peng Sun, Xinyue Zhang, Wei Li, Jingwen Li, Wenhai Wang, Hang Yan, Conghui He, Xingcheng Zhang, Kai Chen, Jifeng Dai, Yu Qiao, Dahua Lin, and Jiaqi Wang. InternLMXComposer-2.5: A Versatile Large Vision Language Model Supporting Long-Contextual Input and Output. arXiv preprint arXiv:2407.03320 , 2024.</li>
<li style="list-style-type: '[101] ';">Qiming Zhang, Jing Zhang, Yufei Xu, and Dacheng Tao. Vision Transformer with Quadrangle Attention. arXiv preprint arXiv:2303.15105 , 2023.</li>
<li style="list-style-type: '[102] ';">Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, et al. Opt: Open pre-trained transformer language models. URL https://arxiv. org/abs/2205.01068 , 3:19-0, 2023.</li>
<li style="list-style-type: '[103] ';">Xinyi Zheng, Douglas Burdick, Lucian Popa, Xu Zhong, and Nancy Xin Ru Wang. Global table extractor (gte): A framework for joint table identification and cell structure recognition using visual context. In Proceedings of the IEEE/CVF winter conference on applications of computer vision , pages 697-706, 2021.</li>
<li style="list-style-type: '[104] ';">Xu Zhong, Elaheh ShafieiBavani, and Antonio Jimeno Yepes. Image-based table recognition: data, model, and evaluation. arXiv preprint arXiv:1911.10683 , 2019.</li>
<li style="list-style-type: '[105] ';">Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny. Minigpt-4: Enhancing vision-language understanding with advanced large language models. arXiv preprint arXiv:2304.10592 , 2023.</li>
</ol>
<p>Multi Task</p>
<p>PubTablelM</p>
<p>SynthFormulaNet</p>
<figure><figcaption><div class="caption">Figure 3: Training datasets. A treemap visualization of dataset sizes contributing to the training of SmolDocling, categorized by type. Each rectangle represents a dataset, with its area proportional to the total dataset size. Each color indicates a dataset type. &#x27;Multi-Task&quot; refers to datasets that include multiple different tasks.</div></figcaption></figure>
<h2>A Additional Datasets Details</h2>
<h2>A.1 SynthChartNet</h2>
<p>SynthChartNet includes four distinct types of charts: line, pie, bar, and stacked bar, generated using three visualization libraries: Matplotlib [30], Seaborn [87], and Pyecharts [15]. Each data set was rendered through all three libraries, producing a final dataset of 2.5 million visually diverse charts. The rendering process was designed to leverage the full range of features available in these libraries, ensuring visual variety while maintaining the plausibility of the data presented. Figure 4 depicts 10 samples from SynthChartNet.</p>
<h2>A.2 SynthCodeNet</h2>
<p>To render the code snippets of SynthCodeNet, we employed two rendering libraries: LaTeX and Pygments [16]. LaTex, in conjunction with its listings package, enabled the generation of code renderings similar to those commonly seen in academic and technical publications, while Pygments facilitated renderings with aesthetics resembling those of IDEs. The rendering process utilized the complete set of features provided by these libraries to enhance visual diversity while preserving the plausibility of the presented data. This included randomizing various stylistic parameters such as font, font size, style, weight, line spacing, tab size, background color, line numbering (including randomizing the starting line number), and programming language-specific syntax highlighting. Figure 5 shows 4 samples from SynthCodeNet.</p>
<h2>A.3 SynthFormulaNet</h2>
<p>The extracted formulas of SynthFormulaNet underwent a rigorous normalization procedure to ensure the model was trained only on correct and standardized code. The normalization process consisted of the following major steps:</p>
<ol>
<li style="list-style-type: '1. ';">Token Filtering and Removal : Unnecessary or redundant LaTeX tokens were removed or replaced based on predefined policies. This included:</li>
<li style="list-style-type: '· ';">Eliminating unwanted tokens.</li>
<li style="list-style-type: '· ';">Replacing tokens with equivalent ones for consistency.</li>
</ol>
<p>ChartQA</p>
<p>MolGrapher Synthetic 300k WordScape-PocLayNe[nichart</p>
<p>PlotQA</p>
<p>SynthDocNet</p>
<p>FinTabNet</p>
<p>600</p>
<p>500 -</p>
<p>400-</p>
<p>300 -</p>
<p>200</p>
<p>100-</p>
<p>1700</p>
<p>borond suel</p>
<p>1800</p>
<p>1900</p>
<p>Property plant and equipment -</p>
<p>Iccumulated depre..</p>
<p>Cross asset volur</p>
<p>Construction in progress in millions except per share</p>
<p>Total revenues</p>
<p>Income (loss) from continuing</p>
<p>Income from continuing</p>
<p>Net income (loss)</p>
<p>Total assets</p>
<p>Short-term debt</p>
<p>Total debt (including capital</p>
<p>Total stockholders' equity</p>
<p>2000</p>
<p>Enteray Texal</p>
<p>-** nteray New or eani</p>
<p>Enteray Hississippl</p>
<p>Interay Art.</p>
<p>2100</p>
<p>2200</p>
<p>-Ind</p>
<p>2300</p>
<p>12.31</p>
<p>band improvements</p>
<p>Duldings and building</p>
<p>- machinery and equipmen</p>
<p>13.85</p>
<p>16.92</p>
<p>Total Cash Dividends Paid</p>
<p>Toral Cash Widends Paid der Common Share</p>
<p>2011</p>
<p>2012</p>
<p>2013</p>
<p>2014</p>
<p>Total unrecognized</p>
<p>2500 5000 7500 10000 12500 15000 17500 20000</p>
<p>1Year</p>
<p>United States loYear</p>
<p>· 5Year|</p>
<p>O..................... . ... . . ... ..... ........ .... ·</p>
<p>2075</p>
<p>2100</p>
<p>2125</p>
<p>2150</p>
<p>2175</p>
<p>2200</p>
<p>2225</p>
<p>First econd</p>
<p>Third</p>
<p>7.69</p>
<figure><figcaption><div class="caption">Figure 4: Samples from SynthChartNet.</div></figcaption></figure>
<p>24.7%</p>
<p>2250</p>
<p>25.3%</p>
<p>· 3Year</p>
<p>24.4%</p>
<p>25.7%</p>
<p>16..</p>
<p>20.I</p>
<p>#pragma once</p>
<p>#include &lt;sdm/world/solvable_by_dp. hpp&gt;</p>
<p>#include &lt;sdm/utils/value_function/initializer.hpp&gt;</p>
<p>namespace sdm namespace</p>
<p>{</p>
<p>class</p>
<p>{</p>
<p>registry protected:</p>
<p>typedef std:: map&lt;std:: string, std:: shared</p>
<p>_ptr&lt;</p>
<p>world, Config config) &gt; map_type;</p>
<p>Initializer &gt; (*) (std:: shared_ptr&lt;SolvableByDP&gt;</p>
<p>static map_type container;</p>
<p>initializer sane: Stage the app</p>
<p>A20RE, NEBAT NAVE: Y0218431-T5E-APP...</p>
<p>................</p>
<p>1t: contains (gathub. event.pull_request labela.*.name, 'stage*)</p>
<p>steps:</p>
<p>- uses: actions/checkout (vl</p>
<p>- name: pm install and build webpack run:</p>
<p>npm instal.</p>
<p>- uses: actions/upload-artifactênain apm run build</p>
<p>with:</p>
<p>path: public/</p>
<p>name: webpack artifacts</p>
<p>Busld-Docker-Image:</p>
<p>runs-on: ubuntu-latest</p>
<pre><code>public: static std:: vector<std:: string > available (): static std:: shared_ptr<Initializer > make(std :: string name, std:: shared_ptr<SolvableByDP> world, Config config = {}); } :1 template <class TInitializer > std:: shared_ptr<Initializer > createlnstance (std:: shared_ptr<SolvableByDP> world, Config config) return std:: make_shared<TInitializer >(world, config): package br.com.biot. integra caopagarmeapi.modulos.jwt.utids import br.com.biot. integracaopagarmeapi.config.exception.ValidacaoExceptipn import io.jsonwebtoken.Claims import lombok.extern.slf4j.Slf4j public class JwtCampoUtil{ public static String getCampold(Claims claims) {| tryi return (String) claims.get("id"); loge to tro do tentar recuperar I do JWT: 0'; claims toString): throw new ValidacaoException("Erro ao tentar recuperar o campo ID do Token."): public static String getCampo(String campo, Claims claims) { return (String) claims.get(campo); sagetro euro de entar recuperar o campo f] do IT: 0', campo, claims toString): throw new ValidacaoException( String.format("Erro ao tentar recuperar o campo %s do Token.", campo) 29| needs: build name: Build image and store in GitHub Packages steps: - name: Checkout uses: actions/checkout iva - name: Download built artifact uses: actions/download-arts factinain path: public - name: create inage and store in Packages uses: nattdavsa0351/actions/docker-gpr01.3.0 with: repo-token: 3{ (secrets.GITHUB,TOKEN) ) runneone wountuel tent needs: Build-Docker-Image name: Deploy app container to Azure steps: - name: "Login via Azure CLI" MIRA creda: 91{ SeCretA.AZURE_CREDENTIALS 1) - uses: azure/docker-logan@va with: logan-server: #{(env. IMAGE_REGISTRY_URL)) def main (filename, days) : · array I of buckets 0-8, each representing with that counter value fish_by_counter = [O for i in range (9)] for counter in read_counters (filename): fish_by_counter [counter) += 1 the number of fish for i in range (days): fish_by_counter · spawn (fish_by_counter) print (f"After {days) days: {sum(fish_by_counter)} fish") def spawn (fish_by_counter): # rotate left res = fish_by_counter (1:) + fish_by_counter [:1] # fish that were in bucket O are now new fish in bucket 8. restore the original fish into bucket 6. res [6] += res [8] return res def read_counters (filename) : with open (filename) as £ : return [int (counter) split (',')] for counter in f. readline ().strip(). -_name.. == '__main_.': main('input.txt', 80) main ('input.txt', 256)</code></pre>
<p>kejnKm kejnKm</p>
<p>kejnKm</p>
<p>=I+II + II.</p>
<div class="formula-not-decoded">Formula not decoded</div>
<p>d ≤ 1</p>
<p>Y</p>
<p>4.30</p>
<p>4.31</p>
<p>4.32</p>
<div class="formula-not-decoded">Formula not decoded</div>
<div class="formula-not-decoded">Formula not decoded</div>
<div class="formula-not-decoded">Formula not decoded</div>
<p>(x)</p>
<div class="formula-not-decoded">Formula not decoded</div>
<div class="formula-not-decoded">Formula not decoded</div>
<p>Figure 6: Samples from SynthFormulaNet.</p>
<p>SmolDocling: An ultra-compact vision-language model for end-to-end multi-modal document conversion</p>
<ol>
<li style="list-style-type: '2. ';">Structural Normalization : The equation structure was standardized by:</li>
<li style="list-style-type: '· ';">Normalizing spacing between tokens.</li>
<li style="list-style-type: '· ';">Ensuring consistent use of tokens that require one or two braces.</li>
<li style="list-style-type: '· ';">Standardizing left and right delimiters.</li>
<li style="list-style-type: '3. ';">Token Simplification : Redundant tokens and patterns were simplified, including:</li>
<li style="list-style-type: '· ';">Collapsing consecutive redundant tokens (e.g., repeated primes, dots, or excessive spacing).</li>
<li style="list-style-type: '· ';">Converting special constructs such as \Big into \left and \right for consistency.</li>
</ol>
<p>The extracted equations were subsequently rendered using LaTex at 120 dpi. Several aspects of the rendering procedure have been randomized to ensure the visual diversity of the dataset. Each formula was rendered using a randomly selected font from a set of 85 possible options, with variations in font size, style, weight, and line spacing. Additionally, with a certain probability, each formula was enclosed within an "align" environment, allowing for the inclusion of a randomly generated equation number on either the left or right side of the equation. The equation number itself was chosen at random from a diverse set of possibilities, including small and large numbers, integers, floating-point values, or letters. Furthermore, it could be enclosed in single or double parentheses, brackets, or curly braces and was sometimes preceded by text labels such as 'Eqn.", 'Eqn", 'Eq.", or 'Equation". Figure 6 depicts 6 samples from SynthFormulaNet.</p>
<h2>A.4 DocTags vocabulary</h2>
<p>Here we provide a reference to a complete vocabulary of DocTags for SmolDocling , and their meaning.</p>
<p>We represent tags in an XML-like style of notation. Some tags have an opening and closing part, and wrap around textual content (or other tags) for example: &lt;text&gt;hello world&lt;/text&gt; . Other tags are standalone and do not have a closing tag; they mark a special instruction, for example: &lt;page_break&gt;</p>
<p>The complete DocTags snippet can represent a page or multiple pages, wrapped into &lt;doctag&gt;...&lt;/doctag&gt; . When content represents multiple pages, we use &lt;page_break&gt; tag as a separator.</p>
<p>Within the scope of &lt;doctag&gt; we place high-level, tags that wrap around the textual content of certain document blocks and identify the type of the block. These tags are: &lt;text&gt; , &lt;caption&gt; , &lt;footnote&gt; , &lt;formula&gt; , &lt;title&gt; , &lt;page_footer&gt; , &lt;page_header&gt; , &lt;picture&gt; , &lt;section_header&gt; , &lt;document_index&gt; , &lt;code&gt; , &lt;otsl&gt; , &lt;list_item&gt; , &lt;ordered_list&gt; , &lt;unordered_list&gt;</p>
<p>Each element can nest additional standalone location tags that encode its position on the page as a bounding box, represented in DocTags as &lt;loc_ x1 &gt;&lt;loc_ y1 &gt;&lt;loc_ x2 &gt;&lt;loc_ y2 &gt; . Every x,y pair of coordinates belongs to a fixed grid. In our case we used integer values in the range from 0 to 500 which proportionally maps to width and height of the page.</p>
<p>&lt;otsl&gt; element contains table representation with following OTSL tags, that are standalone and used to describe structure of a table: &lt;fcel&gt; , &lt;ecel&gt; , &lt;lcel&gt; , &lt;ucel&gt; , &lt;xcel&gt; , &lt;nl&gt; . These tags follow rules as described in [55], we additionally differentiate OTSL notion of a cell into &lt;fcel&gt; - full cell, or cell which contain content, and &lt;ecel&gt; - empty cell. We also augment tabular structure information with extra tags &lt;ched&gt; , &lt;rhed&gt; , &lt;srow&gt; that replace &lt;fcel&gt; to describe cells which belong to column and row headers of the table, as well as table section accordingly. We mark such headers when ground truth about table headers from the underlying dataset is available.</p>
<p>&lt;list_item&gt; elements are placed within &lt;ordered_list&gt; or &lt;unordered_list&gt; and define weather it's enumerated (ordered) list or not.</p>
<p>&lt;picture&gt; and &lt;otsl&gt; elements by themselves can encapsulate &lt;caption&gt; tag if appropriate picture or table has it's own caption. This way we can connect extra descriptive information to illustrations and tables in the document.</p>
<p>&lt;code&gt; elements contain pieces of code as a content, and as such, respect tabulation and line breaks. Code elements also contains special standalone classification tag -&lt;_ programming-language _&gt; where programminglanguage is an appropriate programming language name. Supported values (57 in total) for programming language names are: Ada , Awk , Bash , bc , C , C# , C++ , CMake , COBOL , CSS , Ceylon , Clojure , Crystal , Cuda , Cython , D , Dart , dc , Dockerfile , Elixir , Erlang , FORTRAN , Forth , Go , HTML , Haskell , Haxe , Java , JavaScript , Julia , Kotlin , Lisp , Lua , Matlab , MoonScript , Nim , OCaml , ObjectiveC , Octave , PHP , Pascal , Perl , Prolog , Python , Racket , Ruby , Rust , SML , SQL , Scala , Scheme , Swift , TypeScript , unknown , VisualBasic , XML , YAML</p>
<p>Within &lt;picture&gt; elements we include picture classification with extra standalone tags &lt; image_class&gt; . We classify images from our datasets into following categories: natural_image , pie_chart , bar_chart , line_chart ,</p>
<p>flow_chart , scatter_chart , heatmap , remote_sensing , chemistry_molecular_structure , chemistry_markush_structure , icon , logo , signature , stamp , qr_code , bar_code , screenshot , map , stratigraphic_chart , cad_drawing , electrical_diagram</p>
<h2>B Additional Details about the SmolDocling</h2>
<table><caption><div class="caption">Table 6: Supported Conversion Instructions.</div></caption><tbody><tr><th>Instruction</th><th>Description</th></tr><tr><td>Full conversion</td><td>Convert this page to docling.</td></tr><tr><td>Chart</td><td>Convert chart to table (e.g., &lt;chart&gt; ).</td></tr><tr><td>Formula</td><td>Convert formula to LaTeX (e.g., &lt;formula&gt; ).</td></tr><tr><td>Code</td><td>Convert code to text (e.g., &lt;code&gt; ).</td></tr><tr><td>Table</td><td>Convert table to OTSL (e.g., &lt;otsl&gt; ).</td></tr><tr><td rowspan="4">No-Code Ac- tions/Pipelines</td><td>OCR the text in a specific location: &lt;loc_155&gt;&lt;loc_233&gt;&lt;loc_206&gt;&lt;loc_237&gt;</td></tr><tr><td>Identify element at: &lt;loc_247&gt;&lt;loc_482&gt;&lt;loc_252&gt;&lt;loc_486&gt;</td></tr><tr><td>Find all &#x27;text&#x27; elements on the page, retrieve all section headers.</td></tr><tr><td>Detect footer elements on the page.</td></tr></tbody></table>
<p>Input Image</p>
<p>OH</p>
<p>Cl</p>
<p>· 7: Qualitative comparison for molecule image recognition. Predictions are shown for GOT-OCR</p>
<p>2.5-VL-72B and SmolDocling on a simple molecule image.</p>
<p>GOT-OCR 2.0</p>
<p>NH,</p>
<figure><figcaption><div class="caption">Figure 7: Qualitative comparison for molecule image recognition. Predictions are shown for GOT-OCR 2.0, Qwen2.5-VL-72B and SmolDocling on a simple molecule image.</div></figcaption></figure>
<h2>C Additional Qualitative Analysis</h2>
<h2>C.1 Molecule Image Recognition Experiment</h2>
<p>Extracting molecule images from documents has potential to accelerate research in chemistry and materials discovery [72]. Some recent document understanding models, such as GOT-OCR 2.0 [89] and Qwen2.5-VL [9], claim to perform molecule image recognition in documents. Following this direction, we experimented with training SmolDocling for molecule image recognition on full document pages and cropped images. In order to predict molecule structures (graphs) with SmolDocling, we use the molecule string identifier named SMILES [91].</p>
<p>Datasets. For training on cropped images, we use the MolGrapher-Synthetic-300K dataset [63]. The dataset contains 300K synthetic samples. It is created using real chemical-structures retrieved from the database PubChem [36] which are then synthetically drawn using the library RDKit [40]. For training of full pages, we use Doc2Mol-80K, a subset of the PatCID dataset [64]. Our subset contains 80 000 real patent documents published in the United-States between 2022 and 2024.</p>
<p>Evaluation. Figure 7 shows an example of predicted molecules for a simple molecule image. While some models capture high-level molecular features, they consistently fail to reconstruct its detailed structure. Notably, this molecule is trivial for specialized models such as MolGrapher [63], MolScribe [73] or DECIMER [75], which accurately recognize its structure. Reducing the gap between general document understanding models and specialized models may require incorporating a specialized vocabulary for SMILES tokens, explicitly encoding atoms and bonds, and extending the SMILES sequence with localization tokens for each atom.</p>
<h2>C.2 Layout analysis samples</h2>
<p>To illustrate and compare the visual grounding capabilities of SmolDocling, a set of sample predictions with SmolDocling and Qwen2.5-VL from DocLayNet [70] is shown in Table 7. Note that the element location results are independent from the correct reproduction of document content and structure.</p>
<p>Ground-Truth</p>
<p>46"</p>
<p>peR, heath and safety</p>
<p>Final gonn so yoR and convertible loan</p>
<p>tor save did exit) in the Opoon promp pur puse the Enter key:</p>
<p>trollowing display appears.</p>
<p>apposoices, tress later, yange Sample form nurter 3 in the Tet "description' prompt.</p>
<p>Hollowing display appears</p>
<p>Tyle doices, tress toler</p>
<p>Create earlay..</p>
<p>Moneyle Actior: Press the Enter key.</p>
<p>310.000,000 pal during the 2015</p>
<p>Sign untain Change in matters suusequent to</p>
<p>the end of the financial year</p>
<p>40</p>
<p>SmolDocling</p>
<p>2wen2.5-VL</p>
<p>Table 7: Visualizations of layout output from SmolDocling and QwenVL2.5 compared to the DocLayNet ground truth. Examples are chosen to be representative of different layout styles and features. The prediction results however do not represent a generalizable measure of the model's performance on inputs with similar features. (1) Multi-column pages are handled by SmolDocling and Qwen2.5-VL, with some recall errors in the latter. (2) A manual page with terminal output shows poor bounding box recall on SmolDocling and label confusion in Qwen2.5-VL. (3) Lists with nesting are handled well in SmolDocling but confuse Qwen2.5-VL. (4) Both SmolDocling and Qwen2.5-VL reconstruct equations well, however with different annotation conventions on including or excluding the equation index. (5) On a portrait page with colorful elements and gradient background, SmolDocling creates less accurate bounding-boxes and Qwen2.5-VL suffers low recall. (6) On a report page with tables and diagrams, SmolDocling output exhibits some repetition loop, fabricating non-existent text cells (bottom left), while Qwen2.5-VL is confused between tables and pictures. Win following, display appears we want yourse</p>
<p>Mum: F4 To 11S</p>
<p>Ground-Truth</p>
<p>301 Workers engarod in cential oe emeriency works pass ntial or emergency works" includes-</p>
<p>gunmpletions, maintenance, repairs, and asset replacement activities.</p>
<p>Quality</p>
<p>*/ Supie (England) Regularions 20164), the Water Supply Nu Qual</p>
<p>Low crane Ixcince tacans the looser of a seucrare Ixcice under sochon 178% of tho</p>
<p>Water Industry Act 1991(e):</p>
<p>we crape services" has the meaning givee in sction 2191) of the Water Industry Actl</p>
<p>48/ We industy ice 19 lar the mating given in seties 17ACT) and 2I9C) of the nak managemcat on bchall of</p>
<p>pay one nvironment Agency, or)</p>
<p>tal tood dinory to three.</p>
<p>Cle the purposes of sub-paragraph l wyneed and "coastal crosion" have the meanings given in section I of the Plood and</p>
<p>Water Management Act 20100h);</p>
<p>pg/w9, management" has the meaning gives in section 3 of that Acci.</p>
<p>229,n29 1) Workers engaged in essential or emergency works- we we stricity interccenector,</p>
<p>regree trict heat network as delined in regulation 2 of the Heat Network (Meicnng and</p>
<p>Billing) Regulations 2014(j).</p>
<p>Billing) Regulations 2014.</p>
<p>put mao ommissicaling, maintenance and repair of industrial machinery for use on al</p>
<p>Рд (1 4/3130. There as Do trialt areading envie:</p>
<p>I canations of the saddle point are thie to lowing)</p>
<p>(11))</p>
<p>Mll9. C is a Lagrange multiplier enforcing the constraint (9). These equations are valid on the support of p. We can prove that these equations lead to a singular support for p. We</p>
<p>do this by contradiction.</p>
<p>woRd then that</p>
<p>397199e first two terms obviously are cancelled. Notice that</p>
<p>3'iMat away from 2' - O we have that</p>
<p>91a + 03) - Pª (log(=*) + log(=*)) - 0|</p>
<p>peese the function is harmonic: a sum of i hodonsorplise plus an antiliolomorphic plece:</p>
<p>RI, is is casy to show that (F-) log(a - 6) o d°(a - 6). With these, we would find that p = 0. This argument contradicts that fp = 1, so the assumption that p is smooth does</p>
<p>not work. This argument has boen given before in (5, 23), and we are presenting it bere for completeness</p>
<p>siddhat this indicates is that the demity distribution of narticks le sinoular. Since tot</p>
<p>9 = 0 the original probability distribution is spherically symmetric, it is natural to assume that the associated distribution is spherically summetric. The radius was computed in 118</p>
<p>Pitchas also been chocked macrically in 6. 7. A geocralization to all Calabi- Yau conc conforinal field tboory singularities for this result can be found in (23), where it was seen that</p>
<p>this procedure matches vurious otber supergravity calculations in the abeence of symmetry.</p>
<p>anger q &gt; 0 but very small, we expect that the distribution does not change much, except in the region :' ≥ 0. Thus we expoct that the distribution gives a five dimensional manifold of</p>
<p>R°, possibly with a boundary, and that the region bear 2' = O has no density of eigenvalues present. After all, the effoctive potential repels all particles from the locus :' = 0. This</p>
<p>smolDocling</p>
<p>Y/I"Woekers engaged in essential or emergensy works.</p>
<p>K/""ential or emergency works" inclades</p>
<p>Greedons, maintenance, repairs, and asset replacement activities,</p>
<p>2016(a).</p>
<p>Repain 2 a. he lies Super it Quie)</p>
<p>Supply (Water</p>
<p>Quality</p>
<p>Regulations 2016(b), the Private Water Supplies (Wales) Regulations 2017(c), or the</p>
<p>Qwen2.5-VL</p>
<p>21. (1) Workers encaned in cascatial or emergency works—</p>
<p>"O) "emal or emergency works" includes-</p>
<p>0) mspoctos, mamienance, repam, and asset replacement actioes and analysis of walcr</p>
<p>Watch</p>
<p>Regulations 2016(b), the Private Water Supplies (Wales) Regulations 2017(c), or the</p>
<p>2016(a).</p>
<p>Supply (Wa</p>
<p>Table 7: Visualizations of layout output from SmolDocling and QwenVL2.5 compared to the DocLayNet ground truth. Examples are chosen to be representative of different layout styles and features. The prediction results however do not represent a generalizable measure of the model's performance on inputs with similar features. (1) Multi-column pages are handled by SmolDocling and Qwen2.5-VL, with some recall errors in the latter. (2) A manual page with terminal output shows poor bounding box recall on SmolDocling and label confusion in Qwen2.5-VL. (3) Lists with nesting are handled well in SmolDocling but confuse Qwen2.5-VL. (4) Both SmolDocling and Qwen2.5-VL reconstruct equations well, however with different annotation conventions on including or excluding the equation index. (5) On a portrait page with colorful elements and gradient background, SmolDocling creates less accurate bounding-boxes and Qwen2.5-VL suffers low recall. (6) On a report page with tables and diagrams, SmolDocling output exhibits some repetition loop, fabricating non-existent text cells (bottom left), while Qwen2.5-VL is confused between tables and pictures. "Wille C is a Lagrange multiplier enforcing the constraint (9). These equations are valid on tbe suppost of p. We can prove that these equations lead to a singular support for p. We do this by contradiction.</p>
<p>do this by contradiction.</p>
<p>DEVOTION TO SERVICE</p>
<p>Net incorre heinrat let tormente</p>
<p>Total astets</p>
<p>Working capital</p>
<p>Shareholders equity</p>
<p>Common shares outstanding</p>
<p>$1171</p>
<p>$ 875.228</p>
<p>$ 265,375</p>
<p>$ 436,616</p>
<p>15.971</p>
<p>38,104</p>
<p>$ 1.197,188</p>
<p>$ 295,648</p>
<p>$ 451.169</p>
<p>36.520</p>
<p>Giving back to the and where our</p>
<p>employees live, work, and care for their</p>
<p>families is a serious and dedicated</p>
<p>commitment.</p>
<p>FINANCIAL HIGHLIGHTS</p>
<p>VISA's ongoing commitment to investing in core products and focus on managing costs helped generate a strong finish in 2011.</p>
<p>CROCURR TUAL SALES</p>
<p>BY CORE PRODUCT GROUP</p>
<p>© Fund Gin A Fume Detectior</p>
<p>· Poutis Car Outection</p>
<p>@ Other hodets</p>
<p>60152</p>
<p>1.91</p>
<p>S1015092</p>
<p>$ 287,079</p>
<p>$ 433,666</p>
<p>36,493</p>
<p>DEVOTION TO SERVICE</p>
<p>"Giving back to the and where our</p>
<p>-employees live, work, and care for their</p>
<p>families is a serious</p>
<p>Giving back to the and where our</p>
<p>employees live, work, and care for their</p>
<p>families is a serious</p>
<p>Table 7: Visualizations of layout output from SmolDocling and QwenVL2.5 compared to the DocLayNet ground truth. Examples are chosen to be representative of different layout styles and features. The prediction results however do not represent a generalizable measure of the model's performance on inputs with similar features. (1) Multi-column pages are handled by SmolDocling and Qwen2.5-VL, with some recall errors in the latter. (2) A manual page with terminal output shows poor bounding box recall on SmolDocling and label confusion in Qwen2.5-VL. (3) Lists with nesting are handled well in SmolDocling but confuse Qwen2.5-VL. (4) Both SmolDocling and Qwen2.5-VL reconstruct equations well, however with different annotation conventions on including or excluding the equation index. (5) On a portrait page with colorful elements and gradient background, SmolDocling creates less accurate bounding-boxes and Qwen2.5-VL suffers low recall. (6) On a report page with tables and diagrams, SmolDocling output exhibits some repetition loop, fabricating non-existent text cells (bottom left), while Qwen2.5-VL is confused between tables and pictures.</p>
<p>340.9</p>
<p>DEVOTION TO SERVICE</p>
<p>22%</p>
</div>
</body>
</html>

pages_json:
[{"page_number": 1, "width": 612.0, "height": 792.0, "elements": [{"type": "section_header", "bbox": [75.333, 90.88667499999997, 536.6712892000002, 134.60335780000003], "content": "SmolDocling: An ultra-compact vision-language model for end-to-end multi-modal document conversion", "level": 1}, {"type": "text", "bbox": [96.914, 186.28416659999993, 517.5758744, 215.79938820000007], "content": "Ahmed Nassar 1 , Andres Marafioti 2 , Matteo Omenetti 1 , Maksym Lysak 1 , Nikolaos Livathinos 1 , Christoph Auer 1 , Lucas Morin 1 , Rafael Teixeira de Lima 1 , Yusik Kim 1 , A. Said Gurbuz 1 , Michele Dolfi 1 , Miquel Farr\u00e9 2 , Peter W. J. Staar 1", "level": 1}, {"type": "text", "bbox": [245.00899999999982, 223.87747500000012, 366.49281699999983, 237.8993882000001], "content": "IBM Research 1 , HuggingFace 2", "level": 1}, {"type": "text", "bbox": [153.43199999999982, 251.5261008, 458.5675583999997, 271.39638820000005], "content": "https://huggingface.co/ds4sd/SmolDocling-256M-preview Version 1.0, March 14 th 2025", "level": 1}, {"type": "section_header", "bbox": [278.3899999999999, 315.2505632, 333.6110687999999, 320.797776], "content": "Abstract", "level": 1}, {"type": "text", "bbox": [107.387, 325.918075, 505.8226149056, 481.5043882], "content": "Weintroduce SmolDocling, an ultra-compact vision-language model targeting end-to-end document conversion. Our model comprehensively processes entire pages by generating DocTags, a new universal markup format that captures all page elements in their full context with location. Unlike existing approaches that rely on large foundational models, or ensemble solutions that rely on handcrafted pipelines of multiple specialized models, SmolDocling offers an end-to-end conversion for accurately capturing content, structure and spatial location of document elements in a 256M parameters vision-language model. SmolDocling exhibits robust performance in correctly reproducing document features such as code listings, tables, equations, charts, lists, and more across a diverse range of document types including business documents, academic papers, technical reports, patents, and forms - significantly extending beyond the commonly observed focus on scientific papers. Additionally, we contribute novel publicly sourced datasets for charts, tables, equations, and code recognition. Experimental results demonstrate that SmolDocling competes with other Vision Language Models that are up to 27 times larger in size, while reducing computational requirements substantially. The model is currently available, datasets will be publicly available soon.", "level": 1}, {"type": "section_header", "bbox": [72.0, 501.1055632, 159.21318400000004, 506.652776], "content": "1 Introduction", "level": 1}, {"type": "text", "bbox": [72.0, 517.858075, 541.0973397709998, 629.8083882], "content": "For decades, converting complex digital documents into a structured, machine-processable format has been a significant technical challenge. This challenge primarily stems from the substantial variability in document layouts and styles, as well as the inherently opaque nature of the widely used PDF format, which is optimized for printing rather than semantic parsing. Intricate layout styles and visually challenging elements such as forms, tables, and complex charts can significantly impact the reading order and general understanding of documents. These problems have driven extensive research and development across multiple domains of computer science. On one hand, sophisticated ensemble systems emerged, which decompose the conversion problem into several sub-tasks (e.g. OCR, layout analysis, table structure recognition, classification) and tackle each sub-task independently. Although such systems can achieve high-quality results for many document types while maintaining relatively low computational demands, they are often difficult to tune and generalize.", "level": 1}, {"type": "text", "bbox": [72.0, 632.428075, 541.6859401416003, 722.5603882], "content": "On the other hand, in the recent past, great interest has developed around large foundational multimodal models that can solve the whole conversion task in one shot, while simultaneously offering flexible querying and parametrization through prompts. This approach has been made possible by the advent of multimodal pre-training of large visionlanguage models (LVLMs), which opened up a vast array of opportunities for leveraging diverse data sources, including PDF documents. However, the literature on this topic highlights a significant gap in the availability of high-quality and open-access datasets suitable for training robust multi-modal models for the task of document understanding. Furthermore, relying on LVLMs may introduce common issues associated with such models, including hallucinations and the use of significant computational resources, making them impractical from both a quality and cost perspective.", "level": 1}]}, {"page_number": 2, "width": 612.0, "height": 792.0, "elements": [{"type": "picture", "bbox": [98.08104705810547, 71.2328872680664, 512.7471923828125, 213.59715270996094], "content": "", "level": 1}, {"type": "text", "bbox": [72.0, 233.74307499999998, 541.538533512, 280.2383882], "content": "Figure 1: SmolDocling/SmolVLM architecture. SmolDocling converts images of document pages to DocTags sequences. First, input images are encoded using a vision encoder and reshaped via projection and pooling. Then, the projected embeddings are concatenated with the text embeddings of the user prompt, possibly with interleaving. Finally, the sequence is used by an LLM to autoregressively predict the DocTags sequence.", "level": 2}, {"type": "text", "bbox": [72.0, 298.831075, 541.5339365599999, 334.4173882], "content": "In this work, we outline how we close the gaps left by publicly available datasets and establish a training approach to achieve end-to-end, full-featured document conversion through a vision-language model, which we name SmolDocling . Our main contributions can be summarized as follows:", "level": 1}, {"type": "list", "bbox": [99.387, 342.095075, 540.0046143088001, 410.40938819999997], "content": "SmolDocling: An ultra-compact VLM for end-to-end document conversion, based on Hugging Face's most recent SmolVLM-256M [56], which is between 5 and 10 times smaller in parameters than comparable VLMs tuned on document understanding tasks. Hence, we reduce the computational complexity to roughly the same order of magnitude as typical representatives of ensemble methods. Uniquely, SmolDocling learns and produces a unified document representation that captures content, structure and spatial location of all elements in a page.", "level": 2}, {"type": "list", "bbox": [99.387, 411.636075, 541.0976111547999, 469.0413882], "content": "We augment existing document pre-training datasets with additional feature annotations, and create new datasets for tasks with insufficient coverage in open multi-modal training datasets, including code listings, equations, and chart understanding. Importantly, we construct full-page ground truth data that combines key annotation features such as layout, table structure, code listings, charts, and formulas in their natural context, and derive instruction datasets from all training datasets used.", "level": 2}, {"type": "list", "bbox": [99.387, 470.269075, 540.0018592584, 494.9463882], "content": "We introduce DocTags , a document markup format optimized to efficiently represent the full content and layout characteristics of a document, inspired by [55].", "level": 2}, {"type": "list", "bbox": [99.387, 496.174075, 539.9973633439998, 520.8513882], "content": "We evaluate the achieved prediction quality of SmolDocling on several tasks, including comparisons to popular community models.", "level": 2}, {"type": "section_header", "bbox": [72.0, 540.8875631999999, 164.712576, 546.434776], "content": "2 Related Work", "level": 1}, {"type": "section_header", "bbox": [72.0, 564.6671666, 273.66294919999996, 569.289813], "content": "2.1 Large Vision-Language Models (LVLMs)", "level": 1}, {"type": "text", "bbox": [71.522, 577.883075, 541.6807994399998, 722.5603882], "content": "With the advent of large language models (LLMs) [74, 66, 82, 32, 90, 4, 8, 25, 2] characterized by their powerful contextual understanding and adaptability, several approaches have emerged to extend them into large visionlanguage models. Some proprietary, closed-source LLMs have been adapted into multimodal large language models (MLLMs), such as GPT-4o [65], Gemini [80], and Claude 3.5 [5], demonstrating exceptional capabilities across various modalities, including vision. Open-source methods are actively advancing to match the capabilities of proprietary MLLMs. BLIP-2 [47] is one of the earliest works combining a vision encoder with a frozen LLM (OPT [102] or FlanT5 [14]) using a lightweight transformer (Q-former [101]). Building upon BLIP-2, MiniGPT-4 [105] integrated a frozen visual encoder with a frozen LLM (Vicuna [85]) using a Q-Former network and a single projection layer. LLaVA [52, 51] employs a similar architecture but uses a minimal adapter layer. LLaVA-OneVision [45] and LLaVA-NeXTInterleave [46] build upon LLaVA to support multi-image, higher resolution, and video understanding. For handling high resolutions and text-image compositions and comprehension, InternLM-XComposer introduces a family of models [99, 100, 20, 19] designed specifically for these tasks. Qwen-VL [8] introduces a position-aware adapter to address efficiency issues caused by the vision encoder generating long image feature sequences. Qwen2.5-VL [9]", "level": 1}]}, {"page_number": 3, "width": 612.0, "height": 792.0, "elements": [{"type": "text", "bbox": [72.0, 70.75507499999992, 540.2538960923998, 95.43238819999999], "content": "employs windowed attention with 2D rotary positional embeddings to process native-resolution inputs efficiently and integrates an vision-language merger for dynamic feature compression.", "level": 1}, {"type": "text", "bbox": [71.751, 98.05207499999995, 541.0914120239996, 155.45738820000008], "content": "In this work, we build upon the SmolVLM architecture approach [56]. Precisely, we adopt SmolVLM-256M, a compact variant of base SmolVLM-2.2B model, inspired by the architecture of Idefics3 [41] which employs the shape-optimized SigLIP encoder as its visual backbone. It applies an aggressive pixel shuffle strategy to compress visual features, reduces the number of image hidden states and introduces new special tokens to separate sub-images and improve tokenization efficiency.", "level": 1}, {"type": "section_header", "bbox": [72.0, 178.95116659999997, 294.42500759999996, 183.57381299999997], "content": "2.2 State of the Art in Document Understanding", "level": 1}, {"type": "text", "bbox": [72.0, 194.742075, 540.0030975999999, 263.0563882], "content": "Several powerful document understanding solutions have emerged on the market in the past decade as commercial cloud offerings on hyperscalers [24, 62, 3, 6], frontier models such as GPT-4o [65] or Claude [5], or as open-source libraries such as Docling [53], Grobid [1], Marker [67], MinerU [86] or Unstructured [84]. Typical tasks that document understanding encompasses are document classification [96, 29], OCR [48], layout analysis [95, 70], table recognition [103, 55, 78], key-value extraction [29, 35, 31], chart understanding [49, 57, 50, 26, 60, 93, 94, 13, 58, 33, 34], equation recognition [77, 68, 18, 97, 23], and more.", "level": 1}, {"type": "text", "bbox": [71.631, 265.67607499999997, 541.6807994399999, 323.08038819999996], "content": "Ensemble systems, such as the aforementioned open-source libraries [53, 1, 67, 86], implement a pipeline in source code, which conditionally applies specialized, single-task models on a given input document, composing the prediction results into a meaningful document representation. Typically, each task entails human-crafted handling of preprocessing and post-processing logic (i.e. setting confidence thresholds for layout detections, matching layout elements to text cells, conditionally applying models).", "level": 1}, {"type": "text", "bbox": [72.0, 325.701075, 541.6814887280002, 437.6513882], "content": "Conversely, multi-task models aim at providing a single model capable of handling various document-understanding related tasks simultaneously, benefiting from shared context and shared representations learned across these different tasks. OCR-reliant methods, such as LayoutLM [96, 95, 29] and UDOP [79], use text extracted from an external OCR engine, along with image and text bounding box locations, as input. In contrast, OCR-free methods such as Donut [35], Dessurt [17], DocParser [76], Pix2Struct [43] are transformer-based models trained end-to-end to take an image as input and directly output text. Large Vision Language Models (LVLMs) such as LLaVA [51], LLaVAOneVision [45], UReader [98], Kosmos-2 [69], and Qwen-VL [8] all leverage various vision encoders, projection adapters, and LLMs. While they may use different image patching techniques and instruction-tuning datasets to handle a range of vision tasks, they commonly evaluate their document understanding capabilities on datasets like DocVQA [59] and mPLUG-DocOwl 1.5 datasets [27], primarily focusing on question answering and reasoning.", "level": 1}, {"type": "text", "bbox": [72.0, 440.271075, 541.0914120239995, 584.9483882], "content": "Our work focuses primarily on document understanding tasks related to conversion and structural recognition, aiming to represent documents with high accuracy and completeness. The closest works to our approach are Nougat [12], DocOwl 2 [28], GOT [89], and Qwen2.5-VL [9]. DocOwl 2 and GOT are dedicated to precise conversion and recognition of document structures. On one hand, DocOwl 2 employs a dynamic shape-adaptive cropping module to process high-resolution images, feeding them into a ViT vision encoder. A dedicated module compresses the processed visual features before feeding them into a LLaMa-based LLM. On the other hand, GOT focuses on converting diverse elements-such as text, formulas, molecular diagrams, tables, charts, sheet music, and geometric shapes-into structured formats. It achieves a lightweight architecture by utilizing a SAM vision encoder [37], a linear projection layer, and a Qwen-0.5B LLM. Building on these approaches, our work not only extends the range of tasks but also broadens the types of documents handled, moving beyond a strictly scientific focus to support a more diverse array of document formats. In parallel, Qwen2.5-VL introduces the Omni-Parsing strategy, that integrates multiple document elements into a unified HTML-based representation. This format encodes layout bounding box information and reading order for various document elements such as paragraphs and charts.", "level": 1}, {"type": "section_header", "bbox": [72.0, 611.4215632, 159.64357120000003, 616.9687759999999], "content": "3 SmolDocling", "level": 1}, {"type": "text", "bbox": [71.701, 632.428075, 541.1003017568001, 722.5603882], "content": "Building on Hugging Face's SmolVLM [56], SmolDocling converts document page images into sequences representing their content and structure. It produces a unified page representation (DocTags markup) that is optimized for the LLM backbone and interpretable by Docling [53]. This new DocTags format covers the reproduction of captions, charts, forms, code, equations, tables, footnotes, lists, page footers and headers, section headings, and text. SmolDocling both recognizes the type and location of these elements as well as performs OCR within them. Our approach enables full-page conversion with reading-order and hierarchical linking between elements (e.g., linking captions with figures and tables), providing bounding boxes for each component, supporting isolated predictions on individual elements (e.g., cropped tables) and grouping for lists and nested lists.", "level": 1}]}, {"page_number": 4, "width": 612.0, "height": 792.0, "elements": [{"type": "text", "bbox": [36.100292821220854, 18.980167062505075, 163.5130835188953, 23.341463272764145], "content": "Fig. 3. OTSL description of table structure: A - table example; B - graphical repre- sentation of table structure; C - mapping structure on a grid; D - OTSL structure", "level": 1}, {"type": "text", "bbox": [36.10029229918238, 23.308976990619044, 164.2209318340661, 27.637787011495902], "content": "Fig. 3. OTSL description of table structure: A - table example; B - graphical repre- sentation of table structure; C - mapping structure on a grid; D - OTSL structure", "level": 1}, {"type": "text", "bbox": [123.87354733573558, 48.09756119613826, 145.10901118131864, 53.04878043231702], "content": "ucccc c c", "level": 1}, {"type": "text", "bbox": [128.12063964106105, 52.278705620740766, 136.61482568757268, 56.607515641617624], "content": "ucccc c c", "level": 1}, {"type": "text", "bbox": [135.9069765908188, 52.27870570326104, 145.10901147453976, 56.6075157241379], "content": "c c", "level": 1}, {"type": "text", "bbox": [109.71657024180797, 66.48780495487813, 141.56976537483456, 68.60975605609758], "content": "2 - horizonta merces. C.", "level": 1}, {"type": "text", "bbox": [110.42441812095446, 69.26096036270587, 139.44622044653588, 72.92379961114011], "content": "I - vertical merges: \"C\", U", "level": 1}, {"type": "text", "bbox": [54.50436035893895, 70.59290190799766, 62.290697568241285, 75.68292660873976], "content": "\u00b7 c", "level": 1}, {"type": "text", "bbox": [26.89825347805241, 86.90918586552834, 167.05232324549425, 90.57202511396258], "content": "Table 2. TSR and cell detection results compared between OTSL and HTML on the PubTabNet (22], FinTabNet [21] and PubTables-IM [14] data sets using Table-", "level": 1}, {"type": "text", "bbox": [26.89825755703121, 91.23799575441467, 166.34447848726379, 95.56680577529153], "content": "Table 2. TSR and cell detection results compared between OTSL and HTML on the PubTabNet (22], FinTabNet [21] and PubTables-IM [14] data sets using Table-", "level": 1}, {"type": "text", "bbox": [26.89825668106829, 95.48780497865857, 96.26744272757992, 99.89561589165692], "content": "Former [9) (with enc=6, dec=6, heads=8).", "level": 1}, {"type": "text", "bbox": [37.51598861984011, 107.51219518516268, 52.38081420123546, 111.04878035386173], "content": "Data set", "level": 1}, {"type": "text", "bbox": [58.75145322882752, 106.80487809634155, 75.73982532185077, 111.04878029878046], "content": "Language", "level": 1}, {"type": "text", "bbox": [34.684593965338316, 117.41463433536592, 54.50436013650276, 121.65853653780482], "content": "PubTabNet", "level": 1}, {"type": "text", "bbox": [35.39244155373063, 128.02439037784552, 53.79651132117249, 131.56097554654468], "content": "FinTabNet", "level": 1}, {"type": "text", "bbox": [32.56104692453972, 138.63414640467477, 57.3357562268653, 142.17073157337393], "content": "Pub'Tables-1M", "level": 1}, {"type": "text", "bbox": [62.29069786317828, 115.29268301361799, 73.61627925852713, 118.82926818231704], "content": "OTSL", "level": 1}, {"type": "text", "bbox": [61.582848686791436, 120.95121963729684, 73.61627891934958, 124.48780480599589], "content": "HTML", "level": 1}, {"type": "text", "bbox": [62.29069780714025, 125.90243909644312, 72.90843036527978, 129.43902426514228], "content": "OTSL", "level": 1}, {"type": "text", "bbox": [61.58284866024709, 130.8536585841464, 73.61627889280523, 135.0975607865853], "content": "HTML", "level": 1}, {"type": "text", "bbox": [62.29069780714025, 136.5121951940041, 72.90843036527978, 140.04878036270327], "content": "OTSL", "level": 1}, {"type": "text", "bbox": [61.582849037766465, 141.46341472093502, 73.6162792703246, 144.99999988963418], "content": "HTMLI", "level": 1}, {"type": "text", "bbox": [26.89825579625727, 156.3170733135163, 35.392441842768896, 159.85365848221545], "content": "5.3", "level": 1}, {"type": "text", "bbox": [25.48255898895346, 156.17014605526674, 76.44767526802322, 162.8298537796926], "content": "15.3 Qualitative Results", "level": 1}, {"type": "text", "bbox": [23.35901285956393, 175.41463444764236, 146.52471053398253, 186.80480158840396], "content": "The OTS: vocabulary is comprised of the following tokens.", "level": 1}, {"type": "text", "bbox": [28.313955196057375, 186.02439035538623, 164.92878077745272, 191.13361169336758], "content": "\"C\" cell - a new table cell that either has or does not have cell content", "level": 1}, {"type": "text", "bbox": [28.313951069888656, 191.6829269790651, 173.42296269779567, 196.7943632953303], "content": "\"L\" cell - left-looking cell, merging with the left neighbor cell to create a", "level": 1}, {"type": "text", "bbox": [28.313951069888656, 202.29268307662608, 173.42296269779567, 207.24390231280483], "content": "\"U\" cell - up-looking cell, merging with the upper neighbor cell to create a span", "level": 1}, {"type": "text", "bbox": [28.31395336449855, 209.36585378363827, 38.22383708542878, 212.90243895233743], "content": "\"U\" cell - up-looking cell, merging with the upper neighbor cell to create a span", "level": 1}, {"type": "text", "bbox": [29.021803656337184, 213.44363251136008, 162.09738505168605, 218.56097559516184], "content": "\"X\" cell - cross cell, to merge with both left and upper neighbor cells", "level": 1}, {"type": "text", "bbox": [29.021803298873518, 219.10438407297715, 106.88517539189678, 223.5121949859755], "content": "\"NL\" - new-line, switch to the next row.", "level": 1}, {"type": "text", "bbox": [28.313955423158863, 229.09394569644826, 172.71511821385653, 234.12195116402438], "content": "A notable attribute of OTSL is that it has the capability of achieving lossless", "level": 1}, {"type": "text", "bbox": [15.572674489389534, 239.07317087378055, 22.651162861482558, 245.43902417743902], "content": "D", "level": 1}, {"type": "text", "bbox": [23.359013775048368, 241.0814195648893, 152.1875021471414, 246.14634133719505], "content": "expression using a data frame called dif in R where you want to summarize x by month:....", "level": 1}, {"type": "text", "bbox": [17.69622050552327, 247.56097574369926, 43.17877864505815, 251.0975609123983], "content": "require(plyr)", "level": 1}, {"type": "text", "bbox": [18.404069224757773, 251.7369520232412, 50.965115736385684, 255.39979127167544], "content": "df <- data. frame(|", "level": 1}, {"type": "text", "bbox": [22.651163510343974, 256.0487805745936, 65.8299425801114, 259.7286013232034], "content": "x = runif(120, 1, 168),", "level": 1}, {"type": "text", "bbox": [22.651163392369167, 263.72442592908055, 70.78488432260173, 268.07317059573165], "content": "2 = runif(120, 1.7, 20.7), y = runif(120, 7, 334).", "level": 1}, {"type": "text", "bbox": [22.651164273748336, 259.5853659012196, 65.12209196491445, 263.8292681036585], "content": "2 = runif(120, 1.7, 20.7), y = runif(120, 7, 334).", "level": 1}, {"type": "text", "bbox": [22.651161964874053, 268.0532359338207, 72.2005805695252, 272.38204595469756], "content": "month = rep(c(5,6,7,8),30), week = sample(1:4, 120, TRUE)", "level": 1}, {"type": "text", "bbox": [22.651163454305934, 272.3170732536586, 75.73982624500361, 276.7108559708596], "content": "month = rep(c(5,6,7,8),30), week = sample(1:4, 120, TRUE)", "level": 1}, {"type": "text", "bbox": [19.111919713614295, 285.03549055403784, 85.64971041128871, 289.3643005749147], "content": "ddply(df, \u00b7 (month, week), sumarize, sd = round(sd(x), 2))", "level": 1}, {"type": "text", "bbox": [29.729653094145604, 292.6941545612348, 69.3691854364745, 297.78048763150406], "content": "ddply(df, \u00b7 (month, week), sumarize, sd = round(sd(x), 2))", "level": 1}, {"type": "text", "bbox": [194.65842814440416, 15.560975775406519, 319.94767233045064, 21.926829079064987], "content": "\u2039picture><loc_102><loc_85><loc_416><loc_160>~", "level": 1}, {"type": "text", "bbox": [196.01325515218164, 22.233914747872973, 230.8195935602695, 28.69291463650609], "content": "<flow_chart>~", "level": 1}, {"type": "text", "bbox": [194.65843009806682, 50.219512223862466, 227.92732544690406, 55.17073177760085], "content": "\u2039/picture>~", "level": 1}, {"type": "text", "bbox": [194.65843165710388, 62.95121962774397, 308.62209444780154, 69.31707293140244], "content": "<otsl><loc_63><loc_78><loc_436><loc_158>*|", "level": 1}, {"type": "text", "bbox": [194.6584321083575, 69.92693126283075, 325.61046699207844, 75.68292657723578], "content": "\u2039ched\u203aData set. <ched>Language<ched>TEDs. <Icel>.", "level": 1}, {"type": "picture", "bbox": [71.95040130615234, 71.29930877685547, 538.4942016601562, 371.3829040527344], "content": "", "level": 1}, {"type": "text", "bbox": [29.729651941424393, 289.29268301361793, 76.44767519723835, 292.82926818231704], "content": "mean = round(mean(x), 2),", "level": 1}, {"type": "text", "bbox": [72.0, 378.186075, 541.0909928319995, 435.59038819999995], "content": "Figure 2: DocTags format describes key features of document elements: type of an element (text, picture, table, code, etc.), location on the page, and content. Nested tags convey additional information: pictures and tables may nest captions, table structure is represented in OTSL tags, lists nest list items, code and pictures carry a classification. All DocTags output is taken from SmolDocling predictions, artificial line-breaks and dots (...) were inserted for better readability. A, B, C - snippets of various pages from [55], D - from [92]", "level": 1}, {"type": "section_header", "bbox": [72.0, 460.7301666, 182.44538359999999, 465.35281299999997], "content": "3.1 Model Architecture", "level": 1}, {"type": "text", "bbox": [71.701, 473.918075, 540.0031329760001, 553.1403882], "content": "The model architecture is illustrated in Figure 1. SmolVLM-256M relies on SigLIP base patch-16/512 (93M) as visual backbone and compared to the 2.2B version of the same model, its training data was rebalanced to emphasize document understanding (41%) and image captioning (14%), combining The Cauldron [42], Docmatix [41] datasets with the addition of MathWriting [23]. It uses lightweight variant of the SmolLM-2 family (135M) [2] as the language backbone and employs a radical pixel shuffle method that compresses each 512x512 image patch into 64 visual tokens. Last but not least, tokenization efficiency was also improved in SmolVLM-256M by increasing the pixel-to-token ratio to 4096 pixels per token and introducing special tokens for sub-image separators.", "level": 1}, {"type": "section_header", "bbox": [72.0, 570.1251666000001, 132.54272019999996, 574.747813], "content": "3.2 DocTags", "level": 1}, {"type": "text", "bbox": [71.701, 583.313075, 541.5381234279996, 684.3533882], "content": "To train VLMs for standardized document conversion, we introduce the DocTags markup format, illustrated in Figure 3. Inspired by OTSL [55], DocTags define a structured vocabulary of unambiguous tags and rules that explicitly separate textual content from document structure. This disentanglement improves performances of Image-to-Sequence models by minimizing confusions. In contrast, direct conversion to standard formats such as HTML or Markdown is often lossy and ambiguous, lacking explicit structural markup, failing to preserve page layout context, and increasing total tokens count. Pieces of text content that represent basic elements in DocTags are enclosed in XML-style tags. Tags define document block types such as: text, caption, footnote, formula, title, list_item, page_footer, page_header, picture, section_header, otsl, code, document_index, etc. Each element can nest additional location tags encoding its position on the page as a bounding box, represented in DocTags as <loc_ x1 ><loc_ y1 ><loc_ x2 ><loc_ y2 > .", "level": 1}, {"type": "text", "bbox": [72.0, 686.974075, 540.0040938599997, 722.5603882], "content": "Special blocks, such as tables and images, nest additional descriptors for captions, tabular structure, or image classes. To encode table structures, we fully include the OTSL vocabulary into DocTags . OTSL has the advantage of being minimalist and supporting cell spans and header information. Its tokens are interleaved with text, allowing", "level": 1}, {"type": "text", "bbox": [367.37354470661336, 70.02439044085372, 475.6744167996367, 74.97560967703248], "content": ",<ched>mAP(0.75)<ched>Inference..<nl>~", "level": 1}]}, {"page_number": 5, "width": 612.0, "height": 792.0, "elements": [{"type": "text", "bbox": [72.0, 32.2990749999999, 500.8301544, 46.06738819999998], "content": "SmolDocling: An ultra-compact vision-language model for end-to-end multi-modal document conversion simultaneous encoding of both table structure and content. In order to promote robust visual-semantic alignment in our document understanding pipeline, we maintain a uniform DocTags representation for cropped page elements (e.g., tables, code, equations) that is identical to their full-page counterparts. This consistency ensures that the model can leverage similar visual cues whether an element is presented in isolation or in the context of an entire page, thereby strengthening the learning signal for both computer vision and downstream machine learning tasks.", "level": 1}, {"type": "text", "bbox": [72.0, 70.75507499999992, 541.0952527039997, 128.15938820000008], "content": "SmolDocling: An ultra-compact vision-language model for end-to-end multi-modal document conversion simultaneous encoding of both table structure and content. In order to promote robust visual-semantic alignment in our document understanding pipeline, we maintain a uniform DocTags representation for cropped page elements (e.g., tables, code, equations) that is identical to their full-page counterparts. This consistency ensures that the model can leverage similar visual cues whether an element is presented in isolation or in the context of an entire page, thereby strengthening the learning signal for both computer vision and downstream machine learning tasks.", "level": 1}, {"type": "text", "bbox": [72.0, 130.780075, 424.48675059999994, 144.5483882000001], "content": "For a complete list of tags and detailed description of the format, consult the appendix.", "level": 1}, {"type": "section_header", "bbox": [72.0, 161.9781666, 164.8215442, 166.60081300000002], "content": "3.3 Model Training", "level": 1}, {"type": "text", "bbox": [71.522, 175.343075, 541.0989836000002, 254.5663882], "content": "We employ a curriculum learning approach to progressively align our model for document conversion, ensuring faster convergence. As an initial step, we incorporate DocTags as tokens in our tokenizer. To align the LLM part we freeze the vision encoder and train only the remaining network to adapt it to the new output format which it hasn't seen before. To ensure comprehensive coverage of all DocTags, we maintain a balanced mix of tasks and data types during training. Next, we unfreeze the vision encoder and train the model on our pretraining datasets (see Sec. 4.1), along with all task-specific conversion datasets, including tables, code, equations, and charts. Finally, we fine-tune using all available datasets.", "level": 1}, {"type": "section_header", "bbox": [72.0, 274.97456320000003, 115.25391359999999, 280.521776], "content": "4 Data", "level": 1}, {"type": "text", "bbox": [72.0, 292.343075, 541.5384139608004, 338.8383882], "content": "For document understanding tasks, each page image represents the visual layout of textual elements-such as paragraphs, formulas, tables, code, or figures with embedded textual content. Publicly available datasets that unify all these characteristics remain limited, and existing corpora often suffer from small sample sizes or fragmented formats. In this section, we explain how we mitigate these issues in both pre-training and task-specific training scenarios.", "level": 1}, {"type": "section_header", "bbox": [72.0, 356.26816660000003, 188.8015224, 360.890813], "content": "4.1 Pre-training datasets", "level": 1}, {"type": "text", "bbox": [71.771, 369.633075, 540.3687153809997, 437.9473882], "content": "In the domain of document understanding, currently available open-access document pre-training datasets include OCR-IDL [10], IIT-CDIP [44], CCpdf [83], and WordScape [88]. OCR-IDL and IIT-CDIP, industry-sourced from the 1990s, provide only page images and OCR text. CCpdf and WordScape, derived from Common Crawl, focus on raw PDFs and Word documents, respectively. While WordScape enriches documents with layout, table structure, and topic information via XML markup, its visual variability is limited. Notably, none of these datasets offer rich annotations for all page elements.", "level": 1}, {"type": "text", "bbox": [71.631, 440.567075, 541.0997608090004, 541.6083882], "content": "DocLayNet-PT. Given the lack of public multimodal document pre-training data with sufficient annotation features, we constructed a new document pre-training dataset named DocLayNet-PT. DocLayNet-PT is a 1.4M pages dataset extracted from the DocFM dataset [25] of unique PDF documents, sourced from CommonCrawl, Wikipedia, and business-related documents. We focused on this dataset to look for documents including visually distinct content such as equations, tables, code and charts with colorful layouts. This dataset was augmented with weak annotations through a series of processing steps. First, we applied PDF parsing to all pages to obtain the text layout including positional information using Docling [7, 53]. Next, we performed an enrichment step to obtain an enhanced, machine-friendly representation of each page. Specifically, we provide annotations for layout elements, table structure, language, topic, and figure classification on each page in DocTag format.", "level": 1}, {"type": "text", "bbox": [72.0, 544.229075, 540.2623427655999, 590.7243882], "content": "Docmatix. To preserve SmolVLM's original DocVQA capabilities, we adapted the same weak annotation strategy used for DocLayNet-PT and applied it to the 1.3M documents inside Docmatix dataset [41]. In addition to the 9.5 million DocVQA questions, we introduced another instruction requiring the full conversion of multi-page documents to DocTags.", "level": 1}, {"type": "section_header", "bbox": [72.0, 608.1541665999999, 193.74297199999998, 612.776813], "content": "4.2 Task-specific Datasets", "level": 1}, {"type": "text", "bbox": [71.522, 621.519075, 541.5291686679997, 722.5603882], "content": "Layout. To optimize prediction quality for document layout and table structure, we sampled 76K pages from DocLayNet-PT. Each page underwent human annotation and rigorous quality review, resulting in a high-quality ground-truth dataset. Of these, 60K pages were utilized for fine-tuning, constituting what we refer to as DocLayNet v2. Additionally, we extracted 63K pages from WordScape, specifically filtering for pages containing both text and tables, and utilized their inherently structure-preserving format as a reliable source of ground truth. To further enhance the model's adaptability to diverse layouts, colors, and fonts, we synthetically generated 250K pages using content from Wikipedia, creating the dataset known as SynthDocNet. This synthetic dataset enables the controlled generation of various document types with precise annotations. All datasets share a unified annotation format and class definitions, ensuring consistency and comprehensive document representation.", "level": 1}]}, {"page_number": 6, "width": 612.0, "height": 792.0, "elements": [{"type": "text", "bbox": [71.631, 70.75507499999992, 541.0960268544001, 128.15938820000008], "content": "Tables. For the table structure recognition task we trained our model on public datasets such as PubTables-1M [78], FinTabNet [103], WikiTableSet [54] and tabular information extracted from WordScape documents. We transformed table structure information from the original ground-truth to OTSL format [55]. Then, we interleaved each cell tag with ground-truth text. This yields a condensed sequence which represents the structure of the table (including merged cells, and header declaration) along with its textual content.", "level": 1}, {"type": "text", "bbox": [72.0, 130.780075, 541.0935758243999, 220.91138820000003], "content": "Charts. For chart reconstruction, several datasets are publicly available [58, 33, 34, 93] that consist of both synthetic and web-scraped charts. However, these datasets are limited by either the quantity of real-world data or the visual diversity of synthetic examples. This has prompted recent studies [50, 26, 60, 94, 93, 13, 57] to crawl or render additional datasets, which unfortunately are rarely shared publicly. To address these gaps, we generated our own chart dataset comprising four types of charts: line, pie, bar, and stacked bar. Using data from 90,000 tables sourced from the FinTabNet dataset [103], we generated a comprehensive dataset containing 5,000 line charts, 380,000 pie charts, 380,000 bar charts, and 77,000 stacked bar charts. Each data point was rendered using three different visualization libraries (details provided in the Appendix), yielding a total of 2.5 million visually diverse charts.", "level": 1}, {"type": "text", "bbox": [71.631, 223.53207499999996, 541.6875238239999, 313.6633882], "content": "Code. Technical books and scientific documents frequently include code snippets, making it essential for documentprocessing models to accurately interpret code structure, particularly indentation, which carries semantic significance in certain programming languages. Several publicly available datasets provide extensive collections of code snippets with permissive licenses in text format [38, 71], typically aggregated from sources like GitHub. However, a common limitation is that these datasets contain only text-based code snippets, lacking any image-based representation. To address this gap, we leveraged LaTeX and Pygments [16] to generate visually diverse renderings of code (details provided in the Appendix). Our dataset includes 9.3M million rendered code snippets generated at 120 dpi, covering 56 programming languages.", "level": 1}, {"type": "text", "bbox": [72.0, 316.28407500000003, 541.5355945449999, 406.4153882], "content": "Equations. Accurately parsing mathematical formulas is crucial for document-processing models, as formulas often contain nuanced structures and semantics critical for scientific understanding. To effectively train our model for formula transcription, we combined publicly available datasets with a custom-rendered dataset. The public datasets [18, 22, 11, 23] collectively comprise approximately 730k unique formulas. In addition, we extracted 4.7 million formulas from arXiv by applying a regular expression to the source LaTeX code. The extracted formulas underwent a rigorous normalization procedure to ensure the model was trained only on correct and standardized LaTex code. Subsequently, these normalized equations were visually rendered using LaTeX (details provided in the Appendix). Overall, our dataset includes 5.5 million unique formulas, each rendered at a resolution of 120 dpi.", "level": 1}, {"type": "table", "bbox": [188.82147216796875, 423.91522216796875, 422.5161437988281, 670.28271484375], "content": "| Dataset Name                     | Size   | Type       |\n|----------------------------------|--------|------------|\n| TheCauldron [42]                 | 2.63M  |            |\n| DocLayNet-PT-Instruct            | 1.62M  | Multi Task |\n| DocMatix w/ Doc Conversion* [41] | 952K   |            |\n| SynthChartNet*                   | 2.5M   |            |\n| PlotQA [61]                      | 225K   |            |\n| FigureQA [33]                    | 100K   | Chart      |\n| Unichart [57]                    | 600K   |            |\n| ChartQA [58]                     | 28K    |            |\n| DocLayNet-PT                     | 1.40M  |            |\n| Doc4MLlaVa - RVLCDIP             | 742K   |            |\n| SynthDocNet                      | 375K   | Conversion |\n| DocLayNet [70]                   | 81K    |            |\n| DocLayNet v2                     | 76K    |            |\n| WordScape-PT                     | 63K    |            |\n| PubTable1M [78]                  | 1M     |            |\n| WordScape Tables [88]            | 207K   | Table      |\n| WikiTableSet (EN) [54]           | 204K   |            |\n| FinTabNet [103]                  | 90K    |            |\n| SynthFormulaNet*                 | 5.5M   | Formula    |\n| SynthCodeNet*                    | 9.3M   | Code       |", "level": 1}, {"type": "text", "bbox": [71.701, 675.237075, 539.99904636, 699.9143882], "content": "Table 1: Datasets. Overview of the datasets used for the training and evaluation of SmolDocling. Datasets we contribute as part of this publication are denoted with (*).", "level": 2}]}, {"page_number": 7, "width": 612.0, "height": 792.0, "elements": [{"type": "section_header", "bbox": [72.0, 77.39016659999993, 268.7713126, 82.01281299999994], "content": "4.3 Document Instruction Tuning datasets", "level": 1}, {"type": "text", "bbox": [71.631, 90.56207499999994, 541.0934817959995, 158.87638820000006], "content": "To reinforce the recognition of different page elements and introduce document-related features and no-code pipelines, we leveraged rule-based techniques and the Granite-3.1-2b-instruct [25] LLM. Using samples from DocLayNet-PT pages, we generated one instruction by randomly sampling layout elements from a page. These instructions included tasks such as 'Perform OCR at bbox,' 'Identify page element type at bbox,' and 'Extract all section headers from the page.' In addition, we train with Cauldron [42] to avoid catastrophic forgetting with the amount of conversation datasets introduced.", "level": 1}, {"type": "table", "bbox": [73.36613464355469, 166.79290771484375, 540.7066040039062, 316.1184997558594], "content": "| Method             | Size   |   Edit Distance \u2193 |   F1-score \u2191 |   Precision \u2191 |   Recall \u2191 |   BLEU \u2191 |   METEOR \u2191 |\n|--------------------|--------|-------------------|--------------|---------------|------------|----------|------------|\n| Full-page          |        |                   |              |               |            |          |            |\n| Qwen2.5 VL [9]     | 7B     |              0.56 |         0.72 |          0.8  |       0.7  |     0.46 |       0.57 |\n| GOT [89]           | 580M   |              0.61 |         0.69 |          0.71 |       0.73 |     0.48 |       0.59 |\n| Nougat (base) [12] | 350M   |              0.62 |         0.66 |          0.72 |       0.67 |     0.44 |       0.54 |\n| SmolDocling (Ours) | 256M   |              0.48 |         0.8  |          0.89 |       0.79 |     0.58 |       0.67 |\n| Code listings      |        |                   |              |               |            |          |            |\n| SmolDocling (Ours) | 256M   |              0.11 |         0.92 |          0.94 |       0.91 |     0.87 |       0.89 |\n| Equations          |        |                   |              |               |            |          |            |\n| Qwen2.5 VL [9]     | 7B     |              0.22 |         0.89 |          0.91 |       0.87 |     0.68 |       0.77 |\n| GOT [89]           | 580M   |              0.11 |         0.95 |          0.95 |       0.96 |     0.85 |       0.91 |\n| Nougat (base) [12] | 350M   |              0.62 |         0.6  |          0.6  |       0.53 |     0.33 |       0.41 |\n| SmolDocling (Ours) | 256M   |              0.11 |         0.95 |          0.96 |       0.95 |     0.83 |       0.89 |", "level": 1}, {"type": "text", "bbox": [71.701, 329.76707500000003, 540.0024400684003, 365.3533882], "content": "Table 2: Structured document text recognition. We evaluate OCR performance and document formatting on DocLayNet, focusing on textual elements, excluding tables. Text accuracy is measured through multiple metrics (Edit Distance, F1-Score, Precision, Recall, BLEU and METEOR) as proposed in [12].", "level": 1}, {"type": "section_header", "bbox": [72.0, 397.4415632, 159.34469120000003, 402.98877600000003], "content": "5 Experiments", "level": 1}, {"type": "text", "bbox": [72.0, 413.91107500000004, 539.9988933828004, 471.3153882], "content": "In this section, we assess the performance of SmolDocling on document understanding tasks, and compare the results to previously published models and baselines. Capturing the qualities and defects of SmolDocling and other models in comparable numbers is a non-trivial effort for reasons we outline further below. Therefore, we report results on standard evaluation tasks and metrics independently (see section 5.2), but also provide a qualitative assessment of output characteristics (see section 5.3).", "level": 1}, {"type": "section_header", "bbox": [72.0, 487.6541666, 202.2211446, 492.27681299999995], "content": "5.1 Implementation Details", "level": 1}, {"type": "text", "bbox": [71.751, 500.82707500000004, 540.1908480214006, 569.1403882], "content": "Our training was conducted using 64 NVIDIA A100 80GB GPUs, with each epoch requiring 38 hours. The model was trained for a total of 4 epochs using the AdamW optimizer with a learning rate of 2 \u00d7 10 -4 and 2 \u00d7 10 -6 for the vision encoder once unfrozen. We also employed gradient clipping to 1.0 for stability and a warmup ratio of 0.03. For inference, using VLLM [39] on an A100 GPU, SmolDocling achieves a page conversion time of 0.35 seconds while occupying only 0.489 GB of VRAM. The model supports a maximum sequence length of 8,192 tokens and is trained to process up to three pages at a time.", "level": 1}, {"type": "section_header", "bbox": [72.0, 585.4791666, 187.0281796, 590.101813], "content": "5.2 Quantitative Results", "level": 1}, {"type": "text", "bbox": [71.701, 598.652075, 539.9997875679996, 623.3293882], "content": "To enable valid cross-model comparisons with SmolDocling, we addressed multiple dataset and harmonization challenges:", "level": 1}, {"type": "list", "bbox": [99.387, 629.154075, 540.004223546, 664.7403882], "content": "Public evaluation datasets lack comprehensive multi-task annotations with diverse layouts. We augmented DocLayNet [70] with text content and reading-order to enable evaluation of both layout analysis and text recognition. Table structure, code listing, formula and chart recognition are evaluated on specific datasets.", "level": 2}, {"type": "list", "bbox": [99.387, 665.156075, 541.0947618512, 722.5603882], "content": "Different models each follow their own conventions for output markup (e.g., Markdown, HTML, DocTags) and produce annotations which do not fully align in semantics. For example, SmolDocling is trained to produce 14 distinct layout-class tags via DocTags , while Qwen2.5-VL combines standard HTML tags and a few explicit class attributes with only partially compatible definitions. GOT and Nougat output Markdown, which can express paragraphs, lists and tables without location annotation.", "level": 2}]}, {"page_number": 8, "width": 612.0, "height": 792.0, "elements": [{"type": "text", "bbox": [72.0, 32.2990749999999, 500.8301544, 46.06738819999998], "content": "SmolDocling: An ultra-compact vision-language model for end-to-end multi-modal document conversion", "level": 1}, {"type": "list", "bbox": [99.387, 70.75507499999992, 540.0018646312003, 106.34138819999998], "content": "Input resolution of page images can affect model performance, but is rarely specified in literature. We standardized at 144 DPI (dots per inch), which reproduces enough detail to be perfectly legible by humans while keeping resource demand for inference reasonable.", "level": 2}, {"type": "text", "bbox": [71.671, 114.70507499999997, 541.097339771, 183.0183882], "content": "Text Recognition (OCR). We first evaluate text recognition accuracy of SmolDocling, compared to Nougat, GOT and Qwen2.5-VL (Table 2), to verify accurate character transcription and reading order. The text-similarity metrics, adopted from [12] require plain formatting without non-textual elements. For different tasks, we conduct evaluations on diverse content types: full-page documents (from DocLayNet test set) in Markdown format, code snippets (from our SynthCodeNet dataset) in plaintext, and equations (from Im2Latex-230k [21]) in LaTeX format. For Qwen2.5-VL and SmolDocling, we convert HTML or DocTags output to Markdown while preserving formatting where possible.", "level": 1}, {"type": "text", "bbox": [71.701, 185.63907499999993, 541.533651838, 243.04338819999998], "content": "Tables are omitted from the full-page output since they do not compare well through Markdown, due to loss of row and column spans. Code snippets must be reproduced with intact line breaks and indentation for accurate scoring. Formulas are evaluated separately in LaTeX format, which is natively produced by all the models tested in this work. The LaTeX outputs from all models, including the ground truth, undergo the same normalization procedure used for generating the SynthFormulaNet dataset. This ensures consistency and fairness in model comparisons.", "level": 1}, {"type": "text", "bbox": [71.631, 245.6640749999999, 540.2554303328002, 303.06838819999996], "content": "SmolDocling significantly outperforms GOT, Nougat and also Qwen2.5-VL, which is 27 times larger, on every metric in full-page transcription (see Table 2. For the code parsing task, we report results exclusively for SmolDocling, as we introduce this task for the first time, and the other models were not explicitly trained for it. SmolDocling also demonstrates superior performance compared to Nougat and Qwen2.5-VL in the formula recognition task, closely matching GOT's results.", "level": 1}, {"type": "text", "bbox": [71.631, 305.689075, 541.0942760316, 406.72938819999996], "content": "Layout. We evaluate element localization accuracy using the DocLayNet test set (Table 3), measuring the ability to reconstruct document representations that support content grounding and visual feature extraction. SmolDocling is compared against Qwen2.5-VL-7b [9], currently the only other VLM addressing this task. For a fair comparison, we map each model's label outputs and the ground-truth to six compatible classes: Text, Section Heading, List Item, Table, Picture, and Formula. Overall, SmolDocling outperforms Qwen2.5-VL-7b by a significant margin, however both models score far below a human baseline. This can be partially explained through inherent difficulty of the dataset, as indicated by relatively low human agreement scores. The scores are primarily impacted by moderate recall of element bounding boxes and different degrees of label confusion in both SmolDocling and Qwen2.5-VL. A majority of samples show accurate element bounding boxes as seen in the Appendix.", "level": 1}, {"type": "table", "bbox": [189.18585205078125, 418.41717529296875, 420.38201904296875, 509.1875915527344], "content": "|                |   SmolDocling |   Qwen2.5-VL |   Human |\n|----------------|---------------|--------------|---------|\n| Formula        |         0.267 |        0.059 |    0.84 |\n| Table          |         0.266 |        0.262 |    0.79 |\n| List Item      |         0.296 |        0.09  |    0.87 |\n| Section Header |         0.289 |        0.129 |    0.83 |\n| Text           |         0.204 |        0.18  |    0.85 |\n| Picture        |         0.066 |        0.078 |    0.7  |\n| Overall        |         0.231 |        0.133 |    0.82 |", "level": 1}, {"type": "text", "bbox": [71.701, 514.252075, 541.3262883968001, 538.9293882], "content": "Table 3: Layout analysis comparison. Evaluation of layout analysis task on DocLayNet with a subset of 6 class labels in mAP[0.5:0.95] metric. Additional reference points are provided for an estimate of human performance [70].", "level": 1}, {"type": "text", "bbox": [71.671, 556.015075, 541.5293487814, 635.2373881999999], "content": "Table Structure Recognition. We evaluate the accuracy of recovering table structures, i.e. shaping columns and rows with the correct text content and spans, through the TEDS metric [104], once including table text content and once on structure only with ommitted text (see Table 4). For isolated assessment, we provide cropped table images from FinTabNet [103] and PubTables-1M [78] test sets, which have poor image quality (72 dpi, compression artifacts). SmolDocling performs competitively against significantly larger models, despite challenges with text transcription from the low-resolution image crops, a limitation likely attributable to insufficient training at such resolutions. Scores for structure only TEDS evaluation are therefore much stronger.", "level": 1}, {"type": "text", "bbox": [71.651, 637.858075, 541.5319459369999, 695.2623882], "content": "Chart Extraction. In this task, we evaluate the ability to convert cropped sections of charts into datapoint tables. All table ouptut is transformed into HTML and compared against the ground-truth HTML using TEDS score, as reported in Table 5. We attribute SmolDocling's performance on charts to the variability in ground-truth styles and the differing interpretations of representations across datasets, which we observe leads to inconsistencies in the model's predictions. Despite its significantly smaller size, SmolDocling achieves a competitive TEDs score.", "level": 1}, {"type": "text", "bbox": [72.0, 697.883075, 539.9963230319994, 722.5603882], "content": "Overall, SmolDocling achieves leading performance across a wide range of document conversion tasks and outperforms significantly larger models in text recognition, layout analysis and structure extraction. Furthermore, the model's", "level": 1}]}, {"page_number": 9, "width": 612.0, "height": 792.0, "elements": [{"type": "text", "bbox": [72.0, 32.2990749999999, 500.8301544, 46.06738819999998], "content": "SmolDocling: An ultra-compact vision-language model for end-to-end multi-modal document conversion", "level": 1}, {"type": "table", "bbox": [165.4056396484375, 69.8314208984375, 446.4163818359375, 130.56399536132812], "content": "| Method              | Model Size   | FinTabNet   | PubTables-1M   |\n|---------------------|--------------|-------------|----------------|\n| SmolVLM [56]        | 2.2B         | 0.18        | -              |\n| Granite Vision [81] | 2.98B        | 0.54        | -              |\n| TableFormer\u2020 [55]   | 52M          | 0.89        | 0.84           |\n| SmolDocling (Ours)  | 256M         | 0.52 (0.81) | 0.65 (0.88)    |", "level": 1}, {"type": "text", "bbox": [71.701, 135.439075, 541.0995813559996, 171.02538820000007], "content": "Table 4: Table structure and cell content reconstruction. Numbers are given in TEDS metric including text content, with structure-only score in brackets where available. We produced results for SmolDocling and TableFormer, other results are reflected here as published. \u2020TableFormer extracts the content through external OCR.", "level": 1}, {"type": "table", "bbox": [218.1203155517578, 187.74884033203125, 393.8341064453125, 257.6752624511719], "content": "| Model               | Size   | TEDs \u2191   |\n|---------------------|--------|----------|\n| Phi-3.5-vision      | 4B     | 0.40     |\n| Granite Vision [81] | 3B     | 0 . 95   |\n| SmolVLM [56]        | 2.2B   | 0.02     |\n| Molmo-E             | 1B     | 0.54     |\n| SmolDocling (Ours)  | 256M   | 0.75     |", "level": 1}, {"type": "text", "bbox": [71.701, 262.261075, 539.9999612536001, 286.93838819999996], "content": "Table 5: Chart analysis. We compare SmolDocling to other small-sized VLMs on the chart-to-table task using the TEDS metric. The results in this table are based on the work reported in [25].", "level": 1}, {"type": "text", "bbox": [72.0, 312.383075, 540.001702836, 347.96938819999997], "content": "high performance in downstream tasks, such as code listing and formula recognition, demonstrates its versatility and real-world applicability. Notably, we establish benchmark results for the code listing recognition, setting a standard for future evaluations.", "level": 1}, {"type": "section_header", "bbox": [72.0, 364.9181666, 180.56245219999997, 369.54081299999996], "content": "5.3 Qualitative Results", "level": 1}, {"type": "text", "bbox": [72.0, 378.091075, 541.5291686679997, 402.7683882], "content": "Several characteristics of SmolDocling output are not evident from task-based metrics alone. In this section, we highlight different output characteristics observed in practice. More illustrated samples can be found in the Appendix.", "level": 1}, {"type": "text", "bbox": [71.651, 405.388075, 540.3676190120005, 462.7933882], "content": "As presented in Figure 3, SmolDocling outputs a sequence of DocTags to encode content, structure and layout. A few notable traits beyond straight content reproduction include: 1) Labeling of page headers and footers, which allows ignoring these repetitive elements in downstream applications. 2) Linking of information, such as captions to tables or pictures, or list elements to parent list through nesting of tags. 3) Preserved line breaks and indentation for code snippets, while line wraps in paragraphs are correctly removed.", "level": 1}, {"type": "text", "bbox": [71.651, 465.413075, 540.2599334279995, 555.5453882], "content": "Among typical defects found in the output of SmolDocling are missing tags (e.g. absent location tags), malformed structure (such as missing closing tags), and endless repetition of tokens from some point in a page. Both of these phenomena are commonly seen in auto-regressive models and may prevent parsing or rendering the output correctly (see Appendix). Despite these limitations, SmolDocling demonstrates unique robustness advantages compared to ensemble systems like Docling, since conversion output is inferred in a single pass. In the latter, errors may accumulate throughout the ensemble model pipeline. For example, a mislocated table detection will lead to missing or distorted output of table structure and content, since this depends on correct table localization. In contrast, SmolDocling can reproduce the tables correctly, even with incorrectly determined location.", "level": 1}, {"type": "section_header", "bbox": [72.0, 575.4725632, 151.88464640000004, 581.019776], "content": "6 Conclusion", "level": 1}, {"type": "text", "bbox": [72.0, 592.551075, 540.0040938599999, 671.7743882], "content": "In this work, we introduce SmolDocling, an efficient and compact VLM optimized for document conversion while providing rich output representation. We also present a suite of new datasets with a unified format for document conversion, including the novel task of code listing transcription. We identify page element localization as a critical area requiring further refinement, where targeted techniques will significantly enhance performance in future iterations. Our results conclusively demonstrate that smaller models with unified, optimized output formats such as DocTags can effectively compete with significantly larger models, establishing a clear pathway for resource-efficient multi-task document understanding models.", "level": 1}]}, {"page_number": 10, "width": 612.0, "height": 792.0, "elements": [{"type": "section_header", "bbox": [72.0, 76.47556320000001, 131.5608064, 82.02277600000002], "content": "References", "level": 1}, {"type": "list", "bbox": [80.339, 93.3957999999999, 365.9494112, 105.78736479999998], "content": "Grobid. https://github.com/kermitt2/grobid , 2008-2025.", "level": 2}, {"type": "list", "bbox": [80.33900000000003, 107.87579999999991, 540.9909192319999, 150.15536480000003], "content": "Loubna Ben Allal, Anton Lozhkov, Elie Bakouch, Gabriel Mart\u00edn Bl\u00e1zquez, Guilherme Penedo, Lewis Tunstall, Andr\u00e9s Marafioti, Hynek Kydl\u00ed\u010dek, Agust\u00edn Piqueres Lajar\u00edn, Vaibhav Srivastav, Joshua Lochner, Caleb Fahlgren, Xuan-Son Nguyen, Cl\u00e9mentine Fourrier, Ben Burtenshaw, Hugo Larcher, Haojun Zhao, Cyril Zakka, Mathieu Morlon, Colin Raffel, Leandro von Werra, and Thomas Wolf. SmolLM2: When Smol Goes Big - Data-Centric Training of a Small Language Model, 2025.", "level": 2}, {"type": "list", "bbox": [80.339, 152.24379999999996, 539.7981920000002, 164.63536480000005], "content": "Amazon Web Services. Amazon Textract. https://aws.amazon.com/textract/ , 2024. Accessed: 2024-11-11.", "level": 2}, {"type": "list", "bbox": [80.339, 166.72479999999996, 540.0004906879999, 189.07836480000003], "content": "Rohan Anil, Andrew M Dai, Orhan Firat, Melvin Johnson, Dmitry Lepikhin, Alexandre Passos, Siamak Shakeri, Emanuel Taropa, Paige Bailey, Zhifeng Chen, et al. Palm 2 technical report. arXiv preprint arXiv:2305.10403 , 2023.", "level": 2}, {"type": "list", "bbox": [80.339, 191.16779999999994, 541.5122199999998, 213.52136480000001], "content": "Anthropic. Claude-3.5. https://www.anthropic.com/news/claude-3-5-sonnet , 2024. Accessed: 202402-11.", "level": 2}, {"type": "list", "bbox": [80.339, 215.60979999999995, 539.9981538559999, 247.92736480000008], "content": "Christoph Auer, Michele Dolfi, Andr\u00e9 Carvalho, Cesar Berrospi Ramis, and Peter WJ Staar. Delivering Document Conversion as a Cloud Service with High Throughput and Responsiveness. In 2022 IEEE 15th International Conference on Cloud Computing (CLOUD) , pages 363-373. IEEE, 2022.", "level": 2}, {"type": "list", "bbox": [80.339, 250.0157999999999, 541.3768627199998, 272.37036480000006], "content": "Christoph Auer, Maksym Lysak, Ahmed Nassar, Michele Dolfi, Nikolaos Livathinos, Panos Vagenas, Cesar Berrospi Ramis, Matteo Omenetti, Fabian Lindlbauer, Kasper Dinkla, et al. Docling Technical Report. arXiv preprint arXiv:2408.09869 , 2024.", "level": 2}, {"type": "list", "bbox": [80.339, 274.4588, 541.3797023359997, 296.81236479999995], "content": "Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, et al. Qwen technical report. arXiv preprint arXiv:2309.16609 , 2023.", "level": 2}, {"type": "list", "bbox": [80.339, 298.9018, 540.9864360319997, 341.1813648], "content": "Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang, Humen Zhong, Yuanzhi Zhu, Mingkun Yang, Zhaohai Li, Jianqiang Wan, Pengfei Wang, Wei Ding, Zheren Fu, Yiheng Xu, Jiabo Ye, Xi Zhang, Tianbao Xie, Zesen Cheng, Hang Zhang, Zhibo Yang, Haiyang Xu, and Junyang Lin. Qwen2.5-VL Technical Report, 2025.", "level": 2}, {"type": "list", "bbox": [76.169, 343.26980000000003, 540.2352310399996, 365.6233648], "content": "Ali Furkan Biten, Rub\u00e8n Tito, Lluis Gomez, Ernest Valveny, and Dimosthenis Karatzas. Ocr-idl: Ocr annotations for industry document library dataset. In European Conference on Computer Vision , pages 241-252. Springer, 2022.", "level": 2}, {"type": "list", "bbox": [76.16899999999998, 367.7128, 301.5125648, 380.1043648], "content": "Lukas Blecher. LaTeX-OCR, 2024. Accessed: 2024-11-09.", "level": 2}, {"type": "list", "bbox": [76.16899999999998, 382.1928, 540.002732288, 404.54736479999997], "content": "Lukas Blecher, Guillem Cucurull, Thomas Scialom, and Robert Stojnic. Nougat: Neural optical understanding for academic documents. arXiv preprint arXiv:2308.13418 , 2023.", "level": 2}, {"type": "list", "bbox": [76.16900000000001, 406.6358, 540.0005803519997, 428.9893648], "content": "Jinyue Chen, Lingyu Kong, Haoran Wei, Chenglong Liu, Zheng Ge, Liang Zhao, Jianjian Sun, Chunrui Han, and Xiangyu Zhang. OneChart: Purify the Chart Structural Extraction via One Auxiliary Token. arXiv preprint arXiv:2404.09987 , 2024.", "level": 2}, {"type": "list", "bbox": [76.16899999999998, 431.0788, 540.985909312, 463.3953648], "content": "Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al. Scaling instruction-finetuned language models. Journal of Machine Learning Research , 25(70):1-53, 2024.", "level": 2}, {"type": "list", "bbox": [76.16900000000001, 465.48380000000003, 428.3512592, 477.8753648], "content": "Pyecharts Contributors. pyecharts: Python library for Echarts, 2024. Accessed: 2024-11-06.", "level": 2}, {"type": "list", "bbox": [76.16900000000001, 479.96380000000005, 428.8713104000001, 492.3553648], "content": "Pygments Contributors. Pygments: Python syntax highlighter, 2024. Accessed: 2024-11-06.", "level": 2}, {"type": "list", "bbox": [76.16900000000001, 494.4448, 539.9986974079999, 516.7983648], "content": "Brian Davis, Bryan Morse, Brian Price, Chris Tensmeyer, Curtis Wigington, and Vlad Morariu. End-to-end document recognition and understanding with dessurt. In European Conference on Computer Vision , pages 280-296. Springer, 2022.", "level": 2}, {"type": "list", "bbox": [76.16899999999998, 518.8878, 539.998697408, 541.2413648], "content": "Yuntian Deng, Anssi Kanervisto, Jeffrey Ling, and Alexander M Rush. Image-to-markup generation with coarse-to-fine attention. In International Conference on Machine Learning , pages 980-989. PMLR, 2017.", "level": 2}, {"type": "list", "bbox": [76.16899999999998, 543.3298, 539.9986974079998, 585.6093648], "content": "Xiaoyi Dong, Pan Zhang, Yuhang Zang, Yuhang Cao, Bin Wang, Linke Ouyang, Xilin Wei, Songyang Zhang, Haodong Duan, Maosong Cao, Wenwei Zhang, Yining Li, Hang Yan, Yang Gao, Xinyue Zhang, Wei Li, Jingwen Li, Kai Chen, Conghui He, Xingcheng Zhang, Yu Qiao, Dahua Lin, and Jiaqi Wang. InternLM-XComposer2: Mastering Free-form Text-Image Composition and Comprehension in Vision-Language Large Model. arXiv preprint arXiv:2401.16420 , 2024.", "level": 2}, {"type": "list", "bbox": [76.16900000000004, 587.6988, 540.990919232, 629.9783648], "content": "Xiaoyi Dong, Pan Zhang, Yuhang Zang, Yuhang Cao, Bin Wang, Linke Ouyang, Songyang Zhang, Haodong Duan, Wenwei Zhang, Yining Li, Hang Yan, Yang Gao, Zhe Chen, Xinyue Zhang, Wei Li, Jingwen Li, Wenhai Wang, Kai Chen, Conghui He, Xingcheng Zhang, Jifeng Dai, Yu Qiao, Dahua Lin, and Jiaqi Wang. InternLM-XComposer2-4KHD: A Pioneering Large Vision-Language Model Handling Resolutions from 336 Pixels to 4K HD. arXiv preprint arXiv:2404.06512 , 2024.", "level": 2}, {"type": "list", "bbox": [76.16900000000004, 632.0668000000001, 541.1488080000003, 654.4203648], "content": "Gregory Eritsyan. Im2LaTeX 230K. https://www.kaggle.com/datasets/gregoryeritsyan/ im2latex-230k , 2023. Accessed: 2025-03-06.", "level": 2}, {"type": "list", "bbox": [76.16900000000004, 656.5098, 340.1846480000002, 668.9013648], "content": "Hugging Face. latex-formulas Dataset, 2024. Accessed: 2024-11-09.", "level": 2}, {"type": "list", "bbox": [76.16900000000004, 670.9898, 539.9986974079993, 693.3443648], "content": "Philippe Gervais, Asya Fadeeva, and Andrii Maksai. Mathwriting: A dataset for handwritten mathematical expression recognition. URL https://arxiv. org/abs/2404.10690 , 2024.", "level": 2}, {"type": "list", "bbox": [76.16900000000001, 695.4328, 518.6881920000001, 707.8243648], "content": "Google Cloud. Document AI. https://cloud.google.com/document-ai , 2024. Accessed: 2024-11-11.", "level": 2}, {"type": "list", "bbox": [76.16900000000004, 709.9128000000001, 298.3474256000002, 722.3043648], "content": "IBM Granite Team. Granite 3.0 Language Models, 2024.", "level": 2}]}, {"page_number": 11, "width": 612.0, "height": 792.0, "elements": [{"type": "list", "bbox": [76.169, 71.87579999999991, 540.3096521599998, 94.22936479999998], "content": "Yucheng Han, Chi Zhang, Xin Chen, Xu Yang, Zhibin Wang, Gang Yu, Bin Fu, and Hanwang Zhang. Chartllama: A multimodal llm for chart understanding and generation. arXiv preprint arXiv:2311.16483 , 2023.", "level": 2}, {"type": "list", "bbox": [76.16900000000004, 95.7177999999999, 541.3797023359997, 118.07236480000006], "content": "Anwen Hu, Haiyang Xu, Jiabo Ye, Ming Yan, Liang Zhang, Bo Zhang, Chen Li, Ji Zhang, Qin Jin, Fei Huang, et al. mplug-docowl 1.5: Unified structure learning for ocr-free document understanding. arXiv preprint arXiv:2403.12895 , 2024.", "level": 2}, {"type": "list", "bbox": [76.16899999999998, 119.5598, 541.0578713407995, 141.91436480000004], "content": "Anwen Hu, Haiyang Xu, Liang Zhang, Jiabo Ye, Ming Yan, Ji Zhang, Qin Jin, Fei Huang, and Jingren Zhou. mplug-docowl2: High-resolution compressing for ocr-free multi-page document understanding. arXiv preprint arXiv:2409.03420 , 2024.", "level": 2}, {"type": "list", "bbox": [76.16899999999998, 143.40279999999996, 539.9975048768002, 165.75636480000003], "content": "Yupan Huang, Tengchao Lv, Lei Cui, Yutong Lu, and Furu Wei. Layoutlmv3: Pre-training for document ai with unified text and image masking. In Proceedings of the 30th ACM International Conference on Multimedia , pages 4083-4091, 2022.", "level": 2}, {"type": "list", "bbox": [76.16899999999998, 167.24479999999994, 488.55241599999994, 179.63636480000002], "content": "J. D. Hunter. Matplotlib: A 2D graphics environment. Computing in Science & Engineering , 9(3):90-95, 2007.", "level": 2}, {"type": "list", "bbox": [76.16899999999998, 181.12479999999994, 539.9982270592, 213.44136479999997], "content": "Guillaume Jaume, Hazim Kemal Ekenel, and Jean-Philippe Thiran. Funsd: A dataset for form understanding in noisy scanned documents. In 2019 International Conference on Document Analysis and Recognition Workshops (ICDARW) , volume 2, pages 1-6. IEEE, 2019.", "level": 2}, {"type": "list", "bbox": [76.169, 214.9297999999999, 540.986799744, 247.24636480000004], "content": "Albert Q Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, et al. Mixtral of experts. arXiv preprint arXiv:2401.04088 , 2024.", "level": 2}, {"type": "list", "bbox": [76.16900000000001, 248.73479999999995, 539.9979800959998, 271.0883648], "content": "Samira Ebrahimi Kahou, Vincent Michalski, Adam Atkinson, \u00c1kos K\u00e1d\u00e1r, Adam Trischler, and Yoshua Bengio. Figureqa: An annotated figure dataset for visual reasoning. arXiv preprint arXiv:1710.07300 , 2017.", "level": 2}, {"type": "list", "bbox": [76.16900000000004, 272.57679999999993, 541.3797023359998, 294.9313648], "content": "Shankar Kantharaj, Rixie Tiffany Ko Leong, Xiang Lin, Ahmed Masry, Megh Thakkar, Enamul Hoque, and Shafiq Joty. Chart-to-text: A large-scale benchmark for chart summarization. arXiv preprint arXiv:2203.06486 , 2022.", "level": 2}, {"type": "list", "bbox": [76.16899999999998, 296.4188, 540.9860601600001, 328.7363648], "content": "Geewook Kim, Teakgyu Hong, Moonbin Yim, Jinyoung Park, Jinyeong Yim, Wonseok Hwang, Sangdoo Yun, Dongyoon Han, and Seunghyun Park. Donut: Document understanding transformer without ocr. arXiv preprint arXiv:2111.15664 , 7(15):2, 2021.", "level": 2}, {"type": "list", "bbox": [76.169, 330.2238, 540.3174529279999, 362.54036479999996], "content": "Sunghwan Kim, Jie Chen, Tiejun Cheng, Asta Gindulyte, Jia He, Siqian He, Qingliang Li, Benjamin A Shoemaker, Paul A Thiessen, Bo Yu, Leonid Zaslavsky, Jian Zhang, and Evan E Bolton. PubChem 2019 update: improved access to chemical data. Nucleic Acids Research , 47(D1):D1102-D1109, 10 2018.", "level": 2}, {"type": "list", "bbox": [76.16900000000001, 364.0288, 540.9886238336001, 396.34536479999997], "content": "Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al. Segment anything. In Proceedings of the IEEE/CVF International Conference on Computer Vision , pages 4015-4026, 2023.", "level": 2}, {"type": "list", "bbox": [76.16900000000001, 397.8338, 540.0005086208004, 430.1503648], "content": "Denis Kocetkov, Raymond Li, Loubna Ben Allal, Jia Li, Chenghao Mou, Carlos Mu\u00f1oz Ferrandis, Yacine Jernite, Margaret Mitchell, Sean Hughes, Thomas Wolf, Dzmitry Bahdanau, Leandro von Werra, and Harm de Vries. The Stack: 3 TB of permissively licensed source code. Preprint , 2022.", "level": 2}, {"type": "list", "bbox": [76.16900000000001, 431.6388, 540.9841227008004, 463.9553648], "content": "Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph E. Gonzalez, Hao Zhang, and Ion Stoica. Efficient Memory Management for Large Language Model Serving with PagedAttention. In Proceedings of the ACM SIGOPS 29th Symposium on Operating Systems Principles , 2023.", "level": 2}, {"type": "list", "bbox": [76.16899999999998, 465.4438, 514.3211024000002, 477.8353648], "content": "Greg Landrum. RDKit: Open-Source Cheminformatics Software. http://www.rdkit.org/. Accessed: 1 January 2023.", "level": 2}, {"type": "list", "bbox": [76.16899999999998, 479.3238, 540.0026515903999, 501.67736479999996], "content": "Hugo Lauren\u00e7on, Andr\u00e9s Marafioti, Victor Sanh, and L\u00e9o Tronchon. Building and better understanding vision-language models: insights and future directions., 2024.", "level": 2}, {"type": "list", "bbox": [76.16900000000001, 503.1658, 540.9828046399998, 525.5203647999999], "content": "Hugo Lauren\u00e7on, L\u00e9o Tronchon, Matthieu Cord, and Victor Sanh. What matters when building vision-language models?, 2024.", "level": 2}, {"type": "list", "bbox": [76.16900000000001, 527.0078, 540.9864360319997, 559.3253648], "content": "Kenton Lee, Mandar Joshi, Iulia Raluca Turc, Hexiang Hu, Fangyu Liu, Julian Martin Eisenschlos, Urvashi Khandelwal, Peter Shaw, Ming-Wei Chang, and Kristina Toutanova. Pix2struct: Screenshot parsing as pretraining for visual language understanding. In International Conference on Machine Learning , pages 18893-18912. PMLR, 2023.", "level": 2}, {"type": "list", "bbox": [76.16900000000004, 560.8127999999999, 540.002037312, 593.1303648], "content": "David Lewis, Gady Agam, Shlomo Argamon, Ophir Frieder, David Grossman, and Jefferson Heard. Building a test collection for complex document information processing. In Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval , pages 665-666, 2006.", "level": 2}, {"type": "list", "bbox": [76.16899999999998, 594.6178, 539.9979800960001, 616.9723648], "content": "Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Yanwei Li, Ziwei Liu, and Chunyuan Li. Llava-onevision: Easy visual task transfer. arXiv preprint arXiv:2408.03326 , 2024.", "level": 2}, {"type": "list", "bbox": [76.16900000000004, 618.4608000000001, 541.0596018559999, 640.8143648], "content": "Feng Li, Renrui Zhang, Hao Zhang, Yuanhan Zhang, Bo Li, Wei Li, Zejun Ma, and Chunyuan Li. Llava-next-interleave: Tackling multi-image, video, and 3d in large multimodal models. arXiv preprint arXiv:2407.07895 , 2024.", "level": 2}, {"type": "list", "bbox": [76.16900000000004, 642.3027999999999, 541.3817571200002, 664.6573648], "content": "Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. In International conference on machine learning , pages 19730-19742. PMLR, 2023.", "level": 2}, {"type": "list", "bbox": [76.169, 666.1448, 540.0028559360002, 688.4993648], "content": "Han Lin, Peng Yang, and Fanlong Zhang. Review of scene text detection and recognition. Archives of computational methods in engineering , 27(2):433-454, April 2020.", "level": 2}, {"type": "list", "bbox": [76.16900000000001, 689.9878, 540.000370432, 722.3043648], "content": "Fangyu Liu, Julian Martin Eisenschlos, Francesco Piccinno, Syrine Krichene, Chenxi Pang, Kenton Lee, Mandar Joshi, Wenhu Chen, Nigel Collier, and Yasemin Altun. Deplot: One-shot visual language reasoning by plot-to-table translation. arXiv preprint arXiv:2212.10505 , 2022.", "level": 2}]}, {"page_number": 12, "width": 612.0, "height": 792.0, "elements": [{"type": "list", "bbox": [76.169, 71.87579999999991, 541.3807334720001, 104.19236480000006], "content": "Fangyu Liu, Francesco Piccinno, Syrine Krichene, Chenxi Pang, Kenton Lee, Mandar Joshi, Yasemin Altun, Nigel Collier, and Julian Martin Eisenschlos. Matcha: Enhancing visual language pretraining with math reasoning and chart derendering. arXiv preprint arXiv:2212.09662 , 2022.", "level": 2}, {"type": "list", "bbox": [76.16900000000001, 106.11379999999997, 514.5452624000002, 118.50536480000005], "content": "Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee. Improved Baselines with Visual Instruction Tuning, 2023.", "level": 2}, {"type": "list", "bbox": [76.16900000000001, 120.42679999999996, 479.63431999999995, 132.81836480000004], "content": "Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual Instruction Tuning. In NeurIPS , 2023.", "level": 2}, {"type": "list", "bbox": [76.16899999999998, 134.73879999999997, 540.9861491072, 177.01836480000009], "content": "Nikolaos Livathinos, Christoph Auer, Maksym Lysak, Ahmed Nassar, Michele Dolfi, Panos Vagenas, Cesar Berrospi Ramis, Matteo Omenetti, Kasper Dinkla, Yusik Kim, Shubham Gupta, Rafael Teixeira de Lima, Valery Weber, Lucas Morin, Ingmar Meijer, Viktor Kuropiatnyk, and Peter W. J. Staar. Docling: An Efficient Open-Source Toolkit for AI-driven Document Conversion, 2025.", "level": 2}, {"type": "list", "bbox": [76.16900000000001, 178.9398, 540.2295373759998, 201.29436480000004], "content": "Nam Ly., Atsuhiro Takasu., Phuc Nguyen., and Hideaki Takeda. Rethinking Image-Based Table Recognition Using Weakly Supervised Methods. pages 872-880, 2023.", "level": 2}, {"type": "list", "bbox": [76.16900000000001, 203.21579999999994, 540.1696418240002, 235.53236479999998], "content": "Maksym Lysak, Ahmed Nassar, Nikolaos Livathinos, Christoph Auer, and Peter Staar. Optimized Table Tokenization for Table Structure Recognition. In Gernot A. Fink, Rajiv Jain, Koichi Kise, and Richard Zanibbi, editors, Document Analysis and Recognition - ICDAR 2023 , pages 37-50, Cham, 2023. Springer Nature Switzerland.", "level": 2}, {"type": "list", "bbox": [76.16900000000001, 237.4537999999999, 540.9864360319999, 269.77036480000004], "content": "Andr\u00e9s Marafioti, Orr Zohar, Miquel Farr\u00e9, Merve Noyan, Elie Bakouch, Pedro Cuenca, Cyril Zakka, Loubna Ben Allal, Anton Lozhkov, Nouamane Tazi, Vaibhav Srivastav, Joshua Lochner, Hugo Larcher, Mathieu Morlon, Lewis Tunstall, Leandro von Werra, and Thomas Wolf. SmolVLM: Redefining small and efficient multimodal models. 2025.", "level": 2}, {"type": "list", "bbox": [76.169, 271.69179999999994, 539.9980876927997, 294.0463648], "content": "Ahmed Masry, Parsa Kavehzadeh, Xuan Long Do, Enamul Hoque, and Shafiq Joty. Unichart: A universal vision-language pretrained model for chart comprehension and reasoning. arXiv preprint arXiv:2305.14761 , 2023.", "level": 2}, {"type": "list", "bbox": [76.16899999999998, 295.9668, 539.9979800959997, 318.32136479999997], "content": "Ahmed Masry, Do Xuan Long, Jia Qing Tan, Shafiq Joty, and Enamul Hoque. Chartqa: A benchmark for question answering about charts with visual and logical reasoning. arXiv preprint arXiv:2203.10244 , 2022.", "level": 2}, {"type": "list", "bbox": [76.16900000000004, 320.2428, 539.9974638336, 342.5973648], "content": "Minesh Mathew, Dimosthenis Karatzas, and CV Jawahar. Docvqa: A dataset for vqa on document images. In Proceedings of the IEEE/CVF winter conference on applications of computer vision , pages 2200-2209, 2021.", "level": 2}, {"type": "list", "bbox": [76.16899999999998, 344.5178, 541.5168882559998, 376.8353648], "content": "Fanqing Meng, Wenqi Shao, Quanfeng Lu, Peng Gao, Kaipeng Zhang, Yu Qiao, and Ping Luo. Chartassisstant: A universal chart multimodal language model via chart-to-table pre-training and multitask instruction tuning. arXiv preprint arXiv:2401.02384 , 2024.", "level": 2}, {"type": "list", "bbox": [76.16900000000001, 378.7568, 540.0001900287999, 401.11036479999996], "content": "Nitesh Methani, Pritha Ganguly, Mitesh M. Khapra, and Pratyush Kumar. PlotQA: Reasoning over Scientific Plots. In The IEEE Winter Conference on Applications of Computer Vision (WACV) , March 2020.", "level": 2}, {"type": "list", "bbox": [76.16899999999998, 403.0318, 541.1434800000002, 425.3863648], "content": "Microsoft Azure. AI Document Intelligence. https://azure.microsoft.com/en-us/products/ ai-services/ai-document-intelligence , 2024. Accessed: 2024-11-11.", "level": 2}, {"type": "list", "bbox": [76.16899999999998, 427.3078, 540.1724662399996, 459.62436479999997], "content": "Lucas Morin, Martin Danelljan, Maria Isabel Agea, Ahmed Nassar, Valery Weber, Ingmar Meijer, Peter Staar, and Fisher Yu. MolGrapher: Graph-based Visual Recognition of Chemical Structures. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , pages 19552-19561, October 2023.", "level": 2}, {"type": "list", "bbox": [76.16900000000001, 461.5458, 539.9986974079997, 483.8993648], "content": "Lucas Morin, Val\u00e9ry Weber, Gerhard Ingmar Meijer, Fisher Yu, and Peter W. J. Staar. PatCID: an open-access dataset of chemical structures in patent documents. Nature Communications , 15(1):6532, Aug 2024.", "level": 2}, {"type": "list", "bbox": [76.16900000000004, 485.8208, 541.5111042560003, 508.17536479999995], "content": "OpenAI. Hello GPT-4O. https://openai.com/index/hello-gpt-4o/ , 2024. Accessed: 2024-02-09, 2024-0211, 2024-02-12.", "level": 2}, {"type": "list", "bbox": [76.169, 510.0968, 381.7752543999999, 522.4883648], "content": "R OpenAI. Gpt-4 technical report. arxiv 2303.08774. View in Article , 2(5), 2023.", "level": 2}, {"type": "list", "bbox": [76.16899999999993, 524.4097999999999, 540.9843916927996, 546.7633648], "content": "Vik Paruchuri. Marker: Convert PDF to Markdown Quickly with High Accuracy. https://github.com/VikParuchuri/marker, 2024.", "level": 2}, {"type": "list", "bbox": [76.16900000000001, 548.6848, 503.72719199999995, 561.0763648], "content": "Vik Paruchuri. Texify. https://github.com/VikParuchuri/texify , 2024. Accessed: 2024-11-11.", "level": 2}, {"type": "list", "bbox": [76.16899999999998, 562.9978, 539.9986974079997, 585.3513648], "content": "Zhiliang Peng, Wenhui Wang, Li Dong, Yaru Hao, Shaohan Huang, Shuming Ma, and Furu Wei. Kosmos-2: Grounding multimodal large language models to the world. arXiv preprint arXiv:2306.14824 , 2023.", "level": 2}, {"type": "list", "bbox": [76.16900000000004, 587.2728, 540.0046710144004, 619.5903648], "content": "Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. DocLayNet: A Large Human-Annotated Dataset for Document-Layout Segmentation. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , KDD '22, page 3743-3751, New York, NY, USA, 2022. Association for Computing Machinery.", "level": 2}, {"type": "list", "bbox": [76.169, 621.5108, 540.1751023616002, 653.8283648], "content": "Ruchir Puri, David Kung, Geert Janssen, Wei Zhang, Giacomo Domeniconi, Vladmir Zolotov, Julian Dolby, Jie Chen, Mihir Choudhury, Lindsey Decker, Veronika Thost, Luca Buratti, Saurabh Pujar, Shyam Ramji, Ulrich Finkler, Susan Malaika, and Frederick Reiss. CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks, 2021.", "level": 2}, {"type": "list", "bbox": [76.16900000000001, 655.7498, 540.0010901248, 688.0663648], "content": "Edward O. Pyzer-Knapp, Matteo Manica, Peter Staar, Lucas Morin, Patrick Ruch, Teodoro Laino, John R. Smith, and Alessandro Curioni. Foundation models for materials discovery - current state and future directions. npj Computational Materials , 11(1):61, Mar 2025.", "level": 2}, {"type": "list", "bbox": [76.16900000000001, 689.9878, 540.9879833024002, 722.3043648], "content": "Yujie Qian, Jiang Guo, Zhengkai Tu, Zhening Li, Connor W. Coley, and Regina Barzilay. MolScribe: Robust Molecular Structure Recognition with Image-to-Graph Generation. Journal of Chemical Information and Modeling , 63(7):1925-1934, Apr 2023.", "level": 2}]}, {"page_number": 13, "width": 612.0, "height": 792.0, "elements": [{"type": "list", "bbox": [76.169, 71.87579999999991, 540.986553216, 104.19236480000006], "content": "Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of machine learning research , 21(140):1-67, 2020.", "level": 2}, {"type": "list", "bbox": [76.16900000000001, 105.98079999999993, 541.3829840384004, 138.29736480000008], "content": "Kohulan Rajan, Henning Otto Brinkhaus, M. Isabel Agea, Achim Zielesny, and Christoph Steinbeck. DECIMER.ai: an open platform for automated optical chemical structure identification, segmentation and recognition in scientific publications. Nature Communications , 14(1):5045, Aug 2023.", "level": 2}, {"type": "list", "bbox": [76.16900000000001, 140.08579999999995, 539.99857792, 172.4023648], "content": "Johannes Rausch, Octavio Martinez, Fabian Bissig, Ce Zhang, and Stefan Feuerriegel. Docparser: Hierarchical document structure parsing from renderings. In Proceedings of the AAAI Conference on Artificial Intelligence , volume 35, pages 4328-4338, 2021.", "level": 2}, {"type": "list", "bbox": [76.169, 174.19079999999985, 540.3096521599997, 196.54536480000002], "content": "Felix M Schmitt-Koopmann, Elaine M Huang, Hans-Peter Hutter, Thilo Stadelmann, and Alireza Darvishy. MathNet: A Data-Centric Approach for Printed Mathematical Expression Recognition. IEEE Access , 2024.", "level": 2}, {"type": "list", "bbox": [76.16899999999998, 198.33379999999988, 540.9838650879999, 230.65036480000003], "content": "Brandon Smock, Rohith Pesala, and Robin Abraham. PubTables-1M: Towards comprehensive table extraction from unstructured documents. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages 4624-4632, 2022.", "level": 2}, {"type": "list", "bbox": [76.16900000000001, 232.4387999999999, 541.3775503999998, 264.75636480000003], "content": "Zineng Tang, Ziyi Yang, Guoxin Wang, Yuwei Fang, Yang Liu, Chenguang Zhu, Michael Zeng, Cha Zhang, and Mohit Bansal. Unifying vision, text, and layout for universal document processing. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 19254-19264, 2023.", "level": 2}, {"type": "list", "bbox": [76.169, 266.5447999999999, 540.98615584, 298.8613648], "content": "Gemini Team, Rohan Anil, Sebastian Borgeaud, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, Katie Millican, et al. Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312.11805 , 2023.", "level": 2}, {"type": "list", "bbox": [76.16900000000001, 300.6498, 540.9903902143997, 392.7423648], "content": "Granite Vision Team, Leonid Karlinsky, Assaf Arbelle, Abraham Daniels, Ahmed Nassar, Amit Alfassi, Bo Wu, Eli Schwartz, Dhiraj Joshi, Jovana Kondic, Nimrod Shabtay, Pengyuan Li, Roei Herzig, Shafiq Abedin, Shaked Perek, Sivan Harary, Udi Barzelay, Adi Raz Goldfarb, Aude Oliva, Ben Wieles, Bishwaranjan Bhattacharjee, Brandon Huang, Christoph Auer, Dan Gutfreund, David Beymer, David Wood, Hilde Kuehne, Jacob Hansen, Joseph Shtok, Ken Wong, Luis Angel Bathen, Mayank Mishra, Maksym Lysak, Michele Dolfi, Mikhail Yurochkin, Nikolaos Livathinos, Nimrod Harel, Ophir Azulai, Oshri Naparstek, Rafael Teixeira de Lima, Rameswar Panda, Sivan Doveh, Shubham Gupta, Subhro Das, Syed Zawad, Yusik Kim, Zexue He, Alexander Brooks, Gabe Goodhart, Anita Govindjee, Derek Leist, Ibrahim Ibrahim, Aya Soffer, David Cox, Kate Soule, Luis Lastras, Nirmit Desai, Shila Ofek-koifman, Sriram Raghavan, Tanveer Syeda-Mahmood, Peter Staar, Tal Drory, and Rogerio Feris. Granite Vision: a lightweight, open-source multimodal model for enterprise Intelligence, 2025.", "level": 2}, {"type": "list", "bbox": [76.16900000000001, 394.5308, 540.9889107584005, 426.8473648], "content": "Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timoth\u00e9e Lacroix, Baptiste Rozi\u00e8re, Naman Goyal, Eric Hambro, Faisal Azhar, et al. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 , 2023.", "level": 2}, {"type": "list", "bbox": [76.16900000000001, 428.6358, 540.9902019199999, 460.9523648], "content": "Micha\u0142 Turski, Tomasz Stanis\u0142awek, Karol Kaczmarek, Pawe\u0142 Dyda, and Filip Grali\u0144ski. CCpdf: Building a High Quality Corpus for Visually Rich Documents from Web Crawl Data. In Gernot A. Fink, Rajiv Jain, Koichi Kise, and Richard Zanibbi, editors, Document Analysis and Recognition - ICDAR 2023 , pages 348-365, Cham, 2023. Springer Nature Switzerland.", "level": 2}, {"type": "list", "bbox": [76.16900000000004, 462.7408, 540.9886238336003, 485.09536479999997], "content": "Unstructured.io Team. Unstructured.io: Open-Source Pre-Processing Tools for Unstructured Data. https://unstructured.io, 2024. Accessed: 2024-11-19.", "level": 2}, {"type": "list", "bbox": [76.16900000000001, 486.8838, 544.0174160000003, 509.2383648], "content": "Vicuna. Vicuna: An open-source chatbot impressing GPT-4 with 90%* ChatGPT quality. https://vicuna.lmsys. org/ , 2023.", "level": 2}, {"type": "list", "bbox": [76.169, 511.0268, 540.9900225920003, 543.3433648], "content": "Bin Wang, Chao Xu, Xiaomeng Zhao, Linke Ouyang, Fan Wu, Zhiyuan Zhao, Rui Xu, Kaiwen Liu, Yuan Qu, Fukai Shang, Bo Zhang, Liqun Wei, Zhihao Sui, Wei Li, Botian Shi, Yu Qiao, Dahua Lin, and Conghui He. MinerU: An Open-Source Solution for Precise Document Content Extraction, 2024.", "level": 2}, {"type": "list", "bbox": [76.16900000000001, 545.1318, 491.18020479999996, 557.5233648], "content": "Michael L Waskom. Seaborn: statistical data visualization. Journal of Open Source Software , 6(60):3021, 2021.", "level": 2}, {"type": "list", "bbox": [76.16900000000004, 559.3118, 540.9899867263999, 591.6283648], "content": "Maurice Weber, Carlo Siebenschuh, Rory Butler, Anton Alexandrov, Valdemar Thanner, Georgios Tsolakis, Haris Jabbar, Ian Foster, Bo Li, Rick Stevens, et al. WordScape: a Pipeline to extract multilingual, visually rich Documents with Layout Annotations from Web Crawl Data. Advances in Neural Information Processing Systems , 36, 2024.", "level": 2}, {"type": "list", "bbox": [76.16899999999998, 593.4168, 541.3849199680001, 615.7713648], "content": "Haoran Wei, Chenglong Liu, Jinyue Chen, Jia Wang, Lingyu Kong, Yanming Xu, Zheng Ge, Liang Zhao, Jianjian Sun, Yuang Peng, et al. General OCR Theory: Towards OCR-2.0 via a Unified End-to-end Model. arXiv preprint arXiv:2409.01704 , 2024.", "level": 2}, {"type": "list", "bbox": [76.169, 617.5598, 540.9827459583997, 649.8763648], "content": "Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems , 35:24824-24837, 2022.", "level": 2}, {"type": "list", "bbox": [76.16900000000001, 651.6648, 539.9986974079999, 674.0193648], "content": "David Weininger. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. Journal of Chemical Information and Computer Sciences , 28(1):31-36, Feb 1988.", "level": 2}, {"type": "list", "bbox": [76.16900000000004, 675.8078, 542.8689760000003, 698.1613648], "content": "Wes McKinney and the Pandas Development Team. Pandas: powerful Python data analysis toolkit, Release 1.4.4. https: //pandas.pydata.org/pandas-docs/version/1.4/pandas.pdf , 2022.", "level": 2}, {"type": "list", "bbox": [76.16900000000004, 699.9498, 541.3797023359994, 722.3043648], "content": "Renqiu Xia, Bo Zhang, Haoyang Peng, Hancheng Ye, Xiangchao Yan, Peng Ye, Botian Shi, Junchi Yan, and Yu Qiao. StructChart: Perception, Structuring, Reasoning for Visual Chart Understanding. arXiv preprint arXiv:2309.11268 , 2023.", "level": 2}]}, {"page_number": 14, "width": 612.0, "height": 792.0, "elements": [{"type": "list", "bbox": [76.169, 71.87579999999991, 540.9861491072, 104.19236480000006], "content": "Renqiu Xia, Bo Zhang, Hancheng Ye, Xiangchao Yan, Qi Liu, Hongbin Zhou, Zijun Chen, Min Dou, Botian Shi, Junchi Yan, et al. Chartx & chartvlm: A versatile benchmark and foundation model for complicated chart reasoning. arXiv preprint arXiv:2402.12185 , 2024.", "level": 2}, {"type": "list", "bbox": [76.16900000000001, 105.74879999999996, 540.9909192320001, 138.0653648], "content": "Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, et al. Layoutlmv2: Multi-modal pre-training for visually-rich document understanding. arXiv preprint arXiv:2012.14740 , 2020.", "level": 2}, {"type": "list", "bbox": [76.16900000000001, 139.6217999999999, 540.3183327104, 171.93836480000004], "content": "Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, and Ming Zhou. Layoutlm: Pre-training of text and layout for document image understanding. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining , pages 1192-1200, 2020.", "level": 2}, {"type": "list", "bbox": [76.16900000000001, 173.49479999999994, 541.3833827200001, 205.81136479999998], "content": "Zuoyu Yan, Xiaode Zhang, Liangcai Gao, Ke Yuan, and Zhi Tang. ConvMath: a convolutional sequence network for mathematical expression recognition. In 2020 25th International Conference on Pattern Recognition (ICPR) , pages 4566-4572. IEEE, 2021.", "level": 2}, {"type": "list", "bbox": [76.16900000000001, 207.36779999999987, 541.3791105536002, 239.68436480000003], "content": "Jiabo Ye, Anwen Hu, Haiyang Xu, Qinghao Ye, Ming Yan, Guohai Xu, Chenliang Li, Junfeng Tian, Qi Qian, Ji Zhang, et al. Ureader: Universal ocr-free visually-situated language understanding with multimodal large language model. arXiv preprint arXiv:2310.05126 , 2023.", "level": 2}, {"type": "list", "bbox": [76.16900000000001, 241.24079999999992, 540.9909192319999, 283.51936479999995], "content": "Pan Zhang, Xiaoyi Dong, Bin Wang, Yuhang Cao, Chao Xu, Linke Ouyang, Zhiyuan Zhao, Shuangrui Ding, Songyang Zhang, Haodong Duan, Wenwei Zhang, Hang Yan, Xinyue Zhang, Wei Li, Jingwen Li, Kai Chen, Conghui He, Xingcheng Zhang, Yu Qiao, Dahua Lin, and Jiaqi Wang. InternLM-XComposer: A Vision-Language Large Model for Advanced Text-image Comprehension and Composition. arXiv preprint arXiv:2309.15112 , 2023.", "level": 2}, {"type": "list", "bbox": [72.0, 285.07579999999996, 541.5167374719999, 337.3183648], "content": "Pan Zhang, Xiaoyi Dong, Yuhang Zang, Yuhang Cao, Rui Qian, Lin Chen, Qipeng Guo, Haodong Duan, Bin Wang, Linke Ouyang, Songyang Zhang, Wenwei Zhang, Yining Li, Yang Gao, Peng Sun, Xinyue Zhang, Wei Li, Jingwen Li, Wenhai Wang, Hang Yan, Conghui He, Xingcheng Zhang, Kai Chen, Jifeng Dai, Yu Qiao, Dahua Lin, and Jiaqi Wang. InternLMXComposer-2.5: A Versatile Large Vision Language Model Supporting Long-Contextual Input and Output. arXiv preprint arXiv:2407.03320 , 2024.", "level": 2}, {"type": "list", "bbox": [72.00000000000001, 338.8738, 539.9980428799997, 361.22836479999995], "content": "Qiming Zhang, Jing Zhang, Yufei Xu, and Dacheng Tao. Vision Transformer with Quadrangle Attention. arXiv preprint arXiv:2303.15105 , 2023.", "level": 2}, {"type": "list", "bbox": [72.00000000000001, 362.78479999999996, 540.9888390272004, 395.1013648], "content": "Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, et al. Opt: Open pre-trained transformer language models. URL https://arxiv. org/abs/2205.01068 , 3:19-0, 2023.", "level": 2}, {"type": "list", "bbox": [72.0, 396.6578, 540.2352310399997, 428.9743648], "content": "Xinyi Zheng, Douglas Burdick, Lucian Popa, Xu Zhong, and Nancy Xin Ru Wang. Global table extractor (gte): A framework for joint table identification and cell structure recognition using visual context. In Proceedings of the IEEE/CVF winter conference on applications of computer vision , pages 697-706, 2021.", "level": 2}, {"type": "list", "bbox": [72.0, 430.5308, 541.3810203968, 452.88436479999996], "content": "Xu Zhong, Elaheh ShafieiBavani, and Antonio Jimeno Yepes. Image-based table recognition: data, model, and evaluation. arXiv preprint arXiv:1911.10683 , 2019.", "level": 2}, {"type": "list", "bbox": [72.0, 454.44079999999997, 541.5165744320003, 476.79536479999996], "content": "Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny. Minigpt-4: Enhancing vision-language understanding with advanced large language models. arXiv preprint arXiv:2304.10592 , 2023.", "level": 2}]}, {"page_number": 15, "width": 612.0, "height": 792.0, "elements": [{"type": "text", "bbox": [58.00420054962188, 249.85692688935512, 87.10481144181726, 256.6014063866289], "content": "Multi Task", "level": 1}, {"type": "text", "bbox": [258.3648255468455, 26.208333328273852, 288.8023255468455, 30.458333328273852], "content": "PubTablelM", "level": 1}, {"type": "text", "bbox": [251.2863364542636, 65.87500019436243, 296.58866203565896, 70.83333320980432], "content": "SynthFormulaNet", "level": 1}, {"type": "picture", "bbox": [75.61947631835938, 73.98613739013672, 538.412353515625, 321.9443054199219], "content": "", "level": 1}, {"type": "text", "bbox": [72.0, 331.49807500000003, 541.0974002959998, 367.0843882], "content": "Figure 3: Training datasets. A treemap visualization of dataset sizes contributing to the training of SmolDocling, categorized by type. Each rectangle represents a dataset, with its area proportional to the total dataset size. Each color indicates a dataset type. 'Multi-Task\" refers to datasets that include multiple different tasks.", "level": 2}, {"type": "section_header", "bbox": [72.00000000000003, 395.32356319999997, 240.0422912000001, 400.870776], "content": "A Additional Datasets Details", "level": 1}, {"type": "section_header", "bbox": [72.00000000000003, 418.8831666, 165.22004820000006, 423.50581299999993], "content": "A.1 SynthChartNet", "level": 1}, {"type": "text", "bbox": [72.0, 432.056075, 541.0956473522003, 489.46038819999995], "content": "SynthChartNet includes four distinct types of charts: line, pie, bar, and stacked bar, generated using three visualization libraries: Matplotlib [30], Seaborn [87], and Pyecharts [15]. Each data set was rendered through all three libraries, producing a final dataset of 2.5 million visually diverse charts. The rendering process was designed to leverage the full range of features available in these libraries, ensuring visual variety while maintaining the plausibility of the data presented. Figure 4 depicts 10 samples from SynthChartNet.", "level": 1}, {"type": "section_header", "bbox": [72.0, 506.2541666, 162.20138040000003, 510.87681299999997], "content": "A.2 SynthCodeNet", "level": 1}, {"type": "text", "bbox": [71.631, 519.4270750000001, 541.5354849564001, 609.5583882], "content": "To render the code snippets of SynthCodeNet, we employed two rendering libraries: LaTeX and Pygments [16]. LaTex, in conjunction with its listings package, enabled the generation of code renderings similar to those commonly seen in academic and technical publications, while Pygments facilitated renderings with aesthetics resembling those of IDEs. The rendering process utilized the complete set of features provided by these libraries to enhance visual diversity while preserving the plausibility of the presented data. This included randomizing various stylistic parameters such as font, font size, style, weight, line spacing, tab size, background color, line numbering (including randomizing the starting line number), and programming language-specific syntax highlighting. Figure 5 shows 4 samples from SynthCodeNet.", "level": 1}, {"type": "section_header", "bbox": [72.0, 626.3531666, 177.58363480000003, 630.975813], "content": "A.3 SynthFormulaNet", "level": 1}, {"type": "text", "bbox": [71.701, 639.525075, 539.9997875679999, 664.2023882], "content": "The extracted formulas of SynthFormulaNet underwent a rigorous normalization procedure to ensure the model was trained only on correct and standardized code. The normalization process consisted of the following major steps:", "level": 1}, {"type": "list", "bbox": [96.06, 670.448075, 539.9949081919997, 695.1253882], "content": "Token Filtering and Removal : Unnecessary or redundant LaTeX tokens were removed or replaced based on predefined policies. This included:", "level": 2}, {"type": "list", "bbox": [119.313, 696.071075, 250.4007181999999, 709.8393882], "content": "Eliminating unwanted tokens.", "level": 2}, {"type": "list", "bbox": [119.313, 708.792075, 350.7838758000001, 722.5603882], "content": "Replacing tokens with equivalent ones for consistency.", "level": 2}, {"type": "text", "bbox": [453.02325558744184, 14.166666695000004, 475.67441837813953, 19.833333361666632], "content": "ChartQA", "level": 1}, {"type": "text", "bbox": [322.07122104525797, 16.291666524999982, 475.67441871967657, 27.666666524999982], "content": "MolGrapher Synthetic 300k WordScape-PocLayNe[nichart", "level": 1}, {"type": "text", "bbox": [406.3052327421802, 47.458333305, 424.7093025096221, 53.124999971666625], "content": "PlotQA", "level": 1}, {"type": "text", "bbox": [356.7141590301681, 72.69643520177078, 390.7742125881003, 78.88689794998038], "content": "SynthDocNet", "level": 1}, {"type": "text", "bbox": [443.1061719932873, 47.42391144883425, 470.01882733443955, 53.15942195705372], "content": "FinTabNet", "level": 1}]}, {"page_number": 16, "width": 612.0, "height": 792.0, "elements": [{"type": "text", "bbox": [13.02816935271395, 239.89534886028537, 19.107981084688234, 244.24127895977267], "content": "600", "level": 1}, {"type": "text", "bbox": [13.028169404052155, 250.32558144739778, 20.84507029313093, 254.67151154688509], "content": "500 -", "level": 1}, {"type": "text", "bbox": [13.028169404052155, 261.62500002307615, 20.84507029313093, 265.97093012256346], "content": "400-", "level": 1}, {"type": "text", "bbox": [13.028169404052155, 272.00000005634377, 20.84507029313093, 276.401162680703], "content": "300 -", "level": 1}, {"type": "text", "bbox": [13.028168890065082, 283.35465122297427, 19.107981257822242, 286.8313952389858], "content": "200", "level": 1}, {"type": "text", "bbox": [13.028169404052155, 293.7848837440063, 20.84507029313093, 298.1308138434936], "content": "100-", "level": 1}, {"type": "text", "bbox": [20.84507055193949, 308.56104662162267, 28.661971441018267, 312.9069767211099], "content": "1700", "level": 1}, {"type": "text", "bbox": [33.00469495349818, 138.20058151733042, 51.24412951363815, 139.99999996673228], "content": "borond suel", "level": 1}, {"type": "text", "bbox": [37.34741781411653, 308.56104662162267, 44.2957744425345, 312.9069767211099], "content": "1800", "level": 1}, {"type": "text", "bbox": [53.84976513419654, 308.561046547121, 59.9295775019537, 312.90697664660826], "content": "1900", "level": 1}, {"type": "text", "bbox": [28.661972857750055, 354.62790699582473, 63.403755999839895, 360.0000000375626], "content": "Property plant and equipment -", "level": 1}, {"type": "text", "bbox": [15.63380455492809, 378.9651164260286, 38.215962961503614, 381.6666665918408], "content": "Iccumulated depre..", "level": 1}, {"type": "text", "bbox": [18.2394363852778, 411.9941861275335, 38.2159620098709, 416.3401162270208], "content": "Cross asset volur", "level": 1}, {"type": "text", "bbox": [34.74178531013805, 434.5930233038673, 61.66666629158336, 439.8081394868304], "content": "Construction in progress in millions except per share", "level": 1}, {"type": "text", "bbox": [17.370892648125775, 468.333333275986, 66.8779320361466, 471.99999994265266], "content": "Construction in progress in millions except per share", "level": 1}, {"type": "text", "bbox": [18.239437892297673, 478.0000000378618, 45.164318873742985, 482.3982557039588], "content": "Total revenues", "level": 1}, {"type": "text", "bbox": [17.370894112770014, 488.3333334044628, 72.0892016078872, 492.8284882620983], "content": "Income (loss) from continuing", "level": 1}, {"type": "text", "bbox": [17.370892505614297, 499.66666661211815, 59.92957272208565, 502.3895347367621], "content": "Income from continuing", "level": 1}, {"type": "text", "bbox": [17.370892676313833, 508.3333333490166, 50.37558729708206, 512.8197673318658], "content": "Net income (loss)", "level": 1}, {"type": "text", "bbox": [18.23943684493173, 518.9040698452161, 39.08450673018563, 522.3808138612277], "content": "Total assets", "level": 1}, {"type": "text", "bbox": [17.37089222506978, 528.3333333804891, 46.90140726006327, 532.8110464885518], "content": "Short-term debt", "level": 1}, {"type": "text", "bbox": [18.239437433630446, 538.8953489363392, 67.74647682165129, 543.333333402892], "content": "Total debt (including capital", "level": 1}, {"type": "text", "bbox": [17.370892003758797, 549.3255814763706, 63.4037543491364, 553.671511575858], "content": "Total stockholders' equity", "level": 1}, {"type": "text", "bbox": [69.4835682804702, 308.561046534704, 76.43192490888815, 312.90697663419127], "content": "2000", "level": 1}, {"type": "text", "bbox": [87.72300642794632, 259.8866280289583, 107.69953078097366, 261.66666670363077], "content": "Enteray Texal", "level": 1}, {"type": "text", "bbox": [79.03755917450457, 266.8401164010513, 117.25351681610613, 268.66666664633635], "content": "-** nteray New or eani", "level": 1}, {"type": "text", "bbox": [85.98591496832148, 273.66666666851484, 117.25351852463659, 275.66666666851484], "content": "Enteray Hississippl", "level": 1}, {"type": "text", "bbox": [86.85446182700736, 279.8779070987257, 106.8309861800347, 281.66666664818456], "content": "Interay Art.", "level": 1}, {"type": "text", "bbox": [84.2488264204841, 308.56104662162267, 92.06572730956287, 312.9069767211099], "content": "2100", "level": 1}, {"type": "text", "bbox": [100.7511740169494, 308.561046534704, 111.17370895957636, 312.90697663419127], "content": "2200", "level": 1}, {"type": "text", "bbox": [99.01408463644653, 354.6279070827433, 106.8309855255253, 359.84302326570634], "content": "-Ind", "level": 1}, {"type": "text", "bbox": [114.64788754444534, 308.56104654556884, 123.3333333299678, 312.9069766450562], "content": "2300", "level": 1}, {"type": "text", "bbox": [88.59154956993636, 535.4186046452661, 96.40845045901513, 539.7645347447534], "content": "12.31", "level": 1}, {"type": "text", "bbox": [123.33333431862148, 377.99999996423276, 145.91549272519697, 383.33333329756607], "content": "band improvements", "level": 1}, {"type": "text", "bbox": [123.33333339708996, 411.9941861565064, 148.52112585721363, 416.3401162559937], "content": "Duldings and building", "level": 1}, {"type": "text", "bbox": [97.27699582278147, 435.33333348798425, 128.54459937909658, 440.67732556231493], "content": "- machinery and equipmen", "level": 1}, {"type": "text", "bbox": [97.27699556185534, 519.7732558370295, 105.9624413473778, 524.1191859365168], "content": "13.85", "level": 1}, {"type": "text", "bbox": [100.7511739830819, 558.0174418545683, 108.56807487216065, 562.3633719540557], "content": "16.92", "level": 1}, {"type": "text", "bbox": [244.92957795607091, 240.66666664263994, 278.8028130228027, 242.66666664263994], "content": "Total Cash Dividends Paid", "level": 1}, {"type": "text", "bbox": [245.7981272601436, 246.84883733128402, 303.1220662656774, 248.66666670178256], "content": "Toral Cash Widends Paid der Common Share", "level": 1}, {"type": "text", "bbox": [310.93896710989117, 61.71220941232025, 317.88732373830914, 66.05813951180755], "content": "2011", "level": 1}, {"type": "text", "bbox": [310.9389675261179, 66.92732560447143, 318.7558684151967, 71.27325570395874], "content": "2012", "level": 1}, {"type": "text", "bbox": [311.80751191349054, 72.14244189440603, 318.7558685419085, 76.48837199389334], "content": "2013", "level": 1}, {"type": "text", "bbox": [310.9389671961708, 79.09593035453975, 317.88732318880585, 80.8343022035998], "content": "2014", "level": 1}, {"type": "text", "bbox": [310.9389680394999, 83.33333330465962, 336.12676049962363, 87.78779058767975], "content": "Total unrecognized", "level": 1}, {"type": "text", "bbox": [239.7183127509903, 91.26453501469723, 336.12676001661526, 97.34883728113607], "content": "2500 5000 7500 10000 12500 15000 17500 20000", "level": 1}, {"type": "picture", "bbox": [129.58473205566406, 107.56428527832031, 283.44012451171875, 188.4427490234375], "content": "", "level": 1}, {"type": "text", "bbox": [296.1737090919946, 192.95930235952233, 309.20187808816974, 197.30523245900963], "content": "1Year", "level": 1}, {"type": "text", "bbox": [232.33247440982026, 175.57558091405565, 252.74968467084727, 196.00145371679002], "content": "United States loYear", "level": 1}, {"type": "text", "bbox": [297.91079828311393, 198.17441856578603, 312.6760558006107, 203.38953474874904], "content": "United States loYear", "level": 1}, {"type": "text", "bbox": [295.30516442239406, 205.99709313325047, 310.07042193989076, 210.34302323273778], "content": "\u00b7 5Year|", "level": 1}, {"type": "picture", "bbox": [129.50840759277344, 212.6746826171875, 263.2164306640625, 297.2315368652344], "content": "", "level": 1}, {"type": "text", "bbox": [227.55868335635873, 299.8691861934705, 335.25820728213995, 302.4767441260063], "content": "O..................... . ... . . ... ..... ........ .... \u00b7", "level": 1}, {"type": "text", "bbox": [237.98122091396803, 305.0843023486575, 246.6666666994905, 309.43023244814475], "content": "2075", "level": 1}, {"type": "text", "bbox": [251.00939011266735, 305.0843023486575, 260.56338015885063, 309.43023244814475], "content": "2100", "level": 1}, {"type": "text", "bbox": [264.906103543076, 305.0843023486575, 273.59154932859843, 309.43023244814475], "content": "2125", "level": 1}, {"type": "text", "bbox": [277.06572800755976, 305.0843023486575, 287.4882629501867, 309.43023244814475], "content": "2150", "level": 1}, {"type": "text", "bbox": [290.96244175585986, 305.0843023486575, 300.5164318020431, 309.43023244814475], "content": "2175", "level": 1}, {"type": "text", "bbox": [304.85915523760667, 305.0843023486575, 315.2816901802336, 309.43023244814475], "content": "2200", "level": 1}, {"type": "text", "bbox": [316.1502358243637, 305.95348843213327, 328.3098592883122, 308.56104636466904], "content": "2225", "level": 1}, {"type": "picture", "bbox": [129.56288146972656, 317.75054931640625, 252.27435302734375, 399.7124328613281], "content": "", "level": 1}, {"type": "text", "bbox": [237.1126759214618, 401.5639535005834, 243.192488289219, 405.9098836000707], "content": "First econd", "level": 1}, {"type": "text", "bbox": [237.1126758517264, 410.2558139897885, 249.27230058724072, 411.9941859977944], "content": "First econd", "level": 1}, {"type": "text", "bbox": [237.1126761972231, 415.4709302266613, 245.79812198274556, 419.8168603261486], "content": "Third", "level": 1}, {"type": "picture", "bbox": [129.63485717773438, 419.8803405761719, 273.7579345703125, 527.4857788085938], "content": "", "level": 1}, {"type": "text", "bbox": [117.25352151672821, 559.7558140095329, 125.07042240580698, 563.2325580255446], "content": "7.69", "level": 1}, {"type": "picture", "bbox": [130.27662658691406, 550.5965576171875, 281.7286071777344, 675.2907104492188], "content": "", "level": 1}, {"type": "text", "bbox": [219.088, 682.047075, 392.9120981999998, 695.8153882], "content": "Figure 4: Samples from SynthChartNet.", "level": 2}, {"type": "picture", "bbox": [343.7693786621094, 95.26988220214844, 466.8607177734375, 185.6379852294922], "content": "", "level": 1}, {"type": "picture", "bbox": [343.62298583984375, 198.2246551513672, 477.6539001464844, 303.7454833984375], "content": "", "level": 1}, {"type": "picture", "bbox": [343.8607482910156, 317.83782958984375, 464.3912048339844, 399.9420166015625], "content": "", "level": 1}, {"type": "picture", "bbox": [344.0517272949219, 416.71923828125, 476.05810546875, 525.5697021484375], "content": "", "level": 1}, {"type": "picture", "bbox": [344.1797180175781, 567.9732055664062, 481.4819641113281, 668.5801391601562], "content": "", "level": 1}, {"type": "text", "bbox": [307.46478903819997, 354.62790703049745, 322.23004655569673, 358.97383712998476], "content": "24.7%", "level": 1}, {"type": "text", "bbox": [330.04694879811336, 305.0843023486575, 339.60093884429665, 309.43023244814475], "content": "2250", "level": 1}, {"type": "text", "bbox": [327.4413149067933, 392.00290698133836, 341.3380281636293, 397.2180231643013], "content": "25.3%", "level": 1}, {"type": "text", "bbox": [296.1737093308934, 212.0000000307681, 310.07042258772935, 217.33333336410146], "content": "\u00b7 3Year", "level": 1}, {"type": "text", "bbox": [270.11737102411547, 372.0116278681116, 284.8826285416122, 377.22674405107455], "content": "24.4%", "level": 1}, {"type": "text", "bbox": [288.3568079224428, 411.1249999901076, 302.2535211792787, 416.3401161730706], "content": "25.7%", "level": 1}]}, {"page_number": 17, "width": 612.0, "height": 792.0, "elements": [{"type": "text", "bbox": [11.325581386500724, 137.19180085869732, 17.69622092138444, 140.72767196652228], "content": "16..", "level": 1}, {"type": "text", "bbox": [11.325581315715844, 164.06442172004438, 17.696220850599563, 167.60029282786923], "content": "20.I", "level": 1}, {"type": "text", "bbox": [23.359012733438266, 23.317241517320895, 64.41424275018578, 28.994143539847073], "content": "#pragma once", "level": 1}, {"type": "text", "bbox": [23.359013845833243, 49.50219619495692, 156.43459524118208, 56.627586256960285], "content": "#include <sdm/world/solvable_by_dp. hpp>", "level": 1}, {"type": "text", "bbox": [23.359013362136597, 55.866764281662654, 196.78197847841565, 62.95655186682245], "content": "#include <sdm/utils/value_function/initializer.hpp>", "level": 1}, {"type": "text", "bbox": [24.066860429723835, 69.9517242616713, 67.95348833670057, 75.6676427842101], "content": "namespace sdm namespace", "level": 1}, {"type": "text", "bbox": [38.223836912005815, 84.15373358613965, 67.95348807479651, 88.39677891552947], "content": "namespace sdm namespace", "level": 1}, {"type": "text", "bbox": [37.51598833670058, 89.10395309265664, 41.0552325227471, 94.7613468651765], "content": "{", "level": 1}, {"type": "text", "bbox": [51.67296607015904, 96.88286969826265, 70.78488340411418, 101.12591502765247], "content": "class", "level": 1}, {"type": "text", "bbox": [50.96511624367732, 102.5402634879714, 54.504360429723825, 108.19765726049127], "content": "{", "level": 1}, {"type": "text", "bbox": [71.47373394574048, 96.79374300943755, 97.70213727461125, 102.62939027937716], "content": "registry protected:", "level": 1}, {"type": "text", "bbox": [51.67296535517804, 109.61200582486754, 84.94186070401524, 115.26939959738729], "content": "registry protected:", "level": 1}, {"type": "text", "bbox": [65.12209531196697, 115.9200001573463, 202.44476973057158, 122.34114175877733], "content": "typedef std:: map<std:: string, std:: shared", "level": 1}, {"type": "text", "bbox": [202.4447672413033, 116.68374847870439, 220.14098817153584, 123.04831615511694], "content": "_ptr<", "level": 1}, {"type": "text", "bbox": [80.69476637300873, 129.41288441094525, 187.57994079161338, 136.57241375162346], "content": "world, Config config) > map_type;", "level": 1}, {"type": "text", "bbox": [77.15552446800508, 122.24896541628402, 232.1744198168423, 130.24344817490464], "content": "Initializer > (*) (std:: shared_ptr<SolvableByDP>", "level": 1}, {"type": "text", "bbox": [65.12209389390988, 136.48462665355135, 152.18750087065408, 143.5675861923728], "content": "static map_type container;", "level": 1}, {"type": "text", "bbox": [71.49273301824127, 82.73938507355251, 108.30087255312499, 87.68960462450741], "content": "initializer sane: Stage the app", "level": 1}, {"type": "text", "bbox": [254.82558079367735, 19.653103463867296, 290.92587149135176, 23.336749644908082], "content": "initializer sane: Stage the app", "level": 1}, {"type": "text", "bbox": [259.78052411408066, 70.61793095216035, 328.44186132338297, 77.0819910212773], "content": "A20RE, NEBAT NAVE: Y0218431-T5E-APP...", "level": 1}, {"type": "text", "bbox": [259.78052597759995, 74.96046857994031, 288.8023257617893, 77.08199108579913], "content": "................", "level": 1}, {"type": "text", "bbox": [262.61191671705427, 88.9386207025301, 375.86773067054264, 93.34699853014149], "content": "1t: contains (gathub. event.pull_request labela.*.name, 'stage*)", "level": 1}, {"type": "text", "bbox": [262.61191840409396, 103.95461206563368, 274.64534863665205, 107.49048317345853], "content": "steps:", "level": 1}, {"type": "text", "bbox": [266.8590124537306, 107.49048315157802, 316.4084310583817, 111.02635425940298], "content": "- uses: actions/checkout (vl", "level": 1}, {"type": "text", "bbox": [266.15116393505326, 110.2572413134867, 334.81250114435556, 115.2693996376297], "content": "- name: pm install and build webpack run:", "level": 1}, {"type": "text", "bbox": [270.3982557821003, 115.26939971256547, 278.1845929914026, 118.09809659882535], "content": "- name: pm install and build webpack run:", "level": 1}, {"type": "text", "bbox": [273.93749974340477, 118.80527091477347, 294.4651160224746, 122.34114202259843], "content": "npm instal.", "level": 1}, {"type": "text", "bbox": [266.8590127250726, 125.16983895155693, 332.6889545855377, 129.57724136666798], "content": "- uses: actions/upload-artifact\u00eanain apm run build", "level": 1}, {"type": "text", "bbox": [273.93749969031614, 122.24896551073743, 298.7122089926417, 125.91310344177191], "content": "- uses: actions/upload-artifact\u00eanain apm run build", "level": 1}, {"type": "text", "bbox": [270.3982562967922, 129.41288432288115, 280.30813938237446, 132.948755430706], "content": "with:", "level": 1}, {"type": "text", "bbox": [273.2296515757025, 136.48462662930217, 298.0043608780281, 140.72767195869199], "content": "path: public/", "level": 1}, {"type": "text", "bbox": [273.9375007196463, 132.90827594019788, 317.1162797894138, 136.57241387123236], "content": "name: webpack artifacts", "level": 1}, {"type": "text", "bbox": [259.78052383389047, 144.26354318086055, 294.46511685714626, 147.7994142886855], "content": "Busld-Docker-Image:", "level": 1}, {"type": "text", "bbox": [262.6119205713985, 147.79941433752242, 304.37499942535527, 151.33528544534738], "content": "runs-on: ubuntu-latest", "level": 1}, {"type": "code", "bbox": [22.65116186164609, 149.72993673669635, 459.39389659642075, 466.7349925872483], "content": "public: static std:: vector<std:: string > available (): static std:: shared_ptr<Initializer > make(std :: string name, std:: shared_ptr<SolvableByDP> world, Config config = {}); } :1 template <class TInitializer > std:: shared_ptr<Initializer > createlnstance (std:: shared_ptr<SolvableByDP> world, Config config) return std:: make_shared<TInitializer >(world, config): package br.com.biot. integra caopagarmeapi.modulos.jwt.utids import br.com.biot. integracaopagarmeapi.config.exception.ValidacaoExceptipn import io.jsonwebtoken.Claims import lombok.extern.slf4j.Slf4j public class JwtCampoUtil{ public static String getCampold(Claims claims) {| tryi return (String) claims.get(\"id\"); loge to tro do tentar recuperar I do JWT: 0'; claims toString): throw new ValidacaoException(\"Erro ao tentar recuperar o campo ID do Token.\"): public static String getCampo(String campo, Claims claims) { return (String) claims.get(campo); sagetro euro de entar recuperar o campo f] do IT: 0', campo, claims toString): throw new ValidacaoException( String.format(\"Erro ao tentar recuperar o campo %s do Token.\", campo) 29| needs: build name: Build image and store in GitHub Packages steps: - name: Checkout uses: actions/checkout iva - name: Download built artifact uses: actions/download-arts factinain path: public - name: create inage and store in Packages uses: nattdavsa0351/actions/docker-gpr01.3.0 with: repo-token: 3{ (secrets.GITHUB,TOKEN) ) runneone wountuel tent needs: Build-Docker-Image name: Deploy app container to Azure steps: - name: \"Login via Azure CLI\" MIRA creda: 91{ SeCretA.AZURE_CREDENTIALS 1) - uses: azure/docker-logan@va with: logan-server: #{(env. IMAGE_REGISTRY_URL)) def main (filename, days) : \u00b7 array I of buckets 0-8, each representing with that counter value fish_by_counter = [O for i in range (9)] for counter in read_counters (filename): fish_by_counter [counter) += 1 the number of fish for i in range (days): fish_by_counter \u00b7 spawn (fish_by_counter) print (f\"After {days) days: {sum(fish_by_counter)} fish\") def spawn (fish_by_counter): # rotate left res = fish_by_counter (1:) + fish_by_counter [:1] # fish that were in bucket O are now new fish in bucket 8. restore the original fish into bucket 6. res [6] += res [8] return res def read_counters (filename) : with open (filename) as \u00a3 : return [int (counter) split (',')] for counter in f. readline ().strip(). -_name.. == '__main_.': main('input.txt', 80) main ('input.txt', 256)", "level": 1}, {"type": "text", "bbox": [220.597, 624.777075, 391.4024303999998, 638.5453882], "content": "Figure 5: Samples from SynthCodeNet.", "level": 2}]}, {"page_number": 18, "width": 612.0, "height": 792.0, "elements": [{"type": "text", "bbox": [9.217484046511995, 24.32122105004828, 31.00426471745388, 31.03052308008739], "content": "kejnKm kejnKm", "level": 1}, {"type": "text", "bbox": [99.71641773335632, 23.482558071443805, 121.5031984042982, 31.030523172945777], "content": "kejnKm kejnKm", "level": 1}, {"type": "text", "bbox": [106.42004271821776, 65.41569780541647, 128.20682338915967, 72.12499983545558], "content": "kejnKm", "level": 1}, {"type": "text", "bbox": [101.35118329488006, 73.58318610317087, 145.00702653177095, 82.40809295782242], "content": "=I+II + II.", "level": 1}, {"type": "formula", "bbox": [7.189023801093627, 105.78333390119383, 239.63414228171135, 129.92494172975262], "content": "", "level": 1}, {"type": "text", "bbox": [15.083155932210529, 145.91570454474459, 31.842217302672207, 153.57794472950206], "content": "d \u2264 1", "level": 1}, {"type": "text", "bbox": [243.00639668464342, 51.99709312070718, 249.71002135987692, 58.70639515074629], "content": "Y", "level": 1}, {"type": "text", "bbox": [368.6993602653209, 114.89680252367225, 382.1066096157879, 121.60610455371148], "content": "4.30", "level": 1}, {"type": "text", "bbox": [368.6993602653209, 134.18604652400677, 382.1066096157879, 140.056686117999], "content": "4.31", "level": 1}, {"type": "text", "bbox": [368.6993605851883, 145.92732569561997, 382.9445629406539, 152.63662772565908], "content": "4.32", "level": 1}, {"type": "formula", "bbox": [47.76332576177219, 200.44040741765934, 381.26865631721375, 228.95494182172956], "content": "", "level": 1}, {"type": "formula", "bbox": [119.61103057861328, 276.1187744140625, 490.6849365234375, 330.5036315917969], "content": "", "level": 1}, {"type": "formula", "bbox": [119.65660858154297, 371.4732971191406, 237.5425567626953, 390.8725891113281], "content": "", "level": 1}, {"type": "text", "bbox": [368.6993602149464, 394.171511854554, 380.4307035554161, 402.558139392103], "content": "(x)", "level": 1}, {"type": "formula", "bbox": [353.61620457413386, 538.4215117141714, 381.2686562800665, 547.6468023231831], "content": "", "level": 1}, {"type": "formula", "bbox": [118.35175323486328, 615.8834838867188, 492.0532531738281, 664.5174560546875], "content": "", "level": 1}, {"type": "text", "bbox": [212.906, 671.676075, 399.0936847999997, 685.4443882], "content": "Figure 6: Samples from SynthFormulaNet.", "level": 1}]}, {"page_number": 19, "width": 612.0, "height": 792.0, "elements": [{"type": "text", "bbox": [72.0, 32.2990749999999, 500.8301544, 46.06738819999998], "content": "SmolDocling: An ultra-compact vision-language model for end-to-end multi-modal document conversion", "level": 1}, {"type": "list", "bbox": [96.06, 70.75507499999992, 407.29196640000004, 84.5233882], "content": "Structural Normalization : The equation structure was standardized by:", "level": 2}, {"type": "list", "bbox": [119.31299999999999, 84.94907499999988, 280.517658, 98.71738819999996], "content": "Normalizing spacing between tokens.", "level": 2}, {"type": "list", "bbox": [119.31299999999999, 109.3520749999999, 392.1984040000001, 123.12038819999998], "content": "Ensuring consistent use of tokens that require one or two braces.", "level": 2}, {"type": "list", "bbox": [119.31299999999999, 97.1500749999999, 284.9410523999999, 110.91838819999998], "content": "Standardizing left and right delimiters.", "level": 2}, {"type": "list", "bbox": [96.05999999999997, 123.54607499999986, 445.2093002000001, 137.31438819999994], "content": "Token Simplification : Redundant tokens and patterns were simplified, including:", "level": 2}, {"type": "list", "bbox": [119.31299999999997, 137.74007499999982, 498.58900940000007, 151.5083881999999], "content": "Collapsing consecutive redundant tokens (e.g., repeated primes, dots, or excessive spacing).", "level": 2}, {"type": "list", "bbox": [119.31299999999997, 149.94207499999982, 486.2257620000001, 163.7103881999999], "content": "Converting special constructs such as \\Big into \\left and \\right for consistency.", "level": 2}, {"type": "text", "bbox": [71.631, 168.915075, 541.0995813559998, 269.9563882], "content": "The extracted equations were subsequently rendered using LaTex at 120 dpi. Several aspects of the rendering procedure have been randomized to ensure the visual diversity of the dataset. Each formula was rendered using a randomly selected font from a set of 85 possible options, with variations in font size, style, weight, and line spacing. Additionally, with a certain probability, each formula was enclosed within an \"align\" environment, allowing for the inclusion of a randomly generated equation number on either the left or right side of the equation. The equation number itself was chosen at random from a diverse set of possibilities, including small and large numbers, integers, floating-point values, or letters. Furthermore, it could be enclosed in single or double parentheses, brackets, or curly braces and was sometimes preceded by text labels such as 'Eqn.\", 'Eqn\", 'Eq.\", or 'Equation\". Figure 6 depicts 6 samples from SynthFormulaNet.", "level": 1}, {"type": "section_header", "bbox": [72.0, 286.3041666, 187.22743159999996, 290.9268129999999], "content": "A.4 DocTags vocabulary", "level": 1}, {"type": "text", "bbox": [72.0, 299.47707499999996, 486.26076880000005, 313.2453881999999], "content": "Here we provide a reference to a complete vocabulary of DocTags for SmolDocling , and their meaning.", "level": 1}, {"type": "text", "bbox": [71.522, 315.866075, 540.0015441792, 351.4523882], "content": "We represent tags in an XML-like style of notation. Some tags have an opening and closing part, and wrap around textual content (or other tags) for example: <text>hello world</text> . Other tags are standalone and do not have a closing tag; they mark a special instruction, for example: <page_break>", "level": 1}, {"type": "text", "bbox": [71.522, 354.072075, 541.534087192, 378.7493882], "content": "The complete DocTags snippet can represent a page or multiple pages, wrapped into <doctag>...</doctag> . When content represents multiple pages, we use <page_break> tag as a separator.", "level": 1}, {"type": "text", "bbox": [70.725, 381.370075, 541.6834670079998, 427.8653882], "content": "Within the scope of <doctag> we place high-level, tags that wrap around the textual content of certain document blocks and identify the type of the block. These tags are: <text> , <caption> , <footnote> , <formula> , <title> , <page_footer> , <page_header> , <picture> , <section_header> , <document_index> , <code> , <otsl> , <list_item> , <ordered_list> , <unordered_list>", "level": 1}, {"type": "text", "bbox": [72.0, 430.486075, 541.0945402803999, 476.98138819999997], "content": "Each element can nest additional standalone location tags that encode its position on the page as a bounding box, represented in DocTags as <loc_ x1 ><loc_ y1 ><loc_ x2 ><loc_ y2 > . Every x,y pair of coordinates belongs to a fixed grid. In our case we used integer values in the range from 0 to 500 which proportionally maps to width and height of the page.", "level": 1}, {"type": "text", "bbox": [70.725, 479.602075, 541.4596795330002, 547.9153882], "content": "<otsl> element contains table representation with following OTSL tags, that are standalone and used to describe structure of a table: <fcel> , <ecel> , <lcel> , <ucel> , <xcel> , <nl> . These tags follow rules as described in [55], we additionally differentiate OTSL notion of a cell into <fcel> - full cell, or cell which contain content, and <ecel> - empty cell. We also augment tabular structure information with extra tags <ched> , <rhed> , <srow> that replace <fcel> to describe cells which belong to column and row headers of the table, as well as table section accordingly. We mark such headers when ground truth about table headers from the underlying dataset is available.", "level": 1}, {"type": "text", "bbox": [70.725, 550.535075, 540.1865449603999, 575.2133882], "content": "<list_item> elements are placed within <ordered_list> or <unordered_list> and define weather it's enumerated (ordered) list or not.", "level": 1}, {"type": "text", "bbox": [70.725, 577.833075, 540.1890668959998, 613.4193882], "content": "<picture> and <otsl> elements by themselves can encapsulate <caption> tag if appropriate picture or table has it's own caption. This way we can connect extra descriptive information to illustrations and tables in the document.", "level": 1}, {"type": "text", "bbox": [70.725, 616.040075, 541.6869581199998, 695.2623882], "content": "<code> elements contain pieces of code as a content, and as such, respect tabulation and line breaks. Code elements also contains special standalone classification tag -<_ programming-language _> where programminglanguage is an appropriate programming language name. Supported values (57 in total) for programming language names are: Ada , Awk , Bash , bc , C , C# , C++ , CMake , COBOL , CSS , Ceylon , Clojure , Crystal , Cuda , Cython , D , Dart , dc , Dockerfile , Elixir , Erlang , FORTRAN , Forth , Go , HTML , Haskell , Haxe , Java , JavaScript , Julia , Kotlin , Lisp , Lua , Matlab , MoonScript , Nim , OCaml , ObjectiveC , Octave , PHP , Pascal , Perl , Prolog , Python , Racket , Ruby , Rust , SML , SQL , Scala , Scheme , Swift , TypeScript , unknown , VisualBasic , XML , YAML", "level": 1}, {"type": "text", "bbox": [71.522, 697.883075, 541.53460744, 722.5603882], "content": "Within <picture> elements we include picture classification with extra standalone tags < image_class> . We classify images from our datasets into following categories: natural_image , pie_chart , bar_chart , line_chart ,", "level": 1}]}, {"page_number": 20, "width": 612.0, "height": 792.0, "elements": [{"type": "text", "bbox": [72.0, 70.75507499999992, 541.09589542, 95.43238819999999], "content": "flow_chart , scatter_chart , heatmap , remote_sensing , chemistry_molecular_structure , chemistry_markush_structure , icon , logo , signature , stamp , qr_code , bar_code , screenshot , map , stratigraphic_chart , cad_drawing , electrical_diagram", "level": 1}, {"type": "section_header", "bbox": [72.0, 115.35956320000003, 317.2370176, 120.90677600000004], "content": "B Additional Details about the SmolDocling", "level": 1}, {"type": "table", "bbox": [71.04452514648438, 140.9374237060547, 540.4243774414062, 366.4342041015625], "content": "| Instruction                 | Description                                                               |\n|-----------------------------|---------------------------------------------------------------------------|\n| Full conversion             | Convert this page to docling.                                             |\n| Chart                       | Convert chart to table (e.g., <chart> ).                                  |\n| Formula                     | Convert formula to LaTeX (e.g., <formula> ).                              |\n| Code                        | Convert code to text (e.g., <code> ).                                     |\n| Table                       | Convert table to OTSL (e.g., <otsl> ).                                    |\n| No-Code Ac- tions/Pipelines | OCR the text in a specific location: <loc_155><loc_233><loc_206><loc_237> |\n| No-Code Ac- tions/Pipelines | Identify element at: <loc_247><loc_482><loc_252><loc_486>                 |\n| No-Code Ac- tions/Pipelines | Find all 'text' elements on the page, retrieve all section headers.       |\n| No-Code Ac- tions/Pipelines | Detect footer elements on the page.                                       |", "level": 1}, {"type": "text", "bbox": [208.308, 371.043075, 403.6916159999997, 384.81138819999995], "content": "Table 6: Supported Conversion Instructions.", "level": 2}]}, {"page_number": 21, "width": 612.0, "height": 792.0, "elements": [{"type": "text", "bbox": [25.953342156928077, 25.124710864372787, 69.97212313179601, 33.40160495465102], "content": "Input Image", "level": 1}, {"type": "text", "bbox": [19.98447212740683, 41.89952149784688, 29.310559083928567, 47.22009566052634], "content": "OH", "level": 1}, {"type": "text", "bbox": [19.98447210964286, 90.44976073894736, 25.97981372454969, 95.77033490162682], "content": "Cl", "level": 1}, {"type": "text", "bbox": [2.6729231175909687e-06, 108.40669852718304, 427.66770453627714, 118.38277508220699], "content": "\u00b7 7: Qualitative comparison for molecule image recognition. Predictions are shown for GOT-OCR", "level": 1}, {"type": "text", "bbox": [2.9393826912347486e-06, 119.71291867691389, 235.15062405739513, 129.02392346160286], "content": "2.5-VL-72B and SmolDocling on a simple molecule image.", "level": 1}, {"type": "text", "bbox": [130.48750151180852, 23.16666681337938, 180.53750015652474, 32.0769227520052], "content": "GOT-OCR 2.0", "level": 1}, {"type": "text", "bbox": [180.52639763210402, 63.18181827326555, 191.1847827252717, 70.49760774694971], "content": "NH,", "level": 1}, {"type": "picture", "bbox": [91.59849548339844, 71.18405151367188, 520.1006469726562, 146.24417114257812], "content": "", "level": 1}, {"type": "text", "bbox": [72.0, 155.3040749999999, 541.0942576439995, 179.98138820000008], "content": "Figure 7: Qualitative comparison for molecule image recognition. Predictions are shown for GOT-OCR 2.0, Qwen2.5-VL-72B and SmolDocling on a simple molecule image.", "level": 2}, {"type": "section_header", "bbox": [72.0, 195.6685632000001, 263.42666240000005, 201.21577600000012], "content": "C Additional Qualitative Analysis", "level": 1}, {"type": "section_header", "bbox": [72.0, 219.38316659999998, 281.52344059999996, 224.005813], "content": "C.1 Molecule Image Recognition Experiment", "level": 1}, {"type": "text", "bbox": [72.0, 232.555075, 541.6807994399998, 289.96038819999995], "content": "Extracting molecule images from documents has potential to accelerate research in chemistry and materials discovery [72]. Some recent document understanding models, such as GOT-OCR 2.0 [89] and Qwen2.5-VL [9], claim to perform molecule image recognition in documents. Following this direction, we experimented with training SmolDocling for molecule image recognition on full document pages and cropped images. In order to predict molecule structures (graphs) with SmolDocling, we use the molecule string identifier named SMILES [91].", "level": 1}, {"type": "text", "bbox": [72.0, 292.580075, 540.0005216860001, 349.9853882], "content": "Datasets. For training on cropped images, we use the MolGrapher-Synthetic-300K dataset [63]. The dataset contains 300K synthetic samples. It is created using real chemical-structures retrieved from the database PubChem [36] which are then synthetically drawn using the library RDKit [40]. For training of full pages, we use Doc2Mol-80K, a subset of the PatCID dataset [64]. Our subset contains 80 000 real patent documents published in the United-States between 2022 and 2024.", "level": 1}, {"type": "text", "bbox": [72.0, 352.605075, 540.0019407599998, 420.91938819999996], "content": "Evaluation. Figure 7 shows an example of predicted molecules for a simple molecule image. While some models capture high-level molecular features, they consistently fail to reconstruct its detailed structure. Notably, this molecule is trivial for specialized models such as MolGrapher [63], MolScribe [73] or DECIMER [75], which accurately recognize its structure. Reducing the gap between general document understanding models and specialized models may require incorporating a specialized vocabulary for SMILES tokens, explicitly encoding atoms and bonds, and extending the SMILES sequence with localization tokens for each atom.", "level": 1}, {"type": "section_header", "bbox": [72.0, 437.8671666, 206.00693259999994, 442.48981299999997], "content": "C.2 Layout analysis samples", "level": 1}, {"type": "text", "bbox": [71.701, 451.040075, 541.6806475521998, 486.62638819999995], "content": "To illustrate and compare the visual grounding capabilities of SmolDocling, a set of sample predictions with SmolDocling and Qwen2.5-VL from DocLayNet [70] is shown in Table 7. Note that the element location results are independent from the correct reproduction of document content and structure.", "level": 1}]}, {"page_number": 22, "width": 612.0, "height": 792.0, "elements": [{"type": "text", "bbox": [50.82412872101403, 8.380720828427002e-08, 114.87645347498888, 7.664991462591388], "content": "Ground-Truth", "level": 1}, {"type": "text", "bbox": [17.40552351395739, 16.72361827195516, 30.633720686013547, 23.691792252667938], "content": "46\"", "level": 1}, {"type": "text", "bbox": [20.886628434093495, 73.00000000327748, 52.912790811080924, 77.34673357337635], "content": "peR, heath and safety", "level": 1}, {"type": "text", "bbox": [20.88662774459137, 160.96482420262748, 36.20348856431644, 165.14572859105522], "content": "Final gonn so yoR and convertible loan", "level": 1}, {"type": "text", "bbox": [20.88662840121628, 165.84254609343145, 55.69767479464125, 169.3333332816435], "content": "Final gonn so yoR and convertible loan", "level": 1}, {"type": "text", "bbox": [50.82412833291056, 238.31155783991153, 102.34447374223491, 240.4020100341254], "content": "tor save did exit) in the Opoon promp pur puse the Enter key:", "level": 1}, {"type": "text", "bbox": [50.82412854637895, 240.3333332730091, 76.58430125104113, 243.99999993967572], "content": "tor save did exit) in the Opoon promp pur puse the Enter key:", "level": 1}, {"type": "text", "bbox": [51.520350162505075, 246.66666665781634, 84.93895327702978, 249.4606364479572], "content": "trollowing display appears.", "level": 1}, {"type": "text", "bbox": [54.30523208130554, 255.00000001087926, 79.36918568788055, 258.6666666775459], "content": "apposoices, tress later, yange Sample form nurter 3 in the Tet \"description' prompt.", "level": 1}, {"type": "text", "bbox": [50.82412759723644, 314.2646567071668, 119.05377710518611, 317.74874369752325], "content": "apposoices, tress later, yange Sample form nurter 3 in the Tet \"description' prompt.", "level": 1}, {"type": "text", "bbox": [51.52035159625617, 324.6666666761549, 84.24273307133073, 326.8073700363945], "content": "Hollowing display appears", "level": 1}, {"type": "text", "bbox": [54.305235342714866, 346.31825805828595, 78.67296476847676, 347.71189269548285], "content": "Tyle doices, tress toler", "level": 1}, {"type": "text", "bbox": [55.697676265060466, 350.333333319755, 75.19186056807888, 351.9999999864217], "content": "Create earlay..", "level": 1}, {"type": "text", "bbox": [50.824128268238844, 385.99999992381845, 91.20494015317594, 389.66666659048514], "content": "Moneyle Actior: Press the Enter key.", "level": 1}, {"type": "text", "bbox": [65.44476794081878, 155.33333330095297, 95.38226794081878, 160.33333330095297], "content": "310.000,000 pal during the 2015", "level": 1}, {"type": "text", "bbox": [105.82558257432225, 114.97487440411112, 131.58575527898444, 119.15577879253874], "content": "Sign untain Change in matters suusequent to", "level": 1}, {"type": "text", "bbox": [105.82558199564608, 135.8793970287901, 132.97819797920852, 140.06030141721772], "content": "Sign untain Change in matters suusequent to", "level": 1}, {"type": "text", "bbox": [105.82558085817534, 139.9999999380641, 139.24418651406305, 143.66666660473072], "content": "the end of the financial year", "level": 1}, {"type": "text", "bbox": [174.0552327105734, 18.117253048652856, 181.71366248509517, 23.69179223322317], "content": "40", "level": 1}, {"type": "text", "bbox": [209.56250041773254, 2.2623123641096754e-07, 269.4375004177325, 9.755443799229283], "content": "SmolDocling", "level": 1}, {"type": "text", "bbox": [368.9618290540748, -0.19149229828849457, 424.7300322058149, 10.643753469940634], "content": "2wen2.5-VL", "level": 1}, {"type": "text", "bbox": [182.49, 68.42807499999992, 472.54291496399986, 256.74238820000005], "content": "Table 7: Visualizations of layout output from SmolDocling and QwenVL2.5 compared to the DocLayNet ground truth. Examples are chosen to be representative of different layout styles and features. The prediction results however do not represent a generalizable measure of the model's performance on inputs with similar features. (1) Multi-column pages are handled by SmolDocling and Qwen2.5-VL, with some recall errors in the latter. (2) A manual page with terminal output shows poor bounding box recall on SmolDocling and label confusion in Qwen2.5-VL. (3) Lists with nesting are handled well in SmolDocling but confuse Qwen2.5-VL. (4) Both SmolDocling and Qwen2.5-VL reconstruct equations well, however with different annotation conventions on including or excluding the equation index. (5) On a portrait page with colorful elements and gradient background, SmolDocling creates less accurate bounding-boxes and Qwen2.5-VL suffers low recall. (6) On a report page with tables and diagrams, SmolDocling output exhibits some repetition loop, fabricating non-existent text cells (bottom left), while Qwen2.5-VL is confused between tables and pictures. Win following, display appears we want yourse", "level": 1}, {"type": "text", "bbox": [421.90988355747317, 259.9128979355612, 437.9229647459669, 264.7906197220602], "content": "Mum: F4 To 11S", "level": 1}, {"type": "picture", "bbox": [70.63162994384766, 265.6536560058594, 584.84765625, 692.060791015625], "content": "", "level": 1}]}, {"page_number": 23, "width": 612.0, "height": 792.0, "elements": [{"type": "text", "bbox": [50.718022541995225, -7.913274657767033e-08, 115.33139534472961, 7.639399131479308], "content": "Ground-Truth", "level": 1}, {"type": "text", "bbox": [34.043610828626576, 28.333333303083236, 97.2674420053844, 29.99999996974998], "content": "301 Workers engarod in cential oe emeriency works pass ntial or emergency works\" includes-", "level": 1}, {"type": "text", "bbox": [36.1279081177575, 50.00000005921004, 86.8459307821455, 53.4757929440367], "content": "301 Workers engarod in cential oe emeriency works pass ntial or emergency works\" includes-", "level": 1}, {"type": "text", "bbox": [41.68604582632101, 54.17028378088651, 115.33139596466735, 56.99999998648343], "content": "gunmpletions, maintenance, repairs, and asset replacement activities.", "level": 1}, {"type": "text", "bbox": [124.3633724342539, 60.42070118408708, 134.09011613237405, 64.58764600576274], "content": "Quality", "level": 1}, {"type": "text", "bbox": [40.99127799126094, 58.333333382459955, 134.0901142542818, 64.66666671579333], "content": "*/ Supie (England) Regularions 20164), the Water Supply Nu Qual", "level": 1}, {"type": "text", "bbox": [36.12790762047312, 71.53255429608362, 133.39534333010855, 73.66666665417688], "content": "Low crane Ixcince tacans the looser of a seucrare Ixcice under sochon 178% of tho", "level": 1}, {"type": "text", "bbox": [40.29651199913738, 72.9215358669585, 70.86627772586264, 77.08848068863415], "content": "Water Industry Act 1991(e):", "level": 1}, {"type": "text", "bbox": [36.115944446583725, 77.61524438893412, 134.09590457028165, 80.6824142141702], "content": "we crape services\" has the meaning givee in sction 2191) of the Water Industry Actl", "level": 1}, {"type": "text", "bbox": [36.127905462325934, 84.66666655276663, 134.09011452808113, 90.33333321943337], "content": "48/ We industy ice 19 lar the mating given in seties 17ACT) and 2I9C) of the nak managemcat on bchall of", "level": 1}, {"type": "text", "bbox": [31.264535409722253, 95.83973302397044, 63.918600859754804, 97.99999997966404], "content": "48/ We industy ice 19 lar the mating given in seties 17ACT) and 2I9C) of the nak managemcat on bchall of", "level": 1}, {"type": "text", "bbox": [36.12790698153958, 99.31218704572404, 71.56104551099922, 102.78464111676897], "content": "pay one nvironment Agency, or)", "level": 1}, {"type": "text", "bbox": [36.127907657259584, 104.17362269363139, 81.98255751891323, 105.66666667264042], "content": "tal tood dinory to three.", "level": 1}, {"type": "text", "bbox": [34.04360797372343, 107.64607686103227, 79.20348956552031, 109.72954911292436], "content": "Cle the purposes of sub-paragraph l wyneed and \"coastal crosion\" have the meanings given in section I of the Plood and", "level": 1}, {"type": "text", "bbox": [36.12790464597427, 111.66666666916922, 134.0901137117295, 114.00000000250259], "content": "Cle the purposes of sub-paragraph l wyneed and \"coastal crosion\" have the meanings given in section I of the Plood and", "level": 1}, {"type": "text", "bbox": [40.296512721171325, 113.20200333687603, 75.72965125063095, 116.67445740792095], "content": "Water Management Act 20100h);", "level": 1}, {"type": "text", "bbox": [36.12791062776764, 121.53589313008683, 113.2470919428718, 125.00834720113176], "content": "pg/w9, management\" has the meaning gives in section 3 of that Acci.", "level": 1}, {"type": "text", "bbox": [34.043611207886364, 127.00000000269006, 96.5726741147874, 129.33333333602343], "content": "229,n29 1) Workers engaged in essential or emergency works- we we stricity interccenector,", "level": 1}, {"type": "text", "bbox": [40.99127898192719, 138.2036728322571, 76.42441751138682, 141.67612690330213], "content": "229,n29 1) Workers engaged in essential or emergency works- we we stricity interccenector,", "level": 1}, {"type": "text", "bbox": [40.29651418534994, 142.99999997075645, 133.3953504483708, 145.14858099874107], "content": "regree trict heat network as delined in regulation 2 of the Heat Network (Meicnng and", "level": 1}, {"type": "text", "bbox": [45.15988455530637, 144.99999992380503, 76.4244185518884, 148.66666659047178], "content": "Billing) Regulations 2014(j).", "level": 1}, {"type": "text", "bbox": [45.159884621265576, 151.99999995333462, 74.34011635140881, 155.66666662000125], "content": "Billing) Regulations 2014.", "level": 1}, {"type": "text", "bbox": [40.29651284785757, 159.66666672561132, 134.78488565059195, 163.33333339227806], "content": "put mao ommissicaling, maintenance and repair of industrial machinery for use on al", "level": 1}, {"type": "text", "bbox": [31.959303412935295, 190.98497493206708, 84.76162580063061, 195.15191975374262], "content": "\u0420\u0434 (1 4/3130. There as Do trialt areading envie:", "level": 1}, {"type": "text", "bbox": [30.569773998584065, 236.66666667011623, 93.09883690548509, 238.33333333678286], "content": "I canations of the saddle point are thie to lowing)", "level": 1}, {"type": "text", "bbox": [132.70058158016943, 243.07178626219593, 138.25872074541195, 246.54424033324096], "content": "(11))", "level": 1}, {"type": "text", "bbox": [27.095931503807815, 250.66666666264746, 138.2587224380526, 254.3333333293141], "content": "Mll9. C is a Lagrange multiplier enforcing the constraint (9). These equations are valid on the support of p. We can prove that these equations lead to a singular support for p. We", "level": 1}, {"type": "text", "bbox": [26.401165054807166, 255.57262110146053, 138.95348235623945, 259.73956592313607], "content": "Mll9. C is a Lagrange multiplier enforcing the constraint (9). These equations are valid on the support of p. We can prove that these equations lead to a singular support for p. We", "level": 1}, {"type": "text", "bbox": [26.40116375534269, 261.0000000446156, 57.66569775192472, 264.66666671128223], "content": "do this by contradiction.", "level": 1}, {"type": "text", "bbox": [27.095930939964486, 275.6666666961572, 50.02325587079131, 279.33333336282385], "content": "woRd then that", "level": 1}, {"type": "text", "bbox": [27.095932016668144, 290.99165272941906, 98.65697734544419, 294.464106800464], "content": "397199e first two terms obviously are cancelled. Notice that", "level": 1}, {"type": "text", "bbox": [26.401163564160942, 305.57595990702526, 75.03488141897866, 309.7429047287009], "content": "3'iMat away from 2' - O we have that", "level": 1}, {"type": "text", "bbox": [55.5813963897756, 313.2153589400804, 110.46802358704122, 317.38230376175596], "content": "91a + 03) - P\u00aa (log(=*) + log(=*)) - 0|", "level": 1}, {"type": "text", "bbox": [27.095928400513316, 320.8547579036864, 138.2587193347581, 325.021702725362], "content": "peese the function is harmonic: a sum of i hodonsorplise plus an antiliolomorphic plece:", "level": 1}, {"type": "text", "bbox": [27.095934107698895, 325.6666666668679, 138.95348822553743, 330.0000000002013], "content": "RI, is is casy to show that (F-) log(a - 6) o d\u00b0(a - 6). With these, we would find that p = 0. This argument contradicts that fp = 1, so the assumption that p is smooth does", "level": 1}, {"type": "text", "bbox": [26.401163403326393, 331.27212025329226, 138.25871752116493, 334.7445743243372], "content": "RI, is is casy to show that (F-) log(a - 6) o d\u00b0(a - 6). With these, we would find that p = 0. This argument contradicts that fp = 1, so the assumption that p is smooth does", "level": 1}, {"type": "text", "bbox": [27.095934107698895, 336.0000000122427, 138.95348822553743, 339.6666666789094], "content": "not work. This argument has boen given before in (5, 23), and we are presenting it bere for completeness", "level": 1}, {"type": "text", "bbox": [26.401163191155486, 340.9949916044331, 43.77034832095692, 344.467445675478], "content": "not work. This argument has boen given before in (5, 23), and we are presenting it bere for completeness", "level": 1}, {"type": "text", "bbox": [27.095935833103056, 346.55091820125887, 137.56395341122803, 348.6666666861883], "content": "siddhat this indicates is that the demity distribution of narticks le sinoular. Since tot", "level": 1}, {"type": "text", "bbox": [26.401173316814493, 351.9999999850441, 137.56395407853324, 354.33333331837747], "content": "9 = 0 the original probability distribution is spherically symmetric, it is natural to assume that the associated distribution is spherically summetric. The radius was computed in 118", "level": 1}, {"type": "text", "bbox": [26.401173316814493, 356.96828053382023, 137.56395407853324, 359.0517527857123], "content": "9 = 0 the original probability distribution is spherically symmetric, it is natural to assume that the associated distribution is spherically summetric. The radius was computed in 118", "level": 1}, {"type": "text", "bbox": [27.095943681066256, 361.6666666416275, 137.5639510866652, 363.9999999749609], "content": "Pitchas also been chocked macrically in 6. 7. A geocralization to all Calabi- Yau conc conforinal field tboory singularities for this result can be found in (23), where it was seen that", "level": 1}, {"type": "text", "bbox": [26.401162937253275, 365.9966611461136, 138.2587170550918, 369.4691152171585], "content": "Pitchas also been chocked macrically in 6. 7. A geocralization to all Calabi- Yau conc conforinal field tboory singularities for this result can be found in (23), where it was seen that", "level": 1}, {"type": "text", "bbox": [26.401166665838417, 370.85809680287485, 138.25872078367695, 374.33333337768363], "content": "this procedure matches vurious otber supergravity calculations in the abeence of symmetry.", "level": 1}, {"type": "text", "bbox": [27.095928261559823, 375.6666666154538, 138.25871919580462, 379.9999999487871], "content": "anger q > 0 but very small, we expect that the distribution does not change much, except in the region :' \u2265 0. Thus we expoct that the distribution gives a five dimensional manifold of", "level": 1}, {"type": "text", "bbox": [26.401168806551237, 380.58096827742037, 138.95348610798354, 384.74791309909597], "content": "anger q > 0 but very small, we expect that the distribution does not change much, except in the region :' \u2265 0. Thus we expoct that the distribution gives a five dimensional manifold of", "level": 1}, {"type": "text", "bbox": [26.401166665838417, 385.3333334078331, 138.25872078367695, 389.6666667411664], "content": "R\u00b0, possibly with a boundary, and that the region bear 2' = O has no density of eigenvalues present. After all, the effoctive potential repels all particles from the locus :' = 0. This", "level": 1}, {"type": "text", "bbox": [26.401162937253275, 390.30383982243984, 138.2587170550918, 394.47078464411544], "content": "R\u00b0, possibly with a boundary, and that the region bear 2' = O has no density of eigenvalues present. After all, the effoctive potential repels all particles from the locus :' = 0. This", "level": 1}, {"type": "text", "bbox": [210.4171795505282, -0.6308089336304192, 269.667123601635, 10.353680507287436], "content": "smolDocling", "level": 1}, {"type": "text", "bbox": [191.06104736975303, 27.000000068952545, 254.97965190263062, 30.666666735619174], "content": "Y/I\"Woekers engaged in essential or emergensy works.", "level": 1}, {"type": "text", "bbox": [192.45058253636216, 49.30884810023622, 243.1686052007502, 53.47579292191176], "content": "K/\"\"ential or emergency works\" inclades", "level": 1}, {"type": "text", "bbox": [198.70348810078428, 54.170283780874115, 271.65406996927385, 56.999999986470925], "content": "Greedons, maintenance, repairs, and asset replacement activities,", "level": 1}, {"type": "text", "bbox": [239.69476771998583, 60.42070119450443, 250.1162790521798, 64.58764601618009], "content": "2016(a).", "level": 1}, {"type": "text", "bbox": [226.4941893116771, 57.64273782300074, 291.10755702814845, 64.66666678316506], "content": "Repain 2 a. he lies Super it Quie)", "level": 1}, {"type": "text", "bbox": [264.0116281852332, 60.33333331766835, 280.68604631674356, 64.66666665100172], "content": "Supply (Water", "level": 1}, {"type": "text", "bbox": [280.6860470202226, 60.420701219522925, 291.8023253507076, 65.28213679182932], "content": "Quality", "level": 1}, {"type": "text", "bbox": [202.87209532184315, 63.89315523359278, 291.1075587821296, 67.3656093046377], "content": "Regulations 2016(b), the Private Water Supplies (Wales) Regulations 2017(c), or the", "level": 1}, {"type": "text", "bbox": [369.61627919689363, 1.141631855716696e-08, 424.50290639415925, 9.028380723289729], "content": "Qwen2.5-VL", "level": 1}, {"type": "text", "bbox": [348.07849454955686, 27.666666637441494, 411.3023257263147, 29.999999970774752], "content": "21. (1) Workers encaned in cascatial or emergency works\u2014", "level": 1}, {"type": "text", "bbox": [348.7732569549668, 49.30884810023622, 399.4912796193548, 53.47579292191176], "content": "\"O) \"emal or emergency works\" includes-", "level": 1}, {"type": "text", "bbox": [355.7209365659078, 54.86477469074839, 427.9767450782776, 56.99999998676901], "content": "0) mspoctos, mamienance, repam, and asset replacement actioes and analysis of walcr", "level": 1}, {"type": "text", "bbox": [383.51162738534373, 58.99999999380077, 409.9127885791914, 61.11519185082079], "content": "0) mspoctos, mamienance, repam, and asset replacement actioes and analysis of walcr", "level": 1}, {"type": "text", "bbox": [410.60755836203515, 59.031719558444365, 418.9447674277903, 63.19866438011991], "content": "Watch", "level": 1}, {"type": "text", "bbox": [360.58430756971893, 63.89315523359278, 448.1249976738856, 67.3656093046377], "content": "Regulations 2016(b), the Private Water Supplies (Wales) Regulations 2017(c), or the", "level": 1}, {"type": "text", "bbox": [396.7122095804509, 60.42070119450443, 407.13372091264495, 64.58764601618009], "content": "2016(a).", "level": 1}, {"type": "text", "bbox": [421.0290699842453, 60.33333331303845, 434.9244188510259, 64.66666664637171], "content": "Supply (Wa", "level": 1}, {"type": "text", "bbox": [182.49, 68.42807499999992, 472.54291496399986, 259.73956592313607], "content": "Table 7: Visualizations of layout output from SmolDocling and QwenVL2.5 compared to the DocLayNet ground truth. Examples are chosen to be representative of different layout styles and features. The prediction results however do not represent a generalizable measure of the model's performance on inputs with similar features. (1) Multi-column pages are handled by SmolDocling and Qwen2.5-VL, with some recall errors in the latter. (2) A manual page with terminal output shows poor bounding box recall on SmolDocling and label confusion in Qwen2.5-VL. (3) Lists with nesting are handled well in SmolDocling but confuse Qwen2.5-VL. (4) Both SmolDocling and Qwen2.5-VL reconstruct equations well, however with different annotation conventions on including or excluding the equation index. (5) On a portrait page with colorful elements and gradient background, SmolDocling creates less accurate bounding-boxes and Qwen2.5-VL suffers low recall. (6) On a report page with tables and diagrams, SmolDocling output exhibits some repetition loop, fabricating non-existent text cells (bottom left), while Qwen2.5-VL is confused between tables and pictures. \"Wille C is a Lagrange multiplier enforcing the constraint (9). These equations are valid on tbe suppost of p. We can prove that these equations lead to a singular support for p. We do this by contradiction.", "level": 1}, {"type": "text", "bbox": [183.4186056158078, 260.33333335582415, 214.68313961238982, 264.6666666891574], "content": "Table 7: Visualizations of layout output from SmolDocling and QwenVL2.5 compared to the DocLayNet ground truth. Examples are chosen to be representative of different layout styles and features. The prediction results however do not represent a generalizable measure of the model's performance on inputs with similar features. (1) Multi-column pages are handled by SmolDocling and Qwen2.5-VL, with some recall errors in the latter. (2) A manual page with terminal output shows poor bounding box recall on SmolDocling and label confusion in Qwen2.5-VL. (3) Lists with nesting are handled well in SmolDocling but confuse Qwen2.5-VL. (4) Both SmolDocling and Qwen2.5-VL reconstruct equations well, however with different annotation conventions on including or excluding the equation index. (5) On a portrait page with colorful elements and gradient background, SmolDocling creates less accurate bounding-boxes and Qwen2.5-VL suffers low recall. (6) On a report page with tables and diagrams, SmolDocling output exhibits some repetition loop, fabricating non-existent text cells (bottom left), while Qwen2.5-VL is confused between tables and pictures. \"Wille C is a Lagrange multiplier enforcing the constraint (9). These equations are valid on tbe suppost of p. We can prove that these equations lead to a singular support for p. We do this by contradiction.", "level": 1}, {"type": "text", "bbox": [340.43604747627296, 261.0000000446156, 371.700581472855, 264.66666671128223], "content": "do this by contradiction.", "level": 1}, {"type": "picture", "bbox": [70.61808013916016, 265.51434326171875, 584.7910766601562, 692.1535034179688], "content": "", "level": 1}]}, {"page_number": 24, "width": 612.0, "height": 792.0, "elements": [{"type": "text", "bbox": [34.04360409124125, 24.271739241046816, 88.93023128850689, 27.739130393518394], "content": "DEVOTION TO SERVICE", "level": 1}, {"type": "text", "bbox": [27.095930233431805, 188.46972862127268, 38.90697556220784, 190.13465555237917], "content": "Net incorre heinrat let tormente", "level": 1}, {"type": "text", "bbox": [27.09593075855946, 195.46242171811286, 59.749996208592016, 196.94782600894723], "content": "Net incorre heinrat let tormente", "level": 1}, {"type": "text", "bbox": [27.790697373538563, 213.4436325338305, 38.906976975589345, 215.108559464937], "content": "Total astets", "level": 1}, {"type": "text", "bbox": [27.095930654399826, 219.10438419180014, 43.770348785910244, 222.76722344023437], "content": "Working capital", "level": 1}, {"type": "text", "bbox": [27.09593020404631, 226.07391306162663, 47.938952868434335, 229.5413042140981], "content": "Shareholders equity", "level": 1}, {"type": "text", "bbox": [27.040637310742923, 232.936225149081, 56.33097391962965, 236.5667939898883], "content": "Common shares outstanding", "level": 1}, {"type": "text", "bbox": [54.19186057351803, 257.28043475673144, 61.139535006908325, 260.0543478057324], "content": "$1171", "level": 1}, {"type": "text", "bbox": [67.39244164241632, 212.11169109495995, 79.20348824275811, 215.7745303433942], "content": "$ 875.228", "level": 1}, {"type": "text", "bbox": [67.39244164241632, 219.10438419180014, 79.20348824275811, 222.76722344023437], "content": "$ 265,375", "level": 1}, {"type": "text", "bbox": [67.39244164241632, 226.07391306162663, 79.20348824275811, 229.5413042140981], "content": "$ 436,616", "level": 1}, {"type": "text", "bbox": [71.56104685339383, 233.00869562629657, 79.20348828507515, 235.78260867529764], "content": "15.971", "level": 1}, {"type": "text", "bbox": [86.15116277002937, 187.2391305453947, 94.48837183578458, 190.70652169786615], "content": "38,104", "level": 1}, {"type": "text", "bbox": [83.37209308311397, 212.11169109495995, 95.18313968345576, 215.7745303433942], "content": "$ 1.197,188", "level": 1}, {"type": "text", "bbox": [83.37209317574964, 219.10438405613627, 95.18313977609144, 222.7672233045705], "content": "$ 295,648", "level": 1}, {"type": "text", "bbox": [83.37209317574964, 226.07391301760094, 95.18313977609144, 228.8478260666019], "content": "$ 451.169", "level": 1}, {"type": "text", "bbox": [87.54069801618454, 233.00869562629657, 95.18313944786587, 235.78260867529764], "content": "36.520", "level": 1}, {"type": "text", "bbox": [104.21511716907098, 24.96521742251946, 137.5639534320918, 29.968684741426614], "content": "Giving back to the and where our", "level": 1}, {"type": "text", "bbox": [104.21511664836707, 46.46304345100282, 129.92151211548946, 50.62391302450442], "content": "Giving back to the and where our", "level": 1}, {"type": "text", "bbox": [104.21511676506786, 51.9457201775017, 141.03778929110953, 56.27453019837856], "content": "employees live, work, and care for their", "level": 1}, {"type": "text", "bbox": [104.2151171134459, 56.86521743985645, 135.47965111002793, 61.71956511682845], "content": "employees live, work, and care for their", "level": 1}, {"type": "text", "bbox": [104.21511621083835, 62.26826725458284, 138.25872074371594, 67.26739131069621], "content": "families is a serious and dedicated", "level": 1}, {"type": "text", "bbox": [104.21511660613464, 67.92901880153897, 129.22674380340027, 72.25782882241583], "content": "families is a serious and dedicated", "level": 1}, {"type": "text", "bbox": [104.21511699002617, 73.50869557868407, 128.53197591743503, 77.66956515218556], "content": "commitment.", "level": 1}, {"type": "text", "bbox": [27.790698682705, 135.85803759335045, 69.47674401148105, 140.1868476142273], "content": "FINANCIAL HIGHLIGHTS", "level": 1}, {"type": "text", "bbox": [27.790699408846535, 142.16304351814438, 138.95349034309132, 147.17954078421303], "content": "VISA's ongoing commitment to investing in core products and focus on managing costs helped generate a strong finish in 2011.", "level": 1}, {"type": "text", "bbox": [27.790699135047173, 147.71086962821687, 143.81686287202635, 152.56521730518887], "content": "VISA's ongoing commitment to investing in core products and focus on managing costs helped generate a strong finish in 2011.", "level": 1}, {"type": "text", "bbox": [120.1947682135765, 169.1565762223521, 145.2063954108421, 172.81941547078634], "content": "CROCURR TUAL SALES", "level": 1}, {"type": "text", "bbox": [120.19476749110572, 172.67608705846533, 149.37499922124894, 176.14926938677513], "content": "BY CORE PRODUCT GROUP", "level": 1}, {"type": "text", "bbox": [122.97383798101833, 214.28478258281825, 147.98546517828396, 217.10647184011157], "content": "\u00a9 Fund Gin A Fume Detectior", "level": 1}, {"type": "text", "bbox": [122.97383785239903, 217.75217395430775, 143.81686051678705, 221.2195651067792], "content": "\u00b7 Poutis Car Outection", "level": 1}, {"type": "text", "bbox": [122.27906956221783, 223.99347827901795, 138.25872069543723, 227.4608694314894], "content": "@ Other hodets", "level": 1}, {"type": "text", "bbox": [102.1308141180914, 187.2391304529309, 110.4680231838466, 190.70652160540237], "content": "60152", "level": 1}, {"type": "text", "bbox": [104.90988390575085, 194.17391306162654, 110.46802307099335, 197.6413042140981], "content": "1.91", "level": 1}, {"type": "text", "bbox": [98.65697651046685, 213.59130435106852, 109.77325611251763, 214.97826087556905], "content": "S1015092", "level": 1}, {"type": "text", "bbox": [98.65697680404422, 219.10438419180014, 110.46802340438599, 222.76722344023437], "content": "$ 287,079", "level": 1}, {"type": "text", "bbox": [98.65697680404422, 226.07391306162663, 110.46802340438599, 229.5413042140981], "content": "$ 433,666", "level": 1}, {"type": "text", "bbox": [102.13081421072702, 233.0086956934382, 110.46802327648226, 236.47608684590966], "content": "36,493", "level": 1}, {"type": "text", "bbox": [191.75581526426072, 23.578260837683956, 245.94767419166956, 27.739130411185556], "content": "DEVOTION TO SERVICE", "level": 1}, {"type": "text", "bbox": [259.14825587375134, 24.965217474530277, 294.58139440321094, 29.968684793437433], "content": "\"Giving back to the and where our", "level": 1}, {"type": "text", "bbox": [261.2325584017222, 46.46304345678186, 286.9389538688446, 50.62391303028335], "content": "\"Giving back to the and where our", "level": 1}, {"type": "text", "bbox": [259.1482568708299, 51.94572028427069, 298.74999993316715, 56.27453030514744], "content": "-employees live, work, and care for their", "level": 1}, {"type": "text", "bbox": [261.23255840941255, 56.86521754676755, 292.4970924059946, 61.719565223739664], "content": "-employees live, work, and care for their", "level": 1}, {"type": "text", "bbox": [261.2325586242225, 62.26826723642034, 295.27616315710014, 66.5970772572972], "content": "families is a serious", "level": 1}, {"type": "text", "bbox": [418.94476865554003, 24.965217431187966, 451.5988366487041, 29.96868475009512], "content": "Giving back to the and where our", "level": 1}, {"type": "text", "bbox": [418.24999951241756, 46.46304352035065, 443.95639497953994, 50.62391309385225], "content": "Giving back to the and where our", "level": 1}, {"type": "text", "bbox": [418.2499999069926, 51.94572018905967, 455.76744070289107, 56.27453020993653], "content": "employees live, work, and care for their", "level": 1}, {"type": "text", "bbox": [418.25000026987755, 56.86521754676755, 449.5145342664596, 61.719565223739664], "content": "employees live, work, and care for their", "level": 1}, {"type": "text", "bbox": [418.24999993176857, 62.26826725458284, 452.2936044646462, 67.26739131069621], "content": "families is a serious", "level": 1}, {"type": "text", "bbox": [182.49, 68.42807499999992, 472.54291496399986, 256.74238820000005], "content": "Table 7: Visualizations of layout output from SmolDocling and QwenVL2.5 compared to the DocLayNet ground truth. Examples are chosen to be representative of different layout styles and features. The prediction results however do not represent a generalizable measure of the model's performance on inputs with similar features. (1) Multi-column pages are handled by SmolDocling and Qwen2.5-VL, with some recall errors in the latter. (2) A manual page with terminal output shows poor bounding box recall on SmolDocling and label confusion in Qwen2.5-VL. (3) Lists with nesting are handled well in SmolDocling but confuse Qwen2.5-VL. (4) Both SmolDocling and Qwen2.5-VL reconstruct equations well, however with different annotation conventions on including or excluding the equation index. (5) On a portrait page with colorful elements and gradient background, SmolDocling creates less accurate bounding-boxes and Qwen2.5-VL suffers low recall. (6) On a report page with tables and diagrams, SmolDocling output exhibits some repetition loop, fabricating non-existent text cells (bottom left), while Qwen2.5-VL is confused between tables and pictures.", "level": 1}, {"type": "text", "bbox": [261.23255833246395, 258.66739134561203, 267.48546513178036, 262.1347824980835], "content": "340.9", "level": 1}, {"type": "text", "bbox": [348.77325654852, 24.271739204121445, 402.9651154759288, 27.73913035659291], "content": "DEVOTION TO SERVICE", "level": 1}, {"type": "text", "bbox": [441.17732585885165, 262.13478264996, 446.7354650240941, 265.6021738024315], "content": "22%", "level": 1}, {"type": "picture", "bbox": [70.86924743652344, 266.0121765136719, 585.3001098632812, 597.5089721679688], "content": "", "level": 1}]}]


id: fb2fef96-1ee7-46f7-8190-51a3e593d4cd
document_id: f8976eb8-7578-4af8-a8f8-e7d3bbc787aa
status: COMPLETED
started_at: 2026-03-21 08:32:44.443421+00:00
completed_at: 2026-03-21 08:32:47.876695+00:00
created_at: 2026-03-21 08:32:44.437338+00:00
content_markdown:
<!-- image -->

NIVEAU 1

<!-- image -->

## Découvrir - Premiers pas avec l'IA

Ces fiches répondent aux besoins les plus exprimés lors de notre rencontre. Chaque prompt est prêt à copier-coller.

## Poser une bonne question à l'IA

Beaucoup ne savent pas par où commencer pour utiliser l'IA comme un véritable assistant au quotidien.

```
PROMPT PRÊT À L'EMPLOI
```

Tu es un expert en réglementation des spiritueux en France. Explique-moi en termes simples les obligations d'étiquetage pour un Cognac VS destiné à l'export au Japon. Précise les mentions obligatoires et les pièges courants.

💡

Toujours commencer par donner un rôle à l'IA, puis être spécifique sur ce que vous voulez.

<!-- image -->

## Comprendre les hallucinations

L'IA peut inventer des réponses quand elle manque d'information - comment s'en protéger ?

```
PROMPT PRÊT À L'EMPLOI
```

Quelles sont les règles d'étiquetage pour l'export de Cognac au Brésil ? Pour chaque règle que tu cites, indique ta source (nom du texte réglementaire, date, article). Si tu n'es pas sûr d'une information, dis-le explicitement plutôt que d'inventer.

<!-- image -->

- 💡 L'IA ne "ment" pas - elle prédit le mot suivant et peut se tromper. Demander des sources force l'IA à être plus prudente. Toujours vérifier les réponses sur des sujets réglementaires, même avec ces indications, l'IA peut halluciner.

## 3 structures de prompts à retenir

Comment formuler ses demandes pour obtenir des résultats vraiment exploitables ?

```
PROMPT PRÊT À L'EMPLOI STRUCTURE 1 - LE RÔLE Tu es [rôle]. [Ta demande spécifique]. STRUCTURE 2 - L'EXEMPLE Voici un exemple de ce que je veux : [coller un exemple]. Fais la même chose pour [nouveau sujet]. STRUCTURE 3 - LE FORMAT Réponds sous forme de [tableau / liste à puces / email de 5 lignes]. Inclus [éléments précis].
```

💡

Ces 3 structures couvrent 90% des besoins quotidiens. Combinez-les pour plus de précision.

## Rédiger un post LinkedIn premium

Usage fréquent pour rédiger du contenu LinkedIn, mais sans méthode pour un résultat professionnel.

```
Tu es un expert en communication LinkedIn pour le secteur des vins et spiritueux. Reformule ce texte en post LinkedIn engageant :[COLLER VOTRE TEXTE ICI] Contraintes : - Accroche forte en première ligne (sans emoji) - Maximum 1300 caractères - Ton professionnel mais chaleureux - Terminer par une question ouverte pour générer des commentaires - Pas de hashtags dans le corps du texte, 3 hashtags max à la fin
```

- L'IA vous donne un premier jet. Relisez toujours et ajoutez votre touche personnelle - c'est ce qui fait la différence.

```
PROMPT PRÊT À L'EMPLOI 💡
```

## Synthétiser un compte-rendu

Besoin de transformer rapidement un long compte-rendu en décisions et actions claires.

```
PROMPT PRÊT À L'EMPLOI Voici le compte-rendu d'une réunion :[COLLER LE CR ICI] Résume-le en 3 blocs : 1. DÉCISIONS PRISES (maximum 5, format : "Qui - Quoi - Échéance") 2. ACTIONS À MENER (maximum 5, même format) 3. POINTS EN SUSPENS (ce qui reste à trancher)Sois factuel, pas de reformulation littéraire. 💡 Fonctionne aussi pour des notes de dégustation, des rapports de visite fournisseur, etc.
```

<!-- image -->

Faites attention aux informations que vous mettez dans l'IA ! Particulièrement avec ce prompt !

## Refaire son CV aux normes ATS

Adapter son CV pour passer les filtres automatiques des recruteurs.

```
PROMPT PRÊT À L'EMPLOI Tu es un expert en recrutement et en optimisation de CV pour les systèmes ATS (Applicant Tracking Systems). Voici mon CV actuel :[COLLER LE CV] (ou mettre en pj si pdf) Et voici l'offre d'emploi visée :[COLLER L'OFFRE] Reformule mon CV pour : - Reprendre les mots-clés exacts de l'offre - Format simple (pas de colonnes, pas de tableaux) - Titres de section standards (Expérience, Formation, Compétences) - Chaque poste : 3 résultats mesurables 💡 Hors spiritueux, mais très utile au quotidien. L'ATS est un logiciel qui filtre les CV avant qu'un humain ne les lise.
```

<!-- image -->

NIVEAU 2

<!-- image -->

## Organiser - Intégrer l'IA dans ses routines

Ces cas d'usages demandent un peu plus de pratique ou l'utilisation d'outils complémentaires.

## Améliorer ses présentations

Le fond est souvent prêt, mais la mise en forme des slides prend un temps disproportionné.

PROMPT PRÊT À L'EMPLOI

```
Voici le contenu brut de ma présentation (texte uniquement) :[COLLER LE CONTENU]
```

```
Pour chaque slide : 1. Reformule le titre pour qu'il soit percutant (max 8 mots) 2. Réduit le contenu à 3 points maximum
```

3. Suggère un visuel ou une donnée chiffrée qui renforcerait le message

<!-- image -->

💡 Pour la mise en forme visuelle, essayez Gamma.app (gratuit) ou Canva IA - ils transforment un texte en slides directement.

<!-- image -->

## Des outils existent déjà pour automatiser la mise en forme - et ils sont accessibles.

Outils disponibles aujourd'hui :

- Copilot dans PowerPoint - reformule, restructure et met en forme vos slides (Microsoft 365)
- Gamma.app - collez du texte, l'IA génère des slides complètes (gratuit)
- Canva IA - design automatique, visuels, réseaux sociaux (freemium)

## Veille réglementaire ciblée

Suivre les évolutions réglementaires par marché sans y passer des heures.

## PROMPT PRÊT À L'EMPLOI

Outil recommandé : Perplexity.ai (gratuit)

Exemple de recherche :"Quelles sont les nouvelles réglementations 2025 sur l'export de spiritueux vers les États-Unis ? Cite uniquement des sources officielles (FDA, TTB, douanes)."

<!-- image -->

💡 Perplexity cite ses sources automatiquement. Pour une veille récurrente, créez un espace dédié ("Collections") et posez la même question chaque mois. Avec le mode payant (comme sur ChatGPT) vous pouvez automatiser ces tâches de manière journalière ou hebdomadaire.

## Sélectionner des outils

Les outils gratuit ne permettent que des usages limités

| Gratuit                        |                                   | Payant (~20 € /mois)   |
|--------------------------------|-----------------------------------|------------------------|
| GPT-4o mini, limité en usage   | GPT-4o illimité, fichiers, images | ChatGPT                |
| Sonnet, limité en messages     | Opus, projets, fichiers longs     | Claude                 |
| 5 recherches pro/jour          | Illimité, sources multiples       | Perplexity             |
| Templates de base + IA limitée | Brand kit, Magic Design illimité  | Canva                  |

💡

<!-- image -->

Conseil : commencez en gratuit. Si vous l'utilisez plus de 3 fois par semaine, l'abonnement se rentabilise vite.

## Questions légitimes, réponses honnêtes

## L'IA consomme beaucoup d'énergie - est-ce compatible avec le développement durable ?

- → C'est une préoccupation légitime. Un échange avec ChatGPT consomme environ 10x plus d'énergie qu'une recherche Google. Mais à l'échelle d'un usage professionnel (quelques dizaines de requêtes par jour), l'impact reste comparable à celui d'un trajet en voiture de 2 km. Le vrai sujet est l'entraînement des modèles, pas l'usage quotidien. Les fournisseurs investissent massivement dans l'efficacité énergétique.

## Sans abonnement payant, l'IA reste trop limitée pour être vraiment utile.

- → Partiellement vrai. La version gratuite de ChatGPT ou Claude suffit largement pour découvrir et traiter 80% des cas d'usage présentés ici (posts LinkedIn, synthèses, prompts simples). L'abonnement devient utile quand on traite des fichiers longs, qu'on veut plus de fiabilité ou qu'on l'intègre dans son quotidien. Ne payez pas avant d'avoir testé.

QUESTIONS ? ENVIE D'ALLER PLUS LOIN ?

## Pier-Jean Malandrino

CTO @ SCUB - Ambassadeur IA, Ministère de l'Économie linkedin.com/in/pier-jean-malandrino pierjean.malandrino@scub.net

06 58 27 88 49

content_html:
<!DOCTYPE html>
<html>
<head>
<meta charset="UTF-8"/>
<title>fa70637f-9474-47f1-974d-718719bfe3eb</title>
<meta name="generator" content="Docling HTML Serializer"/>
<style>
    html {
        background-color: #f5f5f5;
        font-family: Arial, sans-serif;
        line-height: 1.6;
    }
    body {
        max-width: 800px;
        margin: 0 auto;
        padding: 2rem;
        background-color: white;
        box-shadow: 0 0 10px rgba(0,0,0,0.1);
    }
    h1, h2, h3, h4, h5, h6 {
        color: #333;
        margin-top: 1.5em;
        margin-bottom: 0.5em;
    }
    h1 {
        font-size: 2em;
        border-bottom: 1px solid #eee;
        padding-bottom: 0.3em;
    }
    table {
        border-collapse: collapse;
        margin: 1em 0;
        width: 100%;
    }
    th, td {
        border: 1px solid #ddd;
        padding: 8px;
        text-align: left;
    }
    th {
        background-color: #f2f2f2;
        font-weight: bold;
    }
    figure {
        margin: 1.5em 0;
        text-align: center;
    }
    figcaption {
        color: #666;
        font-style: italic;
        margin-top: 0.5em;
    }
    img {
        max-width: 100%;
        height: auto;
    }
    pre {
        background-color: #f6f8fa;
        border-radius: 3px;
        padding: 1em;
        overflow: auto;
    }
    code {
        font-family: monospace;
        background-color: #f6f8fa;
        padding: 0.2em 0.4em;
        border-radius: 3px;
    }
    pre code {
        background-color: transparent;
        padding: 0;
    }
    .formula {
        text-align: center;
        padding: 0.5em;
        margin: 1em 0;
        background-color: #f9f9f9;
    }
    .formula-not-decoded {
        text-align: center;
        padding: 0.5em;
        margin: 1em 0;
        background: repeating-linear-gradient(
            45deg,
            #f0f0f0,
            #f0f0f0 10px,
            #f9f9f9 10px,
            #f9f9f9 20px
        );
    }
    .page-break {
        page-break-after: always;
        border-top: 1px dashed #ccc;
        margin: 2em 0;
    }
    .key-value-region {
        background-color: #f9f9f9;
        padding: 1em;
        border-radius: 4px;
        margin: 1em 0;
    }
    .key-value-region dt {
        font-weight: bold;
    }
    .key-value-region dd {
        margin-left: 1em;
        margin-bottom: 0.5em;
    }
    .form-container {
        border: 1px solid #ddd;
        padding: 1em;
        border-radius: 4px;
        margin: 1em 0;
    }
    .form-item {
        margin-bottom: 0.5em;
    }
    .image-classification {
        font-size: 0.9em;
        color: #666;
        margin-top: 0.5em;
    }
</style>
</head>
<body>
<div class='page'>
<p>NIVEAU 1</p>
<h2>Découvrir - Premiers pas avec l'IA</h2>
<p>Ces fiches répondent aux besoins les plus exprimés lors de notre rencontre. Chaque prompt est prêt à copier-coller.</p>
<h2>Poser une bonne question à l'IA</h2>
<p>Beaucoup ne savent pas par où commencer pour utiliser l'IA comme un véritable assistant au quotidien.</p>
<pre><code>PROMPT PRÊT À L'EMPLOI</code></pre>
<p>Tu es un expert en réglementation des spiritueux en France. Explique-moi en termes simples les obligations d'étiquetage pour un Cognac VS destiné à l'export au Japon. Précise les mentions obligatoires et les pièges courants.</p>
<p>💡</p>
<p>Toujours commencer par donner un rôle à l'IA, puis être spécifique sur ce que vous voulez.</p>
<h2>Comprendre les hallucinations</h2>
<p>L'IA peut inventer des réponses quand elle manque d'information - comment s'en protéger ?</p>
<pre><code>PROMPT PRÊT À L'EMPLOI</code></pre>
<p>Quelles sont les règles d'étiquetage pour l'export de Cognac au Brésil ? Pour chaque règle que tu cites, indique ta source (nom du texte réglementaire, date, article). Si tu n'es pas sûr d'une information, dis-le explicitement plutôt que d'inventer.</p>
<ul>
<li>💡 L'IA ne "ment" pas - elle prédit le mot suivant et peut se tromper. Demander des sources force l'IA à être plus prudente. Toujours vérifier les réponses sur des sujets réglementaires, même avec ces indications, l'IA peut halluciner.</li>
</ul>
<h2>3 structures de prompts à retenir</h2>
<p>Comment formuler ses demandes pour obtenir des résultats vraiment exploitables ?</p>
<pre><code>PROMPT PRÊT À L'EMPLOI STRUCTURE 1 - LE RÔLE Tu es [rôle]. [Ta demande spécifique]. STRUCTURE 2 - L'EXEMPLE Voici un exemple de ce que je veux : [coller un exemple]. Fais la même chose pour [nouveau sujet]. STRUCTURE 3 - LE FORMAT Réponds sous forme de [tableau / liste à puces / email de 5 lignes]. Inclus [éléments précis].</code></pre>
<p>💡</p>
<p>Ces 3 structures couvrent 90% des besoins quotidiens. Combinez-les pour plus de précision.</p>
<h2>Rédiger un post LinkedIn premium</h2>
<p>Usage fréquent pour rédiger du contenu LinkedIn, mais sans méthode pour un résultat professionnel.</p>
<pre><code>Tu es un expert en communication LinkedIn pour le secteur des vins et spiritueux. Reformule ce texte en post LinkedIn engageant :[COLLER VOTRE TEXTE ICI] Contraintes : - Accroche forte en première ligne (sans emoji) - Maximum 1300 caractères - Ton professionnel mais chaleureux - Terminer par une question ouverte pour générer des commentaires - Pas de hashtags dans le corps du texte, 3 hashtags max à la fin</code></pre>
<ul>
<li>L'IA vous donne un premier jet. Relisez toujours et ajoutez votre touche personnelle - c'est ce qui fait la différence.</li>
</ul>
<pre><code>PROMPT PRÊT À L'EMPLOI 💡</code></pre>
<h2>Synthétiser un compte-rendu</h2>
<p>Besoin de transformer rapidement un long compte-rendu en décisions et actions claires.</p>
<pre><code>PROMPT PRÊT À L'EMPLOI Voici le compte-rendu d'une réunion :[COLLER LE CR ICI] Résume-le en 3 blocs : 1. DÉCISIONS PRISES (maximum 5, format : "Qui - Quoi - Échéance") 2. ACTIONS À MENER (maximum 5, même format) 3. POINTS EN SUSPENS (ce qui reste à trancher)Sois factuel, pas de reformulation littéraire. 💡 Fonctionne aussi pour des notes de dégustation, des rapports de visite fournisseur, etc.</code></pre>
<p>Faites attention aux informations que vous mettez dans l'IA ! Particulièrement avec ce prompt !</p>
<h2>Refaire son CV aux normes ATS</h2>
<p>Adapter son CV pour passer les filtres automatiques des recruteurs.</p>
<pre><code>PROMPT PRÊT À L'EMPLOI Tu es un expert en recrutement et en optimisation de CV pour les systèmes ATS (Applicant Tracking Systems). Voici mon CV actuel :[COLLER LE CV] (ou mettre en pj si pdf) Et voici l'offre d'emploi visée :[COLLER L'OFFRE] Reformule mon CV pour : - Reprendre les mots-clés exacts de l'offre - Format simple (pas de colonnes, pas de tableaux) - Titres de section standards (Expérience, Formation, Compétences) - Chaque poste : 3 résultats mesurables 💡 Hors spiritueux, mais très utile au quotidien. L'ATS est un logiciel qui filtre les CV avant qu'un humain ne les lise.</code></pre>
<p>NIVEAU 2</p>
<h2>Organiser - Intégrer l'IA dans ses routines</h2>
<p>Ces cas d'usages demandent un peu plus de pratique ou l'utilisation d'outils complémentaires.</p>
<h2>Améliorer ses présentations</h2>
<p>Le fond est souvent prêt, mais la mise en forme des slides prend un temps disproportionné.</p>
<p>PROMPT PRÊT À L'EMPLOI</p>
<pre><code>Voici le contenu brut de ma présentation (texte uniquement) :[COLLER LE CONTENU]</code></pre>
<pre><code>Pour chaque slide : 1. Reformule le titre pour qu'il soit percutant (max 8 mots) 2. Réduit le contenu à 3 points maximum</code></pre>
<ol>
<li style="list-style-type: '3. ';">Suggère un visuel ou une donnée chiffrée qui renforcerait le message</li>
</ol>
<p>💡 Pour la mise en forme visuelle, essayez Gamma.app (gratuit) ou Canva IA - ils transforment un texte en slides directement.</p>
<h2>Des outils existent déjà pour automatiser la mise en forme - et ils sont accessibles.</h2>
<p>Outils disponibles aujourd'hui :</p>
<ul>
<li style="list-style-type: '· ';">Copilot dans PowerPoint - reformule, restructure et met en forme vos slides (Microsoft 365)</li>
<li style="list-style-type: '· ';">Gamma.app - collez du texte, l'IA génère des slides complètes (gratuit)</li>
<li style="list-style-type: '· ';">Canva IA - design automatique, visuels, réseaux sociaux (freemium)</li>
</ul>
<h2>Veille réglementaire ciblée</h2>
<p>Suivre les évolutions réglementaires par marché sans y passer des heures.</p>
<h2>PROMPT PRÊT À L'EMPLOI</h2>
<p>Outil recommandé : Perplexity.ai (gratuit)</p>
<p>Exemple de recherche :"Quelles sont les nouvelles réglementations 2025 sur l'export de spiritueux vers les États-Unis ? Cite uniquement des sources officielles (FDA, TTB, douanes)."</p>
<p>💡 Perplexity cite ses sources automatiquement. Pour une veille récurrente, créez un espace dédié ("Collections") et posez la même question chaque mois. Avec le mode payant (comme sur ChatGPT) vous pouvez automatiser ces tâches de manière journalière ou hebdomadaire.</p>
<h2>Sélectionner des outils</h2>
<p>Les outils gratuit ne permettent que des usages limités</p>
<table><tbody><tr><td>Gratuit</td><td></td><th>Payant (~20 € /mois)</th></tr><tr><td>GPT-4o mini, limité en usage</td><td>GPT-4o illimité, fichiers, images</td><td>ChatGPT</td></tr><tr><td>Sonnet, limité en messages</td><td>Opus, projets, fichiers longs</td><td>Claude</td></tr><tr><td>5 recherches pro/jour</td><td>Illimité, sources multiples</td><td>Perplexity</td></tr><tr><td>Templates de base + IA limitée</td><td>Brand kit, Magic Design illimité</td><td>Canva</td></tr></tbody></table>
<p>💡</p>
<p>Conseil : commencez en gratuit. Si vous l'utilisez plus de 3 fois par semaine, l'abonnement se rentabilise vite.</p>
<h2>Questions légitimes, réponses honnêtes</h2>
<h2>L'IA consomme beaucoup d'énergie - est-ce compatible avec le développement durable ?</h2>
<ul>
<li>→ C'est une préoccupation légitime. Un échange avec ChatGPT consomme environ 10x plus d'énergie qu'une recherche Google. Mais à l'échelle d'un usage professionnel (quelques dizaines de requêtes par jour), l'impact reste comparable à celui d'un trajet en voiture de 2 km. Le vrai sujet est l'entraînement des modèles, pas l'usage quotidien. Les fournisseurs investissent massivement dans l'efficacité énergétique.</li>
</ul>
<h2>Sans abonnement payant, l'IA reste trop limitée pour être vraiment utile.</h2>
<ul>
<li>→ Partiellement vrai. La version gratuite de ChatGPT ou Claude suffit largement pour découvrir et traiter 80% des cas d'usage présentés ici (posts LinkedIn, synthèses, prompts simples). L'abonnement devient utile quand on traite des fichiers longs, qu'on veut plus de fiabilité ou qu'on l'intègre dans son quotidien. Ne payez pas avant d'avoir testé.</li>
</ul>
<p>QUESTIONS ? ENVIE D'ALLER PLUS LOIN ?</p>
<h2>Pier-Jean Malandrino</h2>
<p>CTO @ SCUB - Ambassadeur IA, Ministère de l'Économie linkedin.com/in/pier-jean-malandrino pierjean.malandrino@scub.net</p>
<p>06 58 27 88 49</p>
</div>
</body>
</html>

pages_json:
[{"page_number": 1, "width": 596.0, "height": 842.0, "elements": [{"type": "picture", "bbox": [70.57894134521484, 78.7155990600586, 524.7957153320312, 336.5411071777344], "content": "", "level": 1}]}, {"page_number": 2, "width": 596.0, "height": 842.0, "elements": [{"type": "text", "bbox": [72.0, 38.20312512045405, 107.15743240745117, 45.07755002703436], "content": "NIVEAU 1", "level": 1}, {"type": "picture", "bbox": [71.85594940185547, 47.98542404174805, 96.07481384277344, 73.63606262207031], "content": "", "level": 1}, {"type": "section_header", "bbox": [96.94921875, 55.144531328417884, 389.7102635470215, 71.99348930719395], "content": "D\u00e9couvrir - Premiers pas avec l'IA", "level": 1}, {"type": "text", "bbox": [72.0, 95.50878912704104, 519.4168989765185, 118.20345095765299], "content": "Ces fiches r\u00e9pondent aux besoins les plus exprim\u00e9s lors de notre rencontre. Chaque prompt est pr\u00eat \u00e0 copier-coller.", "level": 1}, {"type": "section_header", "bbox": [78.75, 142.95312521999995, 236.4960975, 153.06250025999998], "content": "Poser une bonne question \u00e0 l'IA", "level": 1}, {"type": "text", "bbox": [78.75, 161.02636717895507, 496.75691163749997, 170.963805695759], "content": "Beaucoup ne savent pas par o\u00f9 commencer pour utiliser l'IA comme un v\u00e9ritable assistant au quotidien.", "level": 1}, {"type": "code", "bbox": [78.75, 188.20410475454594, 163.37482091754885, 194.21922614500534], "content": "PROMPT PR\u00caT \u00c0 L'EMPLOI", "level": 1}, {"type": "text", "bbox": [88.5, 198.95117571499998, 504.15234470999997, 227.76107349322376], "content": "Tu es un expert en r\u00e9glementation des spiritueux en France. Explique-moi en termes simples les obligations d'\u00e9tiquetage pour un Cognac VS destin\u00e9 \u00e0 l'export au Japon. Pr\u00e9cise les mentions obligatoires et les pi\u00e8ges courants.", "level": 1}, {"type": "text", "bbox": [78.75, 232.48241780499995, 89.97933208007812, 243.05565999249995], "content": "\ud83d\udca1", "level": 1}, {"type": "text", "bbox": [89.9765625, 233.87304262704106, 423.95605147651855, 243.13996995765297], "content": "Toujours commencer par donner un r\u00f4le \u00e0 l'IA, puis \u00eatre sp\u00e9cifique sur ce que vous voulez.", "level": 1}, {"type": "picture", "bbox": [80.31708526611328, 231.4993133544922, 87.96992492675781, 243.3762969970703], "content": "", "level": 1}, {"type": "section_header", "bbox": [78.75, 281.06738622, 229.88085978, 291.17676126000003], "content": "Comprendre les hallucinations", "level": 1}, {"type": "text", "bbox": [78.75, 299.14062117895503, 457.656859875, 309.07805969575895], "content": "L'IA peut inventer des r\u00e9ponses quand elle manque d'information - comment s'en prot\u00e9ger ?", "level": 1}, {"type": "code", "bbox": [78.75, 326.3183647545459, 163.37482091754885, 332.3334861450053], "content": "PROMPT PR\u00caT \u00c0 L'EMPLOI", "level": 1}, {"type": "text", "bbox": [88.5, 337.065425715, 509.10058971, 365.87533349322376], "content": "Quelles sont les r\u00e8gles d'\u00e9tiquetage pour l'export de Cognac au Br\u00e9sil ? Pour chaque r\u00e8gle que tu cites, indique ta source (nom du texte r\u00e9glementaire, date, article). Si tu n'es pas s\u00fbr d'une information, dis-le explicitement plut\u00f4t que d'inventer.", "level": 1}, {"type": "picture", "bbox": [79.81617736816406, 369.4933166503906, 88.04532623291016, 380.59600830078125], "content": "", "level": 1}, {"type": "list", "bbox": [78.75, 370.596677805, 510.6879864765185, 392.240559957653], "content": "\ud83d\udca1 L'IA ne \"ment\" pas - elle pr\u00e9dit le mot suivant et peut se tromper. Demander des sources force l'IA \u00e0 \u00eatre plus prudente. Toujours v\u00e9rifier les r\u00e9ponses sur des sujets r\u00e9glementaires, m\u00eame avec ces indications, l'IA peut halluciner.", "level": 2}, {"type": "section_header", "bbox": [78.75, 430.16796622, 242.806642485, 440.27734126], "content": "3 structures de prompts \u00e0 retenir", "level": 1}, {"type": "text", "bbox": [78.75, 448.2412111789551, 412.92341737500004, 458.17864969575896], "content": "Comment formuler ses demandes pour obtenir des r\u00e9sultats vraiment exploitables ?", "level": 1}, {"type": "code", "bbox": [78.75, 475.4189447545459, 504.15234470999997, 557.1282634932238], "content": "PROMPT PR\u00caT \u00c0 L'EMPLOI STRUCTURE 1 - LE R\u00d4LE Tu es [r\u00f4le]. [Ta demande sp\u00e9cifique]. STRUCTURE 2 - L'EXEMPLE Voici un exemple de ce que je veux : [coller un exemple]. Fais la m\u00eame chose pour [nouveau sujet]. STRUCTURE 3 - LE FORMAT R\u00e9ponds sous forme de [tableau / liste \u00e0 puces / email de 5 lignes]. Inclus [\u00e9l\u00e9ments pr\u00e9cis].", "level": 1}, {"type": "text", "bbox": [78.75, 561.849607805, 89.97933208007812, 572.4228499925], "content": "\ud83d\udca1", "level": 1}, {"type": "text", "bbox": [89.9765625, 563.240232627041, 428.7504864765186, 572.507159957653], "content": "Ces 3 structures couvrent 90% des besoins quotidiens. Combinez-les pour plus de pr\u00e9cision.", "level": 1}, {"type": "section_header", "bbox": [78.75, 610.43456622, 249.5917968, 620.54394126], "content": "R\u00e9diger un post LinkedIn premium", "level": 1}, {"type": "text", "bbox": [78.75, 628.5078111789551, 484.8817000875, 638.445249695759], "content": "Usage fr\u00e9quent pour r\u00e9diger du contenu LinkedIn, mais sans m\u00e9thode pour un r\u00e9sultat professionnel.", "level": 1}, {"type": "code", "bbox": [88.5, 666.4326157150001, 494.25586221000003, 747.9329434932238], "content": "Tu es un expert en communication LinkedIn pour le secteur des vins et spiritueux. Reformule ce texte en post LinkedIn engageant :[COLLER VOTRE TEXTE ICI] Contraintes : - Accroche forte en premi\u00e8re ligne (sans emoji) - Maximum 1300 caract\u00e8res - Ton professionnel mais chaleureux - Terminer par une question ouverte pour g\u00e9n\u00e9rer des commentaires - Pas de hashtags dans le corps du texte, 3 hashtags max \u00e0 la fin", "level": 1}, {"type": "list", "bbox": [89.9765625, 754.0449226270409, 516.6762714765187, 763.311849957653], "content": "L'IA vous donne un premier jet. Relisez toujours et ajoutez votre touche personnelle - c'est ce qui fait la diff\u00e9rence.", "level": 2}, {"type": "code", "bbox": [78.75, 655.6855447545458, 163.37482091754885, 763.2275399924999], "content": "PROMPT PR\u00caT \u00c0 L'EMPLOI \ud83d\udca1", "level": 1}]}, {"page_number": 3, "width": 596.0, "height": 842.0, "elements": [{"type": "section_header", "bbox": [78.75, 45.316406219999976, 224.11523463, 55.42578126000001], "content": "Synth\u00e9tiser un compte-rendu", "level": 1}, {"type": "text", "bbox": [78.75, 63.38964917895498, 429.3809199374999, 73.3270876957589], "content": "Besoin de transformer rapidement un long compte-rendu en d\u00e9cisions et actions claires.", "level": 1}, {"type": "code", "bbox": [78.75, 90.56738275454586, 489.30761721, 177.03320299249992], "content": "PROMPT PR\u00caT \u00c0 L'EMPLOI Voici le compte-rendu d'une r\u00e9union :[COLLER LE CR ICI] R\u00e9sume-le en 3 blocs : 1. D\u00c9CISIONS PRISES (maximum 5, format : \"Qui - Quoi - \u00c9ch\u00e9ance\") 2. ACTIONS \u00c0 MENER (maximum 5, m\u00eame format) 3. POINTS EN SUSPENS (ce qui reste \u00e0 trancher)Sois factuel, pas de reformulation litt\u00e9raire. \ud83d\udca1 Fonctionne aussi pour des notes de d\u00e9gustation, des rapports de visite fournisseur, etc.", "level": 1}, {"type": "picture", "bbox": [79.8282470703125, 164.94406127929688, 88.10155487060547, 177.43502807617188], "content": "", "level": 1}, {"type": "text", "bbox": [78.75, 189.82324262704105, 436.42480147651855, 199.09016995765296], "content": "Faites attention aux informations que vous mettez dans l'IA ! Particuli\u00e8rement avec ce prompt !", "level": 1}, {"type": "section_header", "bbox": [78.75, 237.01757622000002, 232.130859375, 247.12695126000006], "content": "Refaire son CV aux normes ATS", "level": 1}, {"type": "text", "bbox": [78.75, 255.09082117895503, 349.7976506624999, 265.02825969575895], "content": "Adapter son CV pour passer les filtres automatiques des recruteurs.", "level": 1}, {"type": "code", "bbox": [78.75, 282.2685547545459, 500.6406264765185, 432.047199957653], "content": "PROMPT PR\u00caT \u00c0 L'EMPLOI Tu es un expert en recrutement et en optimisation de CV pour les syst\u00e8mes ATS (Applicant Tracking Systems). Voici mon CV actuel :[COLLER LE CV] (ou mettre en pj si pdf) Et voici l'offre d'emploi vis\u00e9e :[COLLER L'OFFRE] Reformule mon CV pour : - Reprendre les mots-cl\u00e9s exacts de l'offre - Format simple (pas de colonnes, pas de tableaux) - Titres de section standards (Exp\u00e9rience, Formation, Comp\u00e9tences) - Chaque poste : 3 r\u00e9sultats mesurables \ud83d\udca1 Hors spiritueux, mais tr\u00e8s utile au quotidien. L'ATS est un logiciel qui filtre les CV avant qu'un humain ne les lise.", "level": 1}, {"type": "picture", "bbox": [80.23975372314453, 420.2494201660156, 88.04841613769531, 432.0928955078125], "content": "", "level": 1}]}, {"page_number": 4, "width": 596.0, "height": 842.0, "elements": [{"type": "text", "bbox": [72.0, 38.20312512045405, 107.15743240745117, 45.07755002703436], "content": "NIVEAU 2", "level": 1}, {"type": "picture", "bbox": [71.98075866699219, 47.87467575073242, 95.9478988647461, 73.41450500488281], "content": "", "level": 1}, {"type": "section_header", "bbox": [96.94921875, 55.144531328417884, 454.3121285505372, 71.99348930719395], "content": "Organiser - Int\u00e9grer l'IA dans ses routines", "level": 1}, {"type": "text", "bbox": [72.0, 95.50878912704104, 494.04617647651855, 104.77571645765295], "content": "Ces cas d'usages demandent un peu plus de pratique ou l'utilisation d'outils compl\u00e9mentaires.", "level": 1}, {"type": "section_header", "bbox": [78.75, 129.52539021999996, 218.05664103, 139.63476526], "content": "Am\u00e9liorer ses pr\u00e9sentations", "level": 1}, {"type": "text", "bbox": [78.75, 147.59863317895508, 440.4260942625, 157.536071695759], "content": "Le fond est souvent pr\u00eat, mais la mise en forme des slides prend un temps disproportionn\u00e9.", "level": 1}, {"type": "text", "bbox": [78.75, 174.77636475454585, 163.37482091754885, 180.79148614500525], "content": "PROMPT PR\u00caT \u00c0 L'EMPLOI", "level": 1}, {"type": "code", "bbox": [88.5, 185.5234356962501, 484.35937518000014, 193.25716347447383], "content": "Voici le contenu brut de ma pr\u00e9sentation (texte uniquement) :[COLLER LE CONTENU]", "level": 1}, {"type": "code", "bbox": [88.5, 206.59960569625002, 385.3945285500001, 235.40951347447378], "content": "Pour chaque slide : 1. Reformule le titre pour qu'il soit percutant (max 8 mots) 2. R\u00e9duit le contenu \u00e0 3 points maximum", "level": 1}, {"type": "list", "bbox": [88.5, 238.213865715, 439.82519547000015, 245.94759349322385], "content": "Sugg\u00e8re un visuel ou une donn\u00e9e chiffr\u00e9e qui renforcerait le message", "level": 2}, {"type": "picture", "bbox": [80.11357116699219, 249.12884521484375, 87.69322967529297, 260.6999816894531], "content": "", "level": 1}, {"type": "text", "bbox": [78.75, 250.6689478049999, 494.9545879799999, 272.312829957653], "content": "\ud83d\udca1 Pour la mise en forme visuelle, essayez Gamma.app (gratuit) ou Canva IA - ils transforment un texte en slides directement.", "level": 1}, {"type": "picture", "bbox": [78.44112396240234, 304.95550537109375, 92.39714050292969, 319.54486083984375], "content": "", "level": 1}, {"type": "section_header", "bbox": [97.442757, 309.90136909704097, 478.20961293177254, 319.168296427653], "content": "Des outils existent d\u00e9j\u00e0 pour automatiser la mise en forme - et ils sont accessibles.", "level": 1}, {"type": "text", "bbox": [78.75, 325.35839550204105, 206.92011247651857, 334.62532283265307], "content": "Outils disponibles aujourd'hui :", "level": 1}, {"type": "list", "bbox": [96.75, 341.536129127041, 497.5271889765185, 350.803056480153], "content": "Copilot dans PowerPoint - reformule, restructure et met en forme vos slides (Microsoft 365)", "level": 2}, {"type": "list", "bbox": [96.75, 354.96386912704105, 408.9243339765186, 364.230796480153], "content": "Gamma.app - collez du texte, l'IA g\u00e9n\u00e8re des slides compl\u00e8tes (gratuit)", "level": 2}, {"type": "list", "bbox": [96.75, 368.39159912704105, 396.51724147651856, 377.658526480153], "content": "Canva IA - design automatique, visuels, r\u00e9seaux sociaux (freemium)", "level": 2}, {"type": "section_header", "bbox": [78.75, 416.08593621999995, 211.08398474999998, 426.19531126], "content": "Veille r\u00e9glementaire cibl\u00e9e", "level": 1}, {"type": "text", "bbox": [78.75, 434.1591811789551, 373.05030956249993, 444.096619695759], "content": "Suivre les \u00e9volutions r\u00e9glementaires par march\u00e9 sans y passer des heures.", "level": 1}, {"type": "section_header", "bbox": [78.75, 461.3369147545459, 163.37482091754885, 467.3520361450053], "content": "PROMPT PR\u00caT \u00c0 L'EMPLOI", "level": 1}, {"type": "text", "bbox": [88.5, 472.08398569625, 296.32617489000006, 479.8177134744738], "content": "Outil recommand\u00e9 : Perplexity.ai (gratuit)", "level": 1}, {"type": "text", "bbox": [88.5, 482.622065715, 504.15234470999997, 511.4319734932238], "content": "Exemple de recherche :\"Quelles sont les nouvelles r\u00e9glementations 2025 sur l'export de spiritueux vers les \u00c9tats-Unis ? Cite uniquement des sources officielles (FDA, TTB, douanes).\"", "level": 1}, {"type": "picture", "bbox": [79.85903930664062, 515.3345947265625, 88.61640167236328, 526.2371826171875], "content": "", "level": 1}, {"type": "text", "bbox": [78.75, 516.1533178049999, 512.89452798, 548.783529957653], "content": "\ud83d\udca1 Perplexity cite ses sources automatiquement. Pour une veille r\u00e9currente, cr\u00e9ez un espace d\u00e9di\u00e9 (\"Collections\") et posez la m\u00eame question chaque mois. Avec le mode payant (comme sur ChatGPT) vous pouvez automatiser ces t\u00e2ches de mani\u00e8re journali\u00e8re ou hebdomadaire.", "level": 1}, {"type": "section_header", "bbox": [78.75, 615.13867622, 191.64257816999998, 625.24805126], "content": "S\u00e9lectionner des outils", "level": 1}, {"type": "text", "bbox": [78.75, 633.211911178955, 296.62066687770994, 643.1493496957589], "content": "Les outils gratuit ne permettent que des usages limit\u00e9s", "level": 1}, {"type": "table", "bbox": [71.49305725097656, 671.7009887695312, 524.9082641601562, 765.5820922851562], "content": "| Gratuit                        |                                   | Payant (~20 \u20ac /mois)   |\n|--------------------------------|-----------------------------------|------------------------|\n| GPT-4o mini, limit\u00e9 en usage   | GPT-4o illimit\u00e9, fichiers, images | ChatGPT                |\n| Sonnet, limit\u00e9 en messages     | Opus, projets, fichiers longs     | Claude                 |\n| 5 recherches pro/jour          | Illimit\u00e9, sources multiples       | Perplexity             |\n| Templates de base + IA limit\u00e9e | Brand kit, Magic Design illimit\u00e9  | Canva                  |", "level": 1}, {"type": "text", "bbox": [72.0, 772.4082078125, 83.22933208007812, 782.98145], "content": "\ud83d\udca1", "level": 1}, {"type": "picture", "bbox": [73.56270599365234, 771.6131591796875, 80.8139419555664, 783.3615112304688], "content": "", "level": 1}, {"type": "text", "bbox": [83.2265625, 773.7988326270411, 483.3832989765185, 783.065759957653], "content": "Conseil : commencez en gratuit. Si vous l'utilisez plus de 3 fois par semaine, l'abonnement se rentabilise vite.", "level": 1}]}, {"page_number": 5, "width": 596.0, "height": 842.0, "elements": [{"type": "section_header", "bbox": [72.0, 55.14453151591795, 402.5279284755371, 71.9934894946939], "content": "Questions l\u00e9gitimes, r\u00e9ponses honn\u00eates", "level": 1}, {"type": "section_header", "bbox": [78.75, 113.95019517895503, 436.127112375, 123.88763369575895], "content": "L'IA consomme beaucoup d'\u00e9nergie - est-ce compatible avec le d\u00e9veloppement durable ?", "level": 1}, {"type": "list", "bbox": [78.75, 129.4990230939551, 507.88937464348146, 175.59505183265298], "content": "\u2192 C'est une pr\u00e9occupation l\u00e9gitime. Un \u00e9change avec ChatGPT consomme environ 10x plus d'\u00e9nergie qu'une recherche Google. Mais \u00e0 l'\u00e9chelle d'un usage professionnel (quelques dizaines de requ\u00eates par jour), l'impact reste comparable \u00e0 celui d'un trajet en voiture de 2 km. Le vrai sujet est l'entra\u00eenement des mod\u00e8les, pas l'usage quotidien. Les fournisseurs investissent massivement dans l'efficacit\u00e9 \u00e9nerg\u00e9tique.", "level": 2}, {"type": "section_header", "bbox": [78.75, 213.19726117895505, 373.0160225625, 223.13469969575897], "content": "Sans abonnement payant, l'IA reste trop limit\u00e9e pour \u00eatre vraiment utile.", "level": 1}, {"type": "list", "bbox": [78.75, 228.74609809395508, 515.3133396434814, 274.842122832653], "content": "\u2192 Partiellement vrai. La version gratuite de ChatGPT ou Claude suffit largement pour d\u00e9couvrir et traiter 80% des cas d'usage pr\u00e9sent\u00e9s ici (posts LinkedIn, synth\u00e8ses, prompts simples). L'abonnement devient utile quand on traite des fichiers longs, qu'on veut plus de fiabilit\u00e9 ou qu'on l'int\u00e8gre dans son quotidien. Ne payez pas avant d'avoir test\u00e9.", "level": 2}, {"type": "text", "bbox": [87.0, 698.3359343070458, 229.32210019254882, 704.3510556975052], "content": "QUESTIONS ? ENVIE D'ALLER PLUS LOIN ?", "level": 1}, {"type": "section_header", "bbox": [87.0, 714.348630405918, 211.10956167120116, 726.1429008646941], "content": "Pier-Jean Malandrino", "level": 1}, {"type": "text", "bbox": [87.0, 732.4804726270411, 300.22216897651856, 741.747399957653], "content": "CTO @ SCUB - Ambassadeur IA, Minist\u00e8re de l'\u00c9conomie linkedin.com/in/pier-jean-malandrino pierjean.malandrino@scub.net", "level": 1}, {"type": "text", "bbox": [87.0, 745.8837851204541, 245.1934129074511, 762.1254000270344], "content": "CTO @ SCUB - Ambassadeur IA, Minist\u00e8re de l'\u00c9conomie linkedin.com/in/pier-jean-malandrino pierjean.malandrino@scub.net", "level": 1}, {"type": "text", "bbox": [87.0, 764.618165120454, 148.52228530745117, 771.4925900270343], "content": "06 58 27 88 49", "level": 2}]}]


id: 32bfdb55-daf5-4ccd-90dd-5f24a417a952
document_id: 3ac94486-2675-49ad-8d61-b656e6977141
status: COMPLETED
started_at: 2026-03-21 18:43:52.655564+00:00
completed_at: 2026-03-21 18:44:10.427022+00:00
created_at: 2026-03-21 18:43:52.648798+00:00
content_markdown:
<!-- image -->

LADIES WINE Hotel Restaurant L'Yeuse

L'IA au service des Spiritueux Osez VIA

MUR

WIRE SPIRITS

Ladies Wine

<!-- image -->

<!-- image -->

Osez

HIA

<!-- image -->

linkedin.com/in/pier-jean-malandrino

## Pier-jean Malandrino

- CTO SCUB
- Ambassadeur IA Ministère de l'économie et des finances

<!-- image -->

## Notre voyage

PLAN - SECTIONS

- 01 De quoi parle t'on ?
- 02 Des cas d'usages concrets
- 03 Les limitations, les pièges
- 04 Comment démarrer ?

<!-- image -->

*

Bonus

## Icebreaker

Posons la problématique

<!-- image -->

00

Préparation réglementaire et commerciale pour l'exportation

d'un Cognac XO vers le Japon spécifiques Japon

3.1 Mentions obligatoires en japonais

Les obligations d'étiquetage des boissons alcooliques résultent du

· l'Act on Securing of Liquor Tax and on Liquor Business

Associations, qui définit des obligations de base communes à

- toutes les boissons alcooliques (iN Mo) Ai - "labellisation des · la Food Labeling Act, appliquée sous la supervision de la CAA,

## 1. Qualification réglementaire du produit 1.1 Catégorie juridique au Japon Un Cognac XO de 40% vol., élaboré par distillation de vin de raisin, cumul de : boissons alcooliques") ;[21][7]

entre dans la catégorie des boissons alcooliques distillées au sens de la Liquor Tax Act japonaise (distilled alcoholic beverages).(1]

Au plan douanier, il relève en principe de la position HS 2208.20 -

## "Spirits obtained by distilling grape wine or grape marc" du tarif

japonais, qui couvre les eaux-de-vie de vin et assimilées.[2][3]

Pour la fiscalité intérieure, ce produit est imposé dans la soumise à un barème spécifique de liquor tax.(4][5]

sous-catégorie "Whisky / Brandy / Spirits" des boissons distillées, pour les aspects « denrée alimentaire » (opérateur responsable,

additifs, OGM, allégations nutritionnelles, etc.).[9][7]

La NTA met à disposition une "Check-list de la méthode de

- présentation des boissons alcooliques (éléments communs)" qui énumère de manière détaillée les mentions à vérifier pour tout alcool. [7]

À faire valider localement: la sous-position à 9/12 chiffres Pour un Cognac XO importé, les mentions suivantes doivent figurer en japonais sur l'étiquette (ou une contre-étiquette) :

exacte (2208.20.xxx) et la confirmation écrite de la catégorie fiscale « whisky/brandy » auprès de Japan Customs ou d'un

agent en douane, en utilisant l'outil webTARIFF[6]|

1.2 Principaux textes et organismes compétents

Les textes et autorités suivants encadrent l'importation et la commercialisation au Japon d'un Cognac XO :

· Liquor Tax Act (iMBiA) : définit les catégories de boissons alcooliques (dont les boissons distillées) et fixe les taux de liquor

tax selon la catégorie et le degré alcoolique. Compétence :

<!-- image -->

<!-- image -->

1. Catégorie de boisson alcoolique (iNAO) MEI)

· Mention claire de la catégorie légale (par exemple, terme japonais correspondant à la catégorie retenue pour la

liquor tax - whisky/brandy).[7]

· Doit apparaître sur la face principale, à proximité de la marque, avec une taille minimale de police dépendant du

- volume de la bouteille (pour 700 ml, plusieurs points de

corps sont précisés dans la grille de la NTA).[7)

2. Teneur en alcool (7 N3-N5)

· Affichage en chiffres arabes, suivi de l'unité « I » ou « % »

(ex.: 727-14) 40 1(€).[7)

· Doit être exprimé dans une plage d'un degré maximum pour la catégorie fiscale correspondante.[7]

3. Contenu / volume (P%ht)

<!-- image -->

Un néophyte peut produire des résultats qui ont l'air extrêmement qualitatifs

## 00

<!-- image -->

Prenons du recul

Ce néophyte n'est en revanche pas en mesure de comprendre ce travail qui comporte des erreurs et encore moins de les corriger

<!-- image -->

## 00

BUSINESS 2025

MIT NANDA

Aditya Challapally

Chris Pease

Ramesh Raskar

Pradyumna Chari

July 2025

## 95 %

de projets Gen AI ne produisent aucun ROI

(Return On Investment)

'The GenAI Divide: State of AI in Business 2025' publié par le MIT en 2025'

<!-- image -->

```
De quoi parle t'on ?
```

## 01

<!-- image -->

## L'Histoire

Pourquoi l'IA change tout aujourd'hui ?

Annual attendance at major artificial intelligence conferences

<!-- image -->

## 01

80,000

60,000

40,000

20,000

<!-- image -->

2012

2010

Our World

<!-- image -->

"2017, parution du papier sur les architectures transformer

L'intérêt académique

'2017, parution du papier sur les architectures transformers, suscite un engouement très fort de la communauté scientifique'

Annual global corporate investment in artificial intelligence, by type

<!-- image -->

## 01

$350 billion

$300 billion

$250 billion

$200 billion

$150 billion

$100 billion

$50 billion

$0

2013

2014

2015

Our World

<!-- image -->

Augmentation drastique des investissements autant publiques

Les investissements

Augmentation drastique des investissements autant publiques de que privés.

Exponential growth of computation in the training of notable Al systems

<!-- image -->

## 01

10 billion

100 million

1 million

10,000

100

1

0.01

0.0001

0.000001

0.00000001

0.0000000001

&lt; 1e-11

Jul 2, 1950

Theseus

Jul 2, 1950

Apr 19, 1965

<!-- image -->

Perceptron-Mark 1

Our World

<!-- image -->

La puissance de calcul nécessaire est colossale mais fois l'industrie est prête

La puissance de calcul

La puissance de calcul nécessaire est colossale mais cette fois l'industrie est prête

Test scores of Al systems on various capabilities relative to human

<!-- image -->

## 01

20

-20

-40

-60

-80

-100

1998

-Human-performance

The capability of each Al system is normalized to an initial performanke

2005

<!-- image -->

2010

Le test de Turing vole en éclat, l'IA devient plus performante que I

Les résultats

Le test de Turing vole en éclat, l'IA devient plus performante que les humains dans de nombreux nouveaux domaines

Our World

```
Qui fait l'IA aujourd'hui ?
```

<!-- image -->

00

Al

XI

Alolams

Met

Per

Qui fait l'IA aujourd'hui ?

<!-- image -->

```
Est-ce que l'IA réfléchit ?
```

<!-- image -->

- L'IA ne pense pas c'est un système probabiliste 01
- L'IA est devenue une ' chose sociale ' avec l'avènement des LLM 02
- Ces systèmes ont des limitations malgré leur performances 03

## Key takeaway

Des cas d'usages concrets

<!-- image -->

'

Explique-moi, en français simple, les obligations d'étiquetage pour un spiritueux importé en Allemagne.

Base-toi uniquement sur des sources officielles (textes UE, autorités allemandes) et cite-moi les références exactes (nom du règlement, article).

Présente la réponse en 5 points pour un public non juriste. '

Poser une questions bien formulée, sur une règle précise en donnant des indications claires. La partie 4 de la présentation donne des indications supplémentaires

## Poser une question

<!-- image -->

## 02

'

Liste-moi les nouveautés réglementaires officielles sur l'étiquetage des spiritueux dans l'UE et au Japon de la semaine, avec :

- lien vers la source officielle,
- résumé en 5 lignes,
- impact probable pour une maison de Cognac de taille moyenne.

Regroupe chaque élément dans un tableau par importance. '

## Organiser une veille réglementaire

## 02

## AVANTAGES

02

|   01 | Une veille automatisée           |
|------|----------------------------------|
|   02 | Des références déjà qualifiées   |
|   03 | Un résumé généré automatiquement |

## LIMITES

|   01 | L'IA ne voit pas tout                                           |
|------|-----------------------------------------------------------------|
|   02 | Les responsabilité réglementaires ne doivent pas être déléguées |
|   03 | Les résumés ne sont qu'une 'indication'                         |

<!-- image -->

'

## Cas 1

Utilisation de l'IA proposée par Canva : Elle vous aidera à rédiger et préparer vos communication

## Cas 2

Utilisation de l'IA dans Powerpoint :

Elle vous aidera à formaliser vos présentations

## Cas 3

Utilisation de l'IA dans Notion : Elle vous aidera dans vos prise de notes et vos compte-rendus '

Intégrer l'IA dans un outil existant, soit par des connecteurs soit par des modules mis à disposition

## Intégrer l'IA dans un outil existant

## 02

01

02

02

## AVANTAGES

Automatisation de tâche fastidieuses

Possibilité d'améliorer la qualité de production

- → Implication : Il faut maîtriser le niveau 1 et comprendre comment l'intégrer dans vos outils

## LIMITES

|   01 | Il faut toujours bien relire la production de l'IA   |
|------|------------------------------------------------------|
|   02 | Attention aux informations données                   |

<!-- image -->

## 02

## Construire un assistant à VOS process

- ' Un outil sur mesure qui connaît : - vos documentations, - vos contraintes, Qui sait : - générer des devis sur vos gammes de produits, - publier des communications avec votre charte graphiques - répondre automatiquement aux demande d'un client selon votre SAV/FAQ '

On intègre cette fois-ci l'IA dans vos données d'entreprise, vos processus propre. C'est le niveau le plus exigeant en terme de maturité

## 02

01

02

03

## AVANTAGES

## LIMITES

|   01 | C'est là qu'on a les meilleurs ROI                   | Le niveau de maturité de l'entreprise doit être suffisamment bon   |
|------|------------------------------------------------------|--------------------------------------------------------------------|
|   02 | Offre de véritables opportunités business            | Il faut être accompagnés par des gens compétents                   |
|   03 | Permet la mise en valeur des données de l'entreprise | Les données de l'entreprises doivent être 'activables'             |

- → Implication : Il faut maîtriser les niveau 1,2 et 3 et avoir un équipe prête à conduire le projet

Les limitations, les pièges

## Un impact sociétal

FRED 2 — Software Development Job Postings on Indeed in the United States

240

<!-- image -->

## 03

180

160

140

120

100

80

60

<!-- image -->

2020-07

2021-01

2021-07

2022-01

<!-- image -->

Al jobs are on the rise in the Software Development sector

23%

21%

19%

17%

15%

<!-- image -->

## 03

<!-- image -->

2020

<!-- image -->

Le ' shadow IA ' et la propriété des données

= L'USINEDIGITALE

= L'USINEDIGITALE

<!-- image -->

CHATBOTI

## avoir traité illégalement les données personnelles dans ChatGPT violation des données personnelles Un avocat a saisi la Cnil, reprochant à la start-up d'exploiter les

informations des utilisateurs de son chat gratuit pour entrainer plus de clarté sur le sujet.

L'autorité italienne de protection des données a infligé une amende de 15 millions d'euros à OpenAl pour ne pas avoir traité légalement les données des utilisateurs dans son des modèles d'IA. Celle-ci dément fermement. Elle vient de mettre à jour ses conditions générales d'utilisation pour offrir chatbot.

Samia Lokmane

Alice Vitard

13 février 2025 | 14h00

CLUB DATA PROTECTION

BY L'USINE DIGITALE

Mis à jour 13 Févr. 2025

30 décembre 2024 14h15

· 2 min. de lecture

© 2 min. de lecture

· Réagir →

· Réagir →

f

in

f

<!-- image -->

in

## Utilisation de vos données

Reconnaissance vocale

Reconnaissance vocale

€ OpenAI

OpenAl is an Al res company: Our m

general in

<!-- image -->

Les données sont centrales à la course à la qualité qui est en cours. Vos conversations sont des mines d'or pour l'entraînement.

<!-- image -->

- Ne mettez pas n'importe quelle données dans les moteurs de LLM 01
- Ne croyez pas toutes les informations qui viennent des IA 02
- Validez l'utilisation des outils au niveau entreprise 03

## Key takeaway

Une technologie énergivore

· DeepSeek/DeepSeek R1

SEMI-OUVERT

671 MDS DE PARAMÈTRES

<!-- image -->

## 03

<!-- image -->

671 milliards param.

taille du modèle

Ce qui correspond à :

20g

COz émis

705 tokens taille du texte

6h ampoule LED

Voir plus why the sky is blue

## 'Why the sky is blue ?'

Modèle de troisième génération de la famille Gemma de Google. Malgré sa moyenne taille, il pouvait rivaliser à sa sortie avec de nombreux modèles propriétaires supposés bien plus gros.

492 tokens taille du texte

37 min ampoule LED

Voir plus

Eau (L) ≈ Energie (Wh)×4 L/kWh / 1000

<!-- image -->

<!-- image -->

L'équivalent de 3-4 gorgées d'eau

L'équivalent d' une cuillère à café d'eau

<!-- image -->

## Comment bien démarrer ?

Devenir un pro du Prompt Engineering !

<!-- image -->

## 04

<!-- image -->

LES QUATRES PILIERS

La précision de la réponse dépend de la qualité de votre question

## Structurer sa question

INSTRUCTION

La tâche que l'on souhaite exécuter

DONNEES D'ENTREE

L'information à traiter

CONTEXT

Les éléments utiles à la réalisation de la tâche

FORMAT DE SORTIE

Le format de réponse attendu

Steering, en prompt engineering, fait référence à l a capacité de guider ou orienter le modèle d'IA vers un type de réponse ou un comportement  souhaité  par  des instructions  ou  contraintes spécifiques dans le prompt.

Prompt steering

## 04

<!-- image -->

## Prompt chaining

Enchaîner les sorties d'un prompt pour le passer au suivant

<!-- image -->

04

<!-- image -->

Tu es un assistant Maître de Chai. Tu génères des notes de dégustation courtes, en 2 phrases, au format : Nez / Bouche.

## Exemples :

## Fût 14 - Chêne du Limousin, 12 ans

Nez : fruits confits, vanille, légère touche de noix de coco grillée. Bouche : attaque ronde, finale épicée sur le poivre blanc, long.

## Fût 27 - Chêne américain, 8 ans

Nez : caramel, banane mûre, bois doux.

Bouche : moelleux en entrée, caramel beurré, finale sur la cannelle.

---

## Maintenant génère la note pour :

Fût 61 - Chêne du Tronçais, 18 ans, Grande Champagne.

<!-- image -->

## L'architecture RAG

## Savoir se situer

enterprises

<!-- image -->

Où en êtes vous ?

Il faut avoir conscience de son propre niveau de maturité et de celui de sa structure

<!-- image -->

## 04

## LES NIVEAUX D'ADOPTION

| Championnes   | Véritable appréhension et intégration de la technologie dans les processus   |
|---------------|------------------------------------------------------------------------------|
| Exploratrices | Elles utilisent l'IA dans des condition plus complexes                       |
| Optimiseurs   | Elles utilisent l'IA pour faire mieux, pas encore sur des cas complexes      |
| Novices       | Elles valident les premiers niveau de compréhension et d'utilisation         |

Où en êtes vous ?

Il faut avoir conscience de son propre niveau de maturité et de celui de sa structure

<!-- image -->

## PÉRIMÈTRE D'APPLICATION DE l'IA

## COMPLEXITÉ D'USAGE DE L'IA

| Toute l'organisation   | L'ensemble de l'entreprise utilise des solutions transverses   | Frontier    | Toute l'organisation                                       |
|------------------------|----------------------------------------------------------------|-------------|------------------------------------------------------------|
| Transverse             | Il y a des ponts entre les services                            | Sur mesure  | Transverse                                                 |
| Par service            | Certains service ont automatisé des processus                  | Clé en main | Des produits complets qui sont installés mis à disposition |
| Ponctuel               | Certains éléments utilisent l'IA                               | Embarquée   | Utilisation de l'IA dans un outil existant                 |
| Aucun                  | Aucun                                                          | Aucun       | Aucun                                                      |

Il faut avoir conscience de son propre niveau de maturité et de celui de sa structure

## 04

<!-- image -->

## Formez vous !

Pour progresser il faut des clés de lecture qui permettront une mise en pratique avec un ROI à la clé

<!-- image -->

## Section Bonus !

<!-- image -->

0*

Nicholas G. Otis

Solène Delecourt

Katelyn Cranney

Rembrand Koning

00 00 00

Harvard

Business

School

## L'IA au travail ?

25 %

## moins de femmes utilisent l'IA générative au travail

'Otis, N.G., Delecourt, S., Cranney, K. &amp; Koning, R. (2024). "Global Evidence on Gender Gaps and Generative AI." Harvard Business School Working'

<!-- image -->

Une adoption moindre chez les femmes qui serait dû essentiellement à des problématiques de confiance, perception du risques etc..

We have a new name - Stiftung Neue Verantwortung (SNV) is now interface.

<!-- image -->

0*

22 %

DATA-BRIEF

Al's Missing Link: The Gender

## de femmes dans les métiers qui conçoivent l'IA

Gap in the Talent Pool

Ruggero Marino Lazzaroni, Siddhi Pal

October 01, 2024

Tech analysis and policy ideas for Europe

'Interface EU - "AI's Missing Link: The Gender Gap in the Talent Pool", 2024 interface-eu.org - 1,6M profils analysés'

interface I

## L'IA au travail ?

<!-- image -->

Une adoption moindre chez les femmes qui serait dû essentiellement à des problématiques de confiance, perception du risques etc..

<!-- image -->

"

L'IA n'a pas de nez, pas de mémoire, pas de passion. Elle a du temps . À vous de savoir le lui confier.

K

<!-- image -->

## questions?

Pier-Jean Malandrino

<!-- image -->

ME CONTACTER

- 01 Linkedin :

https://www.linkedin.com/in/pier-jean-malandrino/

- 02 Mail : pierjean.malandrino@scub.net
- 03 Tél : 06 58 27 88 49

content_html:
<!DOCTYPE html>
<html>
<head>
<meta charset="UTF-8"/>
<title>33be42aa-9f90-4730-b48e-a15cd7e51e3a</title>
<meta name="generator" content="Docling HTML Serializer"/>
<style>
    html {
        background-color: #f5f5f5;
        font-family: Arial, sans-serif;
        line-height: 1.6;
    }
    body {
        max-width: 800px;
        margin: 0 auto;
        padding: 2rem;
        background-color: white;
        box-shadow: 0 0 10px rgba(0,0,0,0.1);
    }
    h1, h2, h3, h4, h5, h6 {
        color: #333;
        margin-top: 1.5em;
        margin-bottom: 0.5em;
    }
    h1 {
        font-size: 2em;
        border-bottom: 1px solid #eee;
        padding-bottom: 0.3em;
    }
    table {
        border-collapse: collapse;
        margin: 1em 0;
        width: 100%;
    }
    th, td {
        border: 1px solid #ddd;
        padding: 8px;
        text-align: left;
    }
    th {
        background-color: #f2f2f2;
        font-weight: bold;
    }
    figure {
        margin: 1.5em 0;
        text-align: center;
    }
    figcaption {
        color: #666;
        font-style: italic;
        margin-top: 0.5em;
    }
    img {
        max-width: 100%;
        height: auto;
    }
    pre {
        background-color: #f6f8fa;
        border-radius: 3px;
        padding: 1em;
        overflow: auto;
    }
    code {
        font-family: monospace;
        background-color: #f6f8fa;
        padding: 0.2em 0.4em;
        border-radius: 3px;
    }
    pre code {
        background-color: transparent;
        padding: 0;
    }
    .formula {
        text-align: center;
        padding: 0.5em;
        margin: 1em 0;
        background-color: #f9f9f9;
    }
    .formula-not-decoded {
        text-align: center;
        padding: 0.5em;
        margin: 1em 0;
        background: repeating-linear-gradient(
            45deg,
            #f0f0f0,
            #f0f0f0 10px,
            #f9f9f9 10px,
            #f9f9f9 20px
        );
    }
    .page-break {
        page-break-after: always;
        border-top: 1px dashed #ccc;
        margin: 2em 0;
    }
    .key-value-region {
        background-color: #f9f9f9;
        padding: 1em;
        border-radius: 4px;
        margin: 1em 0;
    }
    .key-value-region dt {
        font-weight: bold;
    }
    .key-value-region dd {
        margin-left: 1em;
        margin-bottom: 0.5em;
    }
    .form-container {
        border: 1px solid #ddd;
        padding: 1em;
        border-radius: 4px;
        margin: 1em 0;
    }
    .form-item {
        margin-bottom: 0.5em;
    }
    .image-classification {
        font-size: 0.9em;
        color: #666;
        margin-top: 0.5em;
    }
</style>
</head>
<body>
<div class='page'>
<p>LADIES WINE Hotel Restaurant L'Yeuse</p>
<p>L'IA au service des Spiritueux Osez VIA</p>
<p>MUR</p>
<p>WIRE SPIRITS</p>
<p>Ladies Wine</p>
<p>Osez</p>
<p>HIA</p>
<p>linkedin.com/in/pier-jean-malandrino</p>
<h2>Pier-jean Malandrino</h2>
<ul>
<li>CTO SCUB</li>
<li>Ambassadeur IA Ministère de l'économie et des finances</li>
</ul>
<h2>Notre voyage</h2>
<p>PLAN - SECTIONS</p>
<ul>
<li>01 De quoi parle t'on ?</li>
<li>02 Des cas d'usages concrets</li>
<li>03 Les limitations, les pièges</li>
<li>04 Comment démarrer ?</li>
</ul>
<p>*</p>
<p>Bonus</p>
<h2>Icebreaker</h2>
<p>Posons la problématique</p>
<p>00</p>
<p>Préparation réglementaire et commerciale pour l'exportation</p>
<p>d'un Cognac XO vers le Japon spécifiques Japon</p>
<p>3.1 Mentions obligatoires en japonais</p>
<p>Les obligations d'étiquetage des boissons alcooliques résultent du</p>
<p>· l'Act on Securing of Liquor Tax and on Liquor Business</p>
<p>Associations, qui définit des obligations de base communes à</p>
<ul>
<li>toutes les boissons alcooliques (iN Mo) Ai - "labellisation des · la Food Labeling Act, appliquée sous la supervision de la CAA,</li>
</ul>
<h2>1. Qualification réglementaire du produit 1.1 Catégorie juridique au Japon Un Cognac XO de 40% vol., élaboré par distillation de vin de raisin, cumul de : boissons alcooliques") ;[21][7]</h2>
<p>entre dans la catégorie des boissons alcooliques distillées au sens de la Liquor Tax Act japonaise (distilled alcoholic beverages).(1]</p>
<p>Au plan douanier, il relève en principe de la position HS 2208.20 -</p>
<h2>"Spirits obtained by distilling grape wine or grape marc" du tarif</h2>
<p>japonais, qui couvre les eaux-de-vie de vin et assimilées.[2][3]</p>
<p>Pour la fiscalité intérieure, ce produit est imposé dans la soumise à un barème spécifique de liquor tax.(4][5]</p>
<p>sous-catégorie "Whisky / Brandy / Spirits" des boissons distillées, pour les aspects « denrée alimentaire » (opérateur responsable,</p>
<p>additifs, OGM, allégations nutritionnelles, etc.).[9][7]</p>
<p>La NTA met à disposition une "Check-list de la méthode de</p>
<ul>
<li>présentation des boissons alcooliques (éléments communs)" qui énumère de manière détaillée les mentions à vérifier pour tout alcool. [7]</li>
</ul>
<p>À faire valider localement: la sous-position à 9/12 chiffres Pour un Cognac XO importé, les mentions suivantes doivent figurer en japonais sur l'étiquette (ou une contre-étiquette) :</p>
<p>exacte (2208.20.xxx) et la confirmation écrite de la catégorie fiscale « whisky/brandy » auprès de Japan Customs ou d'un</p>
<p>agent en douane, en utilisant l'outil webTARIFF[6]|</p>
<p>1.2 Principaux textes et organismes compétents</p>
<p>Les textes et autorités suivants encadrent l'importation et la commercialisation au Japon d'un Cognac XO :</p>
<p>· Liquor Tax Act (iMBiA) : définit les catégories de boissons alcooliques (dont les boissons distillées) et fixe les taux de liquor</p>
<p>tax selon la catégorie et le degré alcoolique. Compétence :</p>
<p>1. Catégorie de boisson alcoolique (iNAO) MEI)</p>
<p>· Mention claire de la catégorie légale (par exemple, terme japonais correspondant à la catégorie retenue pour la</p>
<p>liquor tax - whisky/brandy).[7]</p>
<p>· Doit apparaître sur la face principale, à proximité de la marque, avec une taille minimale de police dépendant du</p>
<ul>
<li>volume de la bouteille (pour 700 ml, plusieurs points de</li>
</ul>
<p>corps sont précisés dans la grille de la NTA).[7)</p>
<p>2. Teneur en alcool (7 N3-N5)</p>
<p>· Affichage en chiffres arabes, suivi de l'unité « I » ou « % »</p>
<p>(ex.: 727-14) 40 1(€).[7)</p>
<p>· Doit être exprimé dans une plage d'un degré maximum pour la catégorie fiscale correspondante.[7]</p>
<ol>
<li style="list-style-type: '3. ';">Contenu / volume (P%ht)</li>
</ol>
<p>Un néophyte peut produire des résultats qui ont l'air extrêmement qualitatifs</p>
<h2>00</h2>
<p>Prenons du recul</p>
<p>Ce néophyte n'est en revanche pas en mesure de comprendre ce travail qui comporte des erreurs et encore moins de les corriger</p>
<h2>00</h2>
<p>BUSINESS 2025</p>
<p>MIT NANDA</p>
<p>Aditya Challapally</p>
<p>Chris Pease</p>
<p>Ramesh Raskar</p>
<p>Pradyumna Chari</p>
<p>July 2025</p>
<h2>95 %</h2>
<p>de projets Gen AI ne produisent aucun ROI</p>
<p>(Return On Investment)</p>
<p>'The GenAI Divide: State of AI in Business 2025' publié par le MIT en 2025'</p>
<pre><code>De quoi parle t'on ?</code></pre>
<h2>01</h2>
<h2>L'Histoire</h2>
<p>Pourquoi l'IA change tout aujourd'hui ?</p>
<p>Annual attendance at major artificial intelligence conferences</p>
<h2>01</h2>
<p>80,000</p>
<p>60,000</p>
<p>40,000</p>
<p>20,000</p>
<p>2012</p>
<p>2010</p>
<p>Our World</p>
<p>"2017, parution du papier sur les architectures transformer</p>
<p>L'intérêt académique</p>
<p>'2017, parution du papier sur les architectures transformers, suscite un engouement très fort de la communauté scientifique'</p>
<p>Annual global corporate investment in artificial intelligence, by type</p>
<h2>01</h2>
<p>$350 billion</p>
<p>$300 billion</p>
<p>$250 billion</p>
<p>$200 billion</p>
<p>$150 billion</p>
<p>$100 billion</p>
<p>$50 billion</p>
<p>$0</p>
<p>2013</p>
<p>2014</p>
<p>2015</p>
<p>Our World</p>
<p>Augmentation drastique des investissements autant publiques</p>
<p>Les investissements</p>
<p>Augmentation drastique des investissements autant publiques de que privés.</p>
<p>Exponential growth of computation in the training of notable Al systems</p>
<h2>01</h2>
<p>10 billion</p>
<p>100 million</p>
<p>1 million</p>
<p>10,000</p>
<p>100</p>
<p>1</p>
<p>0.01</p>
<p>0.0001</p>
<p>0.000001</p>
<p>0.00000001</p>
<p>0.0000000001</p>
<p>&lt; 1e-11</p>
<p>Jul 2, 1950</p>
<p>Theseus</p>
<p>Jul 2, 1950</p>
<p>Apr 19, 1965</p>
<p>Perceptron-Mark 1</p>
<p>Our World</p>
<p>La puissance de calcul nécessaire est colossale mais fois l'industrie est prête</p>
<p>La puissance de calcul</p>
<p>La puissance de calcul nécessaire est colossale mais cette fois l'industrie est prête</p>
<p>Test scores of Al systems on various capabilities relative to human</p>
<h2>01</h2>
<p>20</p>
<p>-20</p>
<p>-40</p>
<p>-60</p>
<p>-80</p>
<p>-100</p>
<p>1998</p>
<p>-Human-performance</p>
<p>The capability of each Al system is normalized to an initial performanke</p>
<p>2005</p>
<p>2010</p>
<p>Le test de Turing vole en éclat, l'IA devient plus performante que I</p>
<p>Les résultats</p>
<p>Le test de Turing vole en éclat, l'IA devient plus performante que les humains dans de nombreux nouveaux domaines</p>
<p>Our World</p>
<pre><code>Qui fait l'IA aujourd'hui ?</code></pre>
<p>00</p>
<p>Al</p>
<p>XI</p>
<p>Alolams</p>
<p>Met</p>
<p>Per</p>
<figure><figcaption><div class="caption">Qui fait l&#x27;IA aujourd&#x27;hui ?</div></figcaption></figure>
<pre><code>Est-ce que l'IA réfléchit ?</code></pre>
<ul>
<li>L'IA ne pense pas c'est un système probabiliste 01</li>
<li>L'IA est devenue une ' chose sociale ' avec l'avènement des LLM 02</li>
<li>Ces systèmes ont des limitations malgré leur performances 03</li>
</ul>
<h2>Key takeaway</h2>
<p>Des cas d'usages concrets</p>
<p>'</p>
<p>Explique-moi, en français simple, les obligations d'étiquetage pour un spiritueux importé en Allemagne.</p>
<p>Base-toi uniquement sur des sources officielles (textes UE, autorités allemandes) et cite-moi les références exactes (nom du règlement, article).</p>
<p>Présente la réponse en 5 points pour un public non juriste. '</p>
<p>Poser une questions bien formulée, sur une règle précise en donnant des indications claires. La partie 4 de la présentation donne des indications supplémentaires</p>
<h2>Poser une question</h2>
<h2>02</h2>
<p>'</p>
<p>Liste-moi les nouveautés réglementaires officielles sur l'étiquetage des spiritueux dans l'UE et au Japon de la semaine, avec :</p>
<ul>
<li style="list-style-type: '- ';">lien vers la source officielle,</li>
<li style="list-style-type: '- ';">résumé en 5 lignes,</li>
<li style="list-style-type: '- ';">impact probable pour une maison de Cognac de taille moyenne.</li>
</ul>
<p>Regroupe chaque élément dans un tableau par importance. '</p>
<h2>Organiser une veille réglementaire</h2>
<h2>02</h2>
<h2>AVANTAGES</h2>
<p>02</p>
<table><tbody><tr><td>01</td><td>Une veille automatisée</td></tr><tr><td>02</td><td>Des références déjà qualifiées</td></tr><tr><td>03</td><td>Un résumé généré automatiquement</td></tr></tbody></table>
<h2>LIMITES</h2>
<table><tbody><tr><td>01</td><td>L&#x27;IA ne voit pas tout</td></tr><tr><td>02</td><td>Les responsabilité réglementaires ne doivent pas être déléguées</td></tr><tr><td>03</td><td>Les résumés ne sont qu&#x27;une &#x27;indication&#x27;</td></tr></tbody></table>
<p>'</p>
<h2>Cas 1</h2>
<p>Utilisation de l'IA proposée par Canva : Elle vous aidera à rédiger et préparer vos communication</p>
<h2>Cas 2</h2>
<p>Utilisation de l'IA dans Powerpoint :</p>
<p>Elle vous aidera à formaliser vos présentations</p>
<h2>Cas 3</h2>
<p>Utilisation de l'IA dans Notion : Elle vous aidera dans vos prise de notes et vos compte-rendus '</p>
<p>Intégrer l'IA dans un outil existant, soit par des connecteurs soit par des modules mis à disposition</p>
<h2>Intégrer l'IA dans un outil existant</h2>
<h2>02</h2>
<p>01</p>
<p>02</p>
<p>02</p>
<h2>AVANTAGES</h2>
<p>Automatisation de tâche fastidieuses</p>
<p>Possibilité d'améliorer la qualité de production</p>
<ul>
<li>→ Implication : Il faut maîtriser le niveau 1 et comprendre comment l'intégrer dans vos outils</li>
</ul>
<h2>LIMITES</h2>
<table><tbody><tr><td>01</td><td>Il faut toujours bien relire la production de l&#x27;IA</td></tr><tr><td>02</td><td>Attention aux informations données</td></tr></tbody></table>
<h2>02</h2>
<h2>Construire un assistant à VOS process</h2>
<ul>
<li>' Un outil sur mesure qui connaît : - vos documentations, - vos contraintes, Qui sait : - générer des devis sur vos gammes de produits, - publier des communications avec votre charte graphiques - répondre automatiquement aux demande d'un client selon votre SAV/FAQ '</li>
</ul>
<p>On intègre cette fois-ci l'IA dans vos données d'entreprise, vos processus propre. C'est le niveau le plus exigeant en terme de maturité</p>
<h2>02</h2>
<p>01</p>
<p>02</p>
<p>03</p>
<h2>AVANTAGES</h2>
<h2>LIMITES</h2>
<table><tbody><tr><td>01</td><td>C&#x27;est là qu&#x27;on a les meilleurs ROI</td><td>Le niveau de maturité de l&#x27;entreprise doit être suffisamment bon</td></tr><tr><td>02</td><td>Offre de véritables opportunités business</td><td>Il faut être accompagnés par des gens compétents</td></tr><tr><td>03</td><td>Permet la mise en valeur des données de l&#x27;entreprise</td><td>Les données de l&#x27;entreprises doivent être &#x27;activables&#x27;</td></tr></tbody></table>
<ul>
<li>→ Implication : Il faut maîtriser les niveau 1,2 et 3 et avoir un équipe prête à conduire le projet</li>
</ul>
<p>Les limitations, les pièges</p>
<h2>Un impact sociétal</h2>
<p>FRED 2 — Software Development Job Postings on Indeed in the United States</p>
<p>240</p>
<h2>03</h2>
<p>180</p>
<p>160</p>
<p>140</p>
<p>120</p>
<p>100</p>
<p>80</p>
<p>60</p>
<p>2020-07</p>
<p>2021-01</p>
<p>2021-07</p>
<p>2022-01</p>
<p>Al jobs are on the rise in the Software Development sector</p>
<p>23%</p>
<p>21%</p>
<p>19%</p>
<p>17%</p>
<p>15%</p>
<h2>03</h2>
<p>2020</p>
<p>Le ' shadow IA ' et la propriété des données</p>
<p>= L'USINEDIGITALE</p>
<p>= L'USINEDIGITALE</p>
<p>CHATBOTI</p>
<h2>avoir traité illégalement les données personnelles dans ChatGPT violation des données personnelles Un avocat a saisi la Cnil, reprochant à la start-up d'exploiter les</h2>
<p>informations des utilisateurs de son chat gratuit pour entrainer plus de clarté sur le sujet.</p>
<p>L'autorité italienne de protection des données a infligé une amende de 15 millions d'euros à OpenAl pour ne pas avoir traité légalement les données des utilisateurs dans son des modèles d'IA. Celle-ci dément fermement. Elle vient de mettre à jour ses conditions générales d'utilisation pour offrir chatbot.</p>
<p>Samia Lokmane</p>
<p>Alice Vitard</p>
<p>13 février 2025 | 14h00</p>
<p>CLUB DATA PROTECTION</p>
<p>BY L'USINE DIGITALE</p>
<p>Mis à jour 13 Févr. 2025</p>
<p>30 décembre 2024 14h15</p>
<p>· 2 min. de lecture</p>
<p>© 2 min. de lecture</p>
<p>· Réagir →</p>
<p>· Réagir →</p>
<p>f</p>
<p>in</p>
<p>f</p>
<p>in</p>
<h2>Utilisation de vos données</h2>
<p>Reconnaissance vocale</p>
<p>Reconnaissance vocale</p>
<p>€ OpenAI</p>
<p>OpenAl is an Al res company: Our m</p>
<p>general in</p>
<p>Les données sont centrales à la course à la qualité qui est en cours. Vos conversations sont des mines d'or pour l'entraînement.</p>
<ul>
<li>Ne mettez pas n'importe quelle données dans les moteurs de LLM 01</li>
<li>Ne croyez pas toutes les informations qui viennent des IA 02</li>
<li>Validez l'utilisation des outils au niveau entreprise 03</li>
</ul>
<h2>Key takeaway</h2>
<p>Une technologie énergivore</p>
<p>· DeepSeek/DeepSeek R1</p>
<p>SEMI-OUVERT</p>
<p>671 MDS DE PARAMÈTRES</p>
<h2>03</h2>
<p>671 milliards param.</p>
<p>taille du modèle</p>
<p>Ce qui correspond à :</p>
<p>20g</p>
<p>COz émis</p>
<p>705 tokens taille du texte</p>
<p>6h ampoule LED</p>
<p>Voir plus why the sky is blue</p>
<h2>'Why the sky is blue ?'</h2>
<p>Modèle de troisième génération de la famille Gemma de Google. Malgré sa moyenne taille, il pouvait rivaliser à sa sortie avec de nombreux modèles propriétaires supposés bien plus gros.</p>
<p>492 tokens taille du texte</p>
<p>37 min ampoule LED</p>
<p>Voir plus</p>
<figure><figcaption><div class="caption">Eau (L) ≈ Energie (Wh)×4 L/kWh / 1000</div></figcaption></figure>
<p>L'équivalent de 3-4 gorgées d'eau</p>
<p>L'équivalent d' une cuillère à café d'eau</p>
<h2>Comment bien démarrer ?</h2>
<p>Devenir un pro du Prompt Engineering !</p>
<h2>04</h2>
<p>LES QUATRES PILIERS</p>
<p>La précision de la réponse dépend de la qualité de votre question</p>
<h2>Structurer sa question</h2>
<p>INSTRUCTION</p>
<p>La tâche que l'on souhaite exécuter</p>
<p>DONNEES D'ENTREE</p>
<p>L'information à traiter</p>
<p>CONTEXT</p>
<p>Les éléments utiles à la réalisation de la tâche</p>
<p>FORMAT DE SORTIE</p>
<p>Le format de réponse attendu</p>
<p>Steering, en prompt engineering, fait référence à l a capacité de guider ou orienter le modèle d'IA vers un type de réponse ou un comportement  souhaité  par  des instructions  ou  contraintes spécifiques dans le prompt.</p>
<p>Prompt steering</p>
<h2>04</h2>
<h2>Prompt chaining</h2>
<p>Enchaîner les sorties d'un prompt pour le passer au suivant</p>
<p>04</p>
<p>Tu es un assistant Maître de Chai. Tu génères des notes de dégustation courtes, en 2 phrases, au format : Nez / Bouche.</p>
<h2>Exemples :</h2>
<h2>Fût 14 - Chêne du Limousin, 12 ans</h2>
<p>Nez : fruits confits, vanille, légère touche de noix de coco grillée. Bouche : attaque ronde, finale épicée sur le poivre blanc, long.</p>
<h2>Fût 27 - Chêne américain, 8 ans</h2>
<p>Nez : caramel, banane mûre, bois doux.</p>
<p>Bouche : moelleux en entrée, caramel beurré, finale sur la cannelle.</p>
<p>---</p>
<h2>Maintenant génère la note pour :</h2>
<p>Fût 61 - Chêne du Tronçais, 18 ans, Grande Champagne.</p>
<h2>L'architecture RAG</h2>
<h2>Savoir se situer</h2>
<p>enterprises</p>
<p>Où en êtes vous ?</p>
<p>Il faut avoir conscience de son propre niveau de maturité et de celui de sa structure</p>
<h2>04</h2>
<h2>LES NIVEAUX D'ADOPTION</h2>
<table><tbody><tr><td>Championnes</td><td>Véritable appréhension et intégration de la technologie dans les processus</td></tr><tr><td>Exploratrices</td><td>Elles utilisent l&#x27;IA dans des condition plus complexes</td></tr><tr><td>Optimiseurs</td><td>Elles utilisent l&#x27;IA pour faire mieux, pas encore sur des cas complexes</td></tr><tr><td>Novices</td><td>Elles valident les premiers niveau de compréhension et d&#x27;utilisation</td></tr></tbody></table>
<p>Où en êtes vous ?</p>
<p>Il faut avoir conscience de son propre niveau de maturité et de celui de sa structure</p>
<h2>PÉRIMÈTRE D'APPLICATION DE l'IA</h2>
<h2>COMPLEXITÉ D'USAGE DE L'IA</h2>
<table><tbody><tr><td>Toute l&#x27;organisation</td><td>L&#x27;ensemble de l&#x27;entreprise utilise des solutions transverses</td><td>Frontier</td><td>Toute l&#x27;organisation</td></tr><tr><td>Transverse</td><td>Il y a des ponts entre les services</td><td>Sur mesure</td><td>Transverse</td></tr><tr><td>Par service</td><td>Certains service ont automatisé des processus</td><td>Clé en main</td><td>Des produits complets qui sont installés mis à disposition</td></tr><tr><td>Ponctuel</td><td>Certains éléments utilisent l&#x27;IA</td><td>Embarquée</td><td>Utilisation de l&#x27;IA dans un outil existant</td></tr><tr><td>Aucun</td><td>Aucun</td><td>Aucun</td><td>Aucun</td></tr></tbody></table>
<p>Il faut avoir conscience de son propre niveau de maturité et de celui de sa structure</p>
<h2>04</h2>
<h2>Formez vous !</h2>
<p>Pour progresser il faut des clés de lecture qui permettront une mise en pratique avec un ROI à la clé</p>
<h2>Section Bonus !</h2>
<p>0*</p>
<p>Nicholas G. Otis</p>
<p>Solène Delecourt</p>
<p>Katelyn Cranney</p>
<p>Rembrand Koning</p>
<p>00 00 00</p>
<p>Harvard</p>
<p>Business</p>
<p>School</p>
<h2>L'IA au travail ?</h2>
<p>25 %</p>
<h2>moins de femmes utilisent l'IA générative au travail</h2>
<p>'Otis, N.G., Delecourt, S., Cranney, K. &amp; Koning, R. (2024). "Global Evidence on Gender Gaps and Generative AI." Harvard Business School Working'</p>
<p>Une adoption moindre chez les femmes qui serait dû essentiellement à des problématiques de confiance, perception du risques etc..</p>
<p>We have a new name - Stiftung Neue Verantwortung (SNV) is now interface.</p>
<p>0*</p>
<p>22 %</p>
<p>DATA-BRIEF</p>
<p>Al's Missing Link: The Gender</p>
<h2>de femmes dans les métiers qui conçoivent l'IA</h2>
<p>Gap in the Talent Pool</p>
<p>Ruggero Marino Lazzaroni, Siddhi Pal</p>
<p>October 01, 2024</p>
<p>Tech analysis and policy ideas for Europe</p>
<p>'Interface EU - "AI's Missing Link: The Gender Gap in the Talent Pool", 2024 interface-eu.org - 1,6M profils analysés'</p>
<p>interface I</p>
<h2>L'IA au travail ?</h2>
<p>Une adoption moindre chez les femmes qui serait dû essentiellement à des problématiques de confiance, perception du risques etc..</p>
<p>"</p>
<p>L'IA n'a pas de nez, pas de mémoire, pas de passion. Elle a du temps . À vous de savoir le lui confier.</p>
<p>K</p>
<h2>questions?</h2>
<p>Pier-Jean Malandrino</p>
<p>ME CONTACTER</p>
<ul>
<li>01 Linkedin :</li>
</ul>
<p>https://www.linkedin.com/in/pier-jean-malandrino/</p>
<ul>
<li>02 Mail : pierjean.malandrino@scub.net</li>
<li>03 Tél : 06 58 27 88 49</li>
</ul>
</div>
</body>
</html>

pages_json:
[{"page_number": 1, "width": 720.0, "height": 405.0, "elements": [{"type": "picture", "bbox": [31.470844268798828, 30.205852508544922, 52.395172119140625, 49.424800872802734], "content": "", "level": 1}, {"type": "text", "bbox": [30.00000096, 217.37060908085925, 148.58400475468795, 240.2214161170042], "content": "LADIES WINE Hotel Restaurant L'Yeuse", "level": 1}, {"type": "text", "bbox": [30.00000096, 255.67938068173993, 276.98700886358404, 389.9374549779982], "content": "L'IA au service des Spiritueux Osez VIA", "level": 1}, {"type": "text", "bbox": [182.66666661666667, 34.666666744444456, 196.66666661666667, 44.00000007777777], "content": "MUR", "level": 1}, {"type": "text", "bbox": [182.66666642222222, 44.66666666666663, 197.3333330888889, 48.66666666666663], "content": "WIRE SPIRITS", "level": 1}, {"type": "text", "bbox": [209.33333354166666, 37.33333323333329, 266.0000002083333, 45.33333323333335], "content": "Ladies Wine", "level": 1}, {"type": "picture", "bbox": [601.0260009765625, 28.633520126342773, 691.3823852539062, 54.44376754760742], "content": "", "level": 1}, {"type": "picture", "bbox": [460.7789611816406, 328.1572570800781, 657.0767211914062, 350.61334228515625], "content": "", "level": 1}]}, {"page_number": 2, "width": 720.0, "height": 405.0, "elements": [{"type": "text", "bbox": [9.209428720763297, 64.2348134956028, 63.781226029284035, 83.32207269683533], "content": "Osez", "level": 1}, {"type": "text", "bbox": [22.560747822066457, 85.07784441477042, 63.037383336085135, 103.68862285788424], "content": "HIA", "level": 1}, {"type": "picture", "bbox": [73.50423431396484, 125.14954376220703, 226.2264404296875, 277.991455078125], "content": "", "level": 1}, {"type": "text", "bbox": [36.000001151999996, 356.30038102661183, 213.8760068440319, 364.0311872039168], "content": "linkedin.com/in/pier-jean-malandrino", "level": 1}, {"type": "section_header", "bbox": [342.000010944, 96.91250410120006, 634.2300202953601, 120.10492263311482], "content": "Pier-jean Malandrino", "level": 1}, {"type": "list", "bbox": [360.00001152, 171.91687975134, 389.148012452736, 207.24866047811076], "content": "CTO SCUB", "level": 2}, {"type": "list", "bbox": [360.00001152, 231.0027816420888, 543.7395173996639, 266.33528986888285], "content": "Ambassadeur IA Minist\u00e8re de l'\u00e9conomie et des finances", "level": 2}, {"type": "picture", "bbox": [596.6705932617188, 172.59158325195312, 650.7384033203125, 266.5946350097656], "content": "", "level": 1}]}, {"page_number": 3, "width": 720.0, "height": 405.0, "elements": [{"type": "section_header", "bbox": [42.000001344, 183.63338087626798, 180.348005771136, 275.1504706444374], "content": "Notre voyage", "level": 1}, {"type": "text", "bbox": [396.00001267199997, 123.06666318813313, 463.93876484604, 130.15323551732928], "content": "PLAN - SECTIONS", "level": 1}, {"type": "list", "bbox": [396.00001267199997, 164.7491520219727, 527.1715668694897, 180.21076437658255], "content": "01 De quoi parle t'on ?", "level": 2}, {"type": "list", "bbox": [396.00001267199997, 206.70585586458716, 564.0603180499296, 222.16746821919702], "content": "02 Des cas d'usages concrets", "level": 2}, {"type": "list", "bbox": [396.00001267199997, 248.66254470720116, 557.7828178490496, 264.12415706181105], "content": "03 Les limitations, les pi\u00e8ges", "level": 2}, {"type": "list", "bbox": [396.54530018944916, 290.61923354981514, 536.5428171693696, 306.080845904425], "content": "04 Comment d\u00e9marrer ?", "level": 2}, {"type": "picture", "bbox": [401.77752685546875, 330.9974670410156, 411.7504577636719, 341.3924865722656], "content": "", "level": 1}, {"type": "text", "bbox": [402.10688861742, 332.57593739242964, 411.988888933644, 348.0375497470395], "content": "*", "level": 1}, {"type": "text", "bbox": [429.6790637497296, 334.2192586950159, 461.5615647699696, 344.55017420417664], "content": "Bonus", "level": 1}]}, {"page_number": 4, "width": 720.0, "height": 405.0, "elements": [{"type": "section_header", "bbox": [192.8503936712124, 181.1940282982087, 341.0803984145724, 204.3864468301235], "content": "Icebreaker", "level": 1}, {"type": "text", "bbox": [192.8503936712124, 214.93999219707953, 445.39039543899213, 232.11956105049012], "content": "Posons la probl\u00e9matique", "level": 1}]}, {"page_number": 5, "width": 720.0, "height": 405.0, "elements": [{"type": "picture", "bbox": [33.70566940307617, 43.722129821777344, 84.61638641357422, 77.1850357055664], "content": "", "level": 1}, {"type": "text", "bbox": [33.000001056, 45.45600145459201, 85.704002742528, 86.68696773355163], "content": "00", "level": 1}, {"type": "text", "bbox": [107.72820001874543, 36.884514546361686, 244.55086994528074, 47.946547906509466], "content": "Pr\u00e9paration r\u00e9glementaire et commerciale pour l'exportation", "level": 1}, {"type": "text", "bbox": [108.51453987629492, 49.27839630716346, 255.5595897409652, 60.4278216863155], "content": "Pr\u00e9paration r\u00e9glementaire et commerciale pour l'exportation", "level": 1}, {"type": "text", "bbox": [108.51453764690575, 61.93095785334725, 242.97819760050697, 72.98425194808715], "content": "d'un Cognac XO vers le Japon sp\u00e9cifiques Japon", "level": 1}, {"type": "text", "bbox": [335.7659884770705, 39.95545646743659, 399.4593034944701, 48.65616774960171], "content": "d'un Cognac XO vers le Japon sp\u00e9cifiques Japon", "level": 1}, {"type": "text", "bbox": [336.55232603616207, 54.14960637297361, 441.1351760554949, 60.42782135437551], "content": "3.1 Mentions obligatoires en japonais", "level": 1}, {"type": "text", "bbox": [335.7659958270795, 64.26169252332966, 485.17005566468396, 70.62992111283569], "content": "Les obligations d'\u00e9tiquetage des boissons alcooliques r\u00e9sultent du", "level": 1}, {"type": "text", "bbox": [341.2703524514706, 82.24164797182078, 478.09302237800597, 87.9020043192595], "content": "\u00b7 l'Act on Securing of Liquor Tax and on Liquor Business", "level": 1}, {"type": "text", "bbox": [346.77470714894224, 88.56792873224668, 490.6744171605419, 94.95800516773505], "content": "Associations, qui d\u00e9finit des obligations de base communes \u00e0", "level": 1}, {"type": "list", "bbox": [340.48401337724545, 95.74278210946693, 491.4607543125832, 115.36220456432301], "content": "toutes les boissons alcooliques (iN Mo) Ai - \"labellisation des \u00b7 la Food Labeling Act, appliqu\u00e9e sous la supervision de la CAA,", "level": 2}, {"type": "section_header", "bbox": [107.72819997550198, 71.31532846114919, 416.75872024690784, 108.29921270331738], "content": "1. Qualification r\u00e9glementaire du produit 1.1 Cat\u00e9gorie juridique au Japon Un Cognac XO de 40% vol., \u00e9labor\u00e9 par distillation de vin de raisin, cumul de : boissons alcooliques\") ;[21][7]", "level": 1}, {"type": "text", "bbox": [107.72819972898628, 109.08398958292105, 262.6366297251197, 115.36220456432301], "content": "entre dans la cat\u00e9gorie des boissons alcooliques distill\u00e9es au sens de la Liquor Tax Act japonaise (distilled alcoholic beverages).(1]", "level": 1}, {"type": "text", "bbox": [107.72819741217103, 115.2048998683859, 242.1918573657723, 120.86525621582462], "content": "entre dans la cat\u00e9gorie des boissons alcooliques distill\u00e9es au sens de la Liquor Tax Act japonaise (distilled alcoholic beverages).(1]", "level": 1}, {"type": "text", "bbox": [107.72820138422095, 126.19265043735368, 253.200581322356, 132.62729642784007], "content": "Au plan douanier, il rel\u00e8ve en principe de la position HS 2208.20 -", "level": 1}, {"type": "section_header", "bbox": [107.72819648665423, 132.5189308999855, 259.4912764634548, 138.1792872474242], "content": "\"Spirits obtained by distilling grape wine or grape marc\" du tarif", "level": 1}, {"type": "text", "bbox": [107.72820163073668, 139.69028865152734, 244.55087155727202, 145.1837269984071], "content": "japonais, qui couvre les eaux-de-vie de vin et assimil\u00e9es.[2][3]", "level": 1}, {"type": "text", "bbox": [107.72820435538819, 150.67716542285984, 231.96947420459227, 156.95538040426177], "content": "Pour la fiscalit\u00e9 int\u00e9rieure, ce produit est impos\u00e9 dans la soumise \u00e0 un bar\u00e8me sp\u00e9cifique de liquor tax.(4][5]", "level": 1}, {"type": "text", "bbox": [107.72819891236766, 163.15144761100606, 221.7470888234367, 169.51181079813753], "content": "Pour la fiscalit\u00e9 int\u00e9rieure, ce produit est impos\u00e9 dans la soumise \u00e0 un bar\u00e8me sp\u00e9cifique de liquor tax.(4][5]", "level": 1}, {"type": "text", "bbox": [107.72819683670606, 156.82516706324031, 253.98691682123982, 163.23359557504043], "content": "sous-cat\u00e9gorie \"Whisky / Brandy / Spirits\" des boissons distill\u00e9es, pour les aspects \u00ab denr\u00e9e alimentaire \u00bb (op\u00e9rateur responsable,", "level": 1}, {"type": "text", "bbox": [346.7747130121279, 115.20489982914705, 492.2470929502629, 120.86525617658583], "content": "sous-cat\u00e9gorie \"Whisky / Brandy / Spirits\" des boissons distill\u00e9es, pour les aspects \u00ab denr\u00e9e alimentaire \u00bb (op\u00e9rateur responsable,", "level": 1}, {"type": "text", "bbox": [346.7747110624018, 121.53118042596134, 466.29796096573773, 127.19153677340012], "content": "additifs, OGM, all\u00e9gations nutritionnelles, etc.).[9][7]", "level": 1}, {"type": "text", "bbox": [334.97964969679373, 134.1837414953336, 472.5886596697278, 139.84409784277238], "content": "La NTA met \u00e0 disposition une \"Check-list de la m\u00e9thode de", "level": 1}, {"type": "list", "bbox": [334.19331387723014, 140.4750656525115, 495.3924427477206, 159.3097111024595], "content": "pr\u00e9sentation des boissons alcooliques (\u00e9l\u00e9ments communs)\" qui \u00e9num\u00e8re de mani\u00e8re d\u00e9taill\u00e9e les mentions \u00e0 v\u00e9rifier pour tout alcool. [7]", "level": 2}, {"type": "text", "bbox": [124.24128380665518, 164.80314955372543, 493.8197694952476, 179.7995545403758], "content": "\u00c0 faire valider localement: la sous-position \u00e0 9/12 chiffres Pour un Cognac XO import\u00e9, les mentions suivantes doivent figurer en japonais sur l'\u00e9tiquette (ou une contre-\u00e9tiquette) :", "level": 1}, {"type": "text", "bbox": [124.2412855402618, 181.13140317564077, 257.1322654010656, 186.12583524691024], "content": "exacte (2208.20.xxx) et la confirmation \u00e9crite de la cat\u00e9gorie fiscale \u00ab whisky/brandy \u00bb aupr\u00e8s de Japan Customs ou d'un", "level": 1}, {"type": "text", "bbox": [124.24128272085629, 187.45768369555037, 255.5595926551247, 193.11804004298912], "content": "exacte (2208.20.xxx) et la confirmation \u00e9crite de la cat\u00e9gorie fiscale \u00ab whisky/brandy \u00bb aupr\u00e8s de Japan Customs ou d'un", "level": 1}, {"type": "text", "bbox": [123.45494373889227, 193.7839642433161, 235.90116372342604, 199.44432059075484], "content": "agent en douane, en utilisant l'outil webTARIFF[6]|", "level": 1}, {"type": "text", "bbox": [108.51453767783569, 205.61154863803918, 237.47383763917006, 211.88976361944114], "content": "1.2 Principaux textes et organismes comp\u00e9tents", "level": 1}, {"type": "text", "bbox": [107.72819518579483, 215.75946562661832, 240.6191852128608, 221.41982197405707], "content": "Les textes et autorit\u00e9s suivants encadrent l'importation et la commercialisation au Japon d'un Cognac XO :", "level": 1}, {"type": "text", "bbox": [107.7281968225533, 222.08574605339763, 209.16569682255331, 227.74610240083635], "content": "Les textes et autorit\u00e9s suivants encadrent l'importation et la commercialisation au Japon d'un Cognac XO :", "level": 1}, {"type": "text", "bbox": [112.44622629526962, 233.73942084597584, 248.48255617540622, 239.3997771934146], "content": "\u00b7 Liquor Tax Act (iMBiA) : d\u00e9finit les cat\u00e9gories de boissons alcooliques (dont les boissons distill\u00e9es) et fixe les taux de liquor", "level": 1}, {"type": "text", "bbox": [118.73692286619844, 240.06570169165965, 261.06395278500065, 245.72605803909838], "content": "\u00b7 Liquor Tax Act (iMBiA) : d\u00e9finit les cat\u00e9gories de boissons alcooliques (dont les boissons distill\u00e9es) et fixe les taux de liquor", "level": 1}, {"type": "text", "bbox": [118.73692474698147, 246.39198224923504, 246.1235446155184, 252.05233859667376], "content": "tax selon la cat\u00e9gorie et le degr\u00e9 alcoolique. Comp\u00e9tence :", "level": 1}, {"type": "picture", "bbox": [120.41427612304688, 46.76447677612305, 185.1753692626953, 324.50494384765625], "content": "", "level": 1}, {"type": "picture", "bbox": [33.574710845947266, 341.03851318359375, 298.6656188964844, 362.0414123535156], "content": "", "level": 1}, {"type": "text", "bbox": [341.2703511841624, 182.79621387731518, 453.71657116869613, 189.13123344879367], "content": "1. Cat\u00e9gorie de boisson alcoolique (iNAO) MEI)", "level": 1}, {"type": "text", "bbox": [351.49273452226834, 189.78841857348365, 487.5290644024049, 195.44877492092238], "content": "\u00b7 Mention claire de la cat\u00e9gorie l\u00e9gale (par exemple, terme japonais correspondant \u00e0 la cat\u00e9gorie retenue pour la", "level": 1}, {"type": "text", "bbox": [357.7834327214437, 196.11469937992857, 481.2383726905112, 201.77505572736732], "content": "\u00b7 Mention claire de la cat\u00e9gorie l\u00e9gale (par exemple, terme japonais correspondant \u00e0 la cat\u00e9gorie retenue pour la", "level": 1}, {"type": "text", "bbox": [357.7834309177152, 203.10690427567752, 430.12645086358333, 208.76726062311627], "content": "liquor tax - whisky/brandy).[7]", "level": 1}, {"type": "text", "bbox": [351.49273620922844, 209.43318500037483, 482.81104614349687, 215.09354134781358], "content": "\u00b7 Doit appara\u00eetre sur la face principale, \u00e0 proximit\u00e9 de la marque, avec une taille minimale de police d\u00e9pendant du", "level": 1}, {"type": "text", "bbox": [357.7834344308066, 215.75946536151574, 489.10174436507504, 222.09186334389435], "content": "\u00b7 Doit appara\u00eetre sur la face principale, \u00e0 proximit\u00e9 de la marque, avec une taille minimale de police d\u00e9pendant du", "level": 1}, {"type": "list", "bbox": [357.783435011904, 222.08574606623685, 485.1700548804409, 228.41202668984488], "content": "volume de la bouteille (pour 700 ml, plusieurs points de", "level": 2}, {"type": "text", "bbox": [357.7834301182724, 229.07795095895634, 463.9389500641405, 234.7383073063951], "content": "corps sont pr\u00e9cis\u00e9s dans la grille de la NTA).[7)", "level": 1}, {"type": "text", "bbox": [341.2703476777547, 236.0701559039942, 421.4767426719548, 241.06458797526366], "content": "2. Teneur en alcool (7 N3-N5)", "level": 1}, {"type": "text", "bbox": [351.49273456027623, 242.3964364549059, 490.6744144597456, 248.7742781728532], "content": "\u00b7 Affichage en chiffres arabes, suivi de l'unit\u00e9 \u00ab I \u00bb ou \u00ab % \u00bb", "level": 1}, {"type": "text", "bbox": [357.78343144600166, 250.34383207148755, 421.4767413802701, 255.05249346630774], "content": "(ex.: 727-14) 40 1(\u20ac).[7)", "level": 1}, {"type": "text", "bbox": [351.4927390286339, 256.6220471915207, 484.38371888943766, 262.1154855384005], "content": "\u00b7 Doit \u00eatre exprim\u00e9 dans une plage d'un degr\u00e9 maximum pour la cat\u00e9gorie fiscale correspondante.[7]", "level": 1}, {"type": "text", "bbox": [358.56976744002804, 262.90026242115766, 457.64825746709397, 268.39370076803743], "content": "\u00b7 Doit \u00eatre exprim\u00e9 dans une plage d'un degr\u00e9 maximum pour la cat\u00e9gorie fiscale correspondante.[7]", "level": 1}, {"type": "list", "bbox": [342.0566868918241, 269.9632545307924, 409.6816868918241, 275.4566928776722], "content": "Contenu / volume (P%ht)", "level": 2}, {"type": "picture", "bbox": [414.23992919921875, 48.96469497680664, 641.6163940429688, 324.4758605957031], "content": "", "level": 1}, {"type": "text", "bbox": [318.14960518078703, 343.0980087291359, 644.2556156161794, 361.62881525204085], "content": "Un n\u00e9ophyte peut produire des r\u00e9sultats qui ont l'air extr\u00eamement qualitatifs", "level": 1}]}, {"page_number": 6, "width": 720.0, "height": 405.0, "elements": [{"type": "section_header", "bbox": [33.000001056, 45.45600145459201, 85.704002742528, 86.68696773355163], "content": "00", "level": 1}, {"type": "picture", "bbox": [171.06849670410156, 31.302690505981445, 587.2294921875, 308.576416015625], "content": "", "level": 1}, {"type": "text", "bbox": [33.000001056, 343.49401099180795, 270.16800864537606, 366.6864295237228], "content": "Prenons du recul", "level": 1}, {"type": "text", "bbox": [298.95078956642493, 343.0980087291359, 659.6438011086013, 361.62881525204085], "content": "Ce n\u00e9ophyte n'est en revanche pas en mesure de comprendre ce travail qui comporte des erreurs et encore moins de les corriger", "level": 1}]}, {"page_number": 7, "width": 720.0, "height": 405.0, "elements": [{"type": "picture", "bbox": [33.71245574951172, 43.70525360107422, 84.67935943603516, 77.15489959716797], "content": "", "level": 1}, {"type": "section_header", "bbox": [33.000001056, 45.45600145459201, 85.704002742528, 86.68696773355163], "content": "00", "level": 1}, {"type": "text", "bbox": [38.61326033425414, 100.66666675185184, 162.44198961602208, 116.0000000851852], "content": "BUSINESS 2025", "level": 1}, {"type": "text", "bbox": [37.94046167310078, 231.29543947817024, 69.9103678214317, 237.37122734031217], "content": "MIT NANDA", "level": 1}, {"type": "text", "bbox": [37.94751426153314, 240.66666664166667, 73.89779050462705, 245.33333330833332], "content": "Aditya Challapally", "level": 1}, {"type": "text", "bbox": [37.947514111740325, 245.33333331666665, 61.9143649404696, 249.33333331666665], "content": "Chris Pease", "level": 1}, {"type": "text", "bbox": [37.94751329065378, 250.66666668888888, 69.23756853927257, 254.66666668888888], "content": "Ramesh Raskar", "level": 1}, {"type": "text", "bbox": [37.94751323517497, 255.3333333666667, 72.56629776556171, 260.0000000333334], "content": "Pradyumna Chari", "level": 1}, {"type": "text", "bbox": [37.903034533925066, 260.4389096619692, 57.29862340874197, 264.8944236161318], "content": "July 2025", "level": 1}, {"type": "section_header", "bbox": [268.505821092186, 55.83439003670043, 334.385823200346, 81.60374396105021], "content": "95 %", "level": 1}, {"type": "text", "bbox": [164.31663150813202, 121.43839025802833, 442.3646490946684, 156.1903948560925], "content": "de projets Gen AI ne produisent aucun ROI", "level": 1}, {"type": "text", "bbox": [215.4721193951076, 159.83840198682873, 387.4081302700598, 175.39040297049272], "content": "(Return On Investment)", "level": 1}, {"type": "text", "bbox": [155.52095372667034, 197.8724185819172, 452.32859947451493, 216.40321760482186], "content": "'The GenAI Divide: State of AI in Business 2025' publi\u00e9 par le MIT en 2025'", "level": 2}, {"type": "picture", "bbox": [469.876708984375, 39.45151901245117, 686.3659057617188, 311.0538635253906], "content": "", "level": 1}]}, {"page_number": 8, "width": 720.0, "height": 405.0, "elements": [{"type": "code", "bbox": [192.8503936712124, 176.9940311638088, 385.5493998375805, 236.1864103477223], "content": "De quoi parle t'on ?", "level": 1}]}, {"page_number": 9, "width": 720.0, "height": 405.0, "elements": [{"type": "section_header", "bbox": [33.000001056, 45.45600145459201, 85.704002742528, 86.68696773355163], "content": "01", "level": 1}, {"type": "picture", "bbox": [32.565731048583984, 103.76466369628906, 686.4443359375, 311.3695068359375], "content": "", "level": 1}, {"type": "section_header", "bbox": [29.97637745924405, 362.95858161467424, 178.2063822026041, 386.15100014658907], "content": "L'Histoire", "level": 1}]}, {"page_number": 10, "width": 720.0, "height": 405.0, "elements": [{"type": "text", "bbox": [192.8503936712124, 176.9940311638088, 563.4254055296126, 236.1864103477223], "content": "Pourquoi l'IA change tout aujourd'hui ?", "level": 1}]}, {"page_number": 11, "width": 720.0, "height": 405.0, "elements": [{"type": "text", "bbox": [21.124999788900258, 23.222222706252808, 373.9125052106829, 38.00000000856198], "content": "Annual attendance at major artificial intelligence conferences", "level": 1}, {"type": "picture", "bbox": [33.63722610473633, 43.99717712402344, 83.43464660644531, 76.94696044921875], "content": "", "level": 1}, {"type": "section_header", "bbox": [33.000001056, 45.45600145459201, 85.704002742528, 86.68696773355163], "content": "01", "level": 1}, {"type": "text", "bbox": [25.79215175709945, 119.35102040209608, 45.68895322567669, 125.98163259773389], "content": "80,000", "level": 1}, {"type": "text", "bbox": [25.78094053661762, 157.62745010463118, 45.70016404130441, 164.32561098699682], "content": "60,000", "level": 1}, {"type": "text", "bbox": [25.74993905988003, 196.56244024150223, 45.73116530800086, 202.7477639074449], "content": "40,000", "level": 1}, {"type": "text", "bbox": [25.771952863897866, 234.94948236971547, 45.70915211938125, 240.9811298038093], "content": "20,000", "level": 1}, {"type": "picture", "bbox": [101.69283294677734, 43.332515716552734, 112.31478881835938, 78.71674346923828], "content": "", "level": 1}, {"type": "text", "bbox": [96.53633754140401, 279.959183930745, 112.01162785490415, 285.8530610850917], "content": "2012", "level": 1}, {"type": "text", "bbox": [47.162790533296594, 279.95918390338056, 61.90116274452731, 285.8530610577273], "content": "2010", "level": 1}, {"type": "text", "bbox": [453.2049414474902, 24.976937118653723, 482.6816858699517, 30.971402063303174], "content": "Our World", "level": 1}, {"type": "picture", "bbox": [155.7349090576172, 30.864551544189453, 639.8141479492188, 313.0915222167969], "content": "", "level": 1}, {"type": "text", "bbox": [215.85964359823694, 321.1788900038501, 506.30398202205737, 333.76983486762344], "content": "\"2017, parution du papier sur les architectures transformer", "level": 1}, {"type": "text", "bbox": [33.000001056, 343.49401099180795, 329.46001054272006, 366.6864295237228], "content": "L'int\u00e9r\u00eat acad\u00e9mique", "level": 1}, {"type": "text", "bbox": [360.4429240341732, 343.0980087291359, 666.7849338371175, 361.62881525204085], "content": "'2017, parution du papier sur les architectures transformers, suscite un engouement tr\u00e8s fort de la communaut\u00e9 scientifique'", "level": 1}]}, {"page_number": 12, "width": 720.0, "height": 405.0, "elements": [{"type": "text", "bbox": [22.844475884709855, 24.977064444920813, 409.7267527831679, 39.024340779071], "content": "Annual global corporate investment in artificial intelligence, by type", "level": 1}, {"type": "picture", "bbox": [33.57703399658203, 44.01348114013672, 83.5166244506836, 76.88802337646484], "content": "", "level": 1}, {"type": "section_header", "bbox": [33.000001056, 45.45600145459201, 85.704002742528, 86.68696773355163], "content": "01", "level": 1}, {"type": "text", "bbox": [21.338946901922185, 87.4622362559723, 53.82675030218994, 94.405918039386], "content": "$350 billion", "level": 1}, {"type": "text", "bbox": [21.370640691643228, 114.8640974867098, 53.795057523182344, 120.88899069355995], "content": "$300 billion", "level": 1}, {"type": "text", "bbox": [21.341450166866284, 140.489228773867, 53.824246889759614, 147.40732281217413], "content": "$250 billion", "level": 1}, {"type": "text", "bbox": [21.336217299613796, 166.9695275061114, 53.82947975724209, 173.941222861868], "content": "$200 billion", "level": 1}, {"type": "text", "bbox": [21.370640108249347, 194.38539554885176, 53.79505693978847, 201.14862379818294], "content": "$150 billion", "level": 1}, {"type": "text", "bbox": [21.32345034432301, 220.65371132539602, 53.84224685656084, 227.75805318845076], "content": "$100 billion", "level": 1}, {"type": "text", "bbox": [25.043487122842603, 246.61205065195546, 53.80680289259457, 253.34129604079138], "content": "$50 billion", "level": 1}, {"type": "text", "bbox": [45.688953643519405, 273.17038548330953, 54.53197658903875, 280.53346859478376], "content": "$0", "level": 1}, {"type": "text", "bbox": [67.00166873422576, 280.38563200403104, 81.85588961454117, 286.5717715797443], "content": "2013", "level": 1}, {"type": "text", "bbox": [96.52961193241079, 280.5157835181178, 112.01835292611607, 286.44162020721814], "content": "2014", "level": 1}, {"type": "text", "bbox": [126.74999990598958, 280.5334686925068, 141.48837211722028, 286.42393492760635], "content": "2015", "level": 1}, {"type": "text", "bbox": [453.20494137379836, 24.977064183742755, 482.68168579625984, 31.661257724018412], "content": "Our World", "level": 1}, {"type": "picture", "bbox": [155.6071319580078, 30.910200119018555, 639.5381469726562, 313.03167724609375], "content": "", "level": 1}, {"type": "text", "bbox": [215.91714713560918, 323.2393510110758, 506.26308020514654, 332.81135911951236], "content": "Augmentation drastique des investissements autant publiques", "level": 1}, {"type": "text", "bbox": [33.000001056, 343.49401099180795, 314.63701006838414, 366.6864295237228], "content": "Les investissements", "level": 1}, {"type": "text", "bbox": [360.4429240341732, 343.0980087291359, 671.7259339952295, 361.62881525204085], "content": "Augmentation drastique des investissements autant publiques de que priv\u00e9s.", "level": 1}]}, {"page_number": 13, "width": 720.0, "height": 405.0, "elements": [{"type": "text", "bbox": [22.107554729803596, 24.977064318107296, 416.3590101094951, 37.632110189666946], "content": "Exponential growth of computation in the training of notable Al systems", "level": 1}, {"type": "picture", "bbox": [33.46236038208008, 43.95885467529297, 83.4960708618164, 76.96517944335938], "content": "", "level": 1}, {"type": "section_header", "bbox": [33.000001056, 45.45600145459201, 85.704002742528, 86.68696773355163], "content": "01", "level": 1}, {"type": "text", "bbox": [36.84593008672932, 103.08316442382949, 61.90116335411367, 108.97363065892915], "content": "10 billion", "level": 1}, {"type": "text", "bbox": [31.674645041592637, 116.27016298695827, 61.914018252444485, 122.29373148506227], "content": "100 million", "level": 1}, {"type": "text", "bbox": [39.05668648823474, 130.21376156362533, 61.90116163661994, 136.21703829320904], "content": "1 million", "level": 1}, {"type": "text", "bbox": [41.982827110228534, 143.51256489548882, 62.65961427233759, 150.27445319128364], "content": "10,000", "level": 1}, {"type": "text", "bbox": [50.11046523892287, 157.5699796813315, 61.90116249961533, 164.1967546722182], "content": "100", "level": 1}, {"type": "text", "bbox": [56.742732624725136, 170.82352937681864, 62.63808125507137, 178.1866124882929], "content": "1", "level": 1}, {"type": "text", "bbox": [48.63662802507153, 184.81338728615904, 61.90116276103313, 191.44016227704574], "content": "0.01", "level": 1}, {"type": "text", "bbox": [41.99602377434187, 198.7750080729248, 61.909499765890466, 204.72194926842153], "content": "0.0001", "level": 1}, {"type": "text", "bbox": [34.60479764674751, 212.64937695276151, 61.93154010722142, 218.82729641057335], "content": "0.000001", "level": 1}, {"type": "text", "bbox": [28.002907433934208, 226.78296157916418, 61.90116301147276, 232.67342781426373], "content": "0.00000001", "level": 1}, {"type": "text", "bbox": [21.370640495922157, 240.77281969441776, 61.901162806076364, 246.66328592951734], "content": "0.0000000001", "level": 1}, {"type": "text", "bbox": [42.004361059425044, 254.02636929198366, 61.90116252800228, 259.91683552708326], "content": "< 1e-11", "level": 1}, {"type": "text", "bbox": [61.90116341050943, 249.60851936885993, 84.74563855889464, 255.49898560395954], "content": "Jul 2, 1950", "level": 1}, {"type": "text", "bbox": [61.1098903434738, 259.75567940902357, 89.22150445547562, 269.6499998585027], "content": "Theseus", "level": 1}, {"type": "text", "bbox": [62.63031634647635, 270.18875012300776, 93.59642783917973, 276.8883288325664], "content": "Jul 2, 1950", "level": 1}, {"type": "text", "bbox": [117.88981667776405, 270.1304956068322, 154.03314771673158, 276.9465835780908], "content": "Apr 19, 1965", "level": 1}, {"type": "picture", "bbox": [101.70133209228516, 43.334327697753906, 112.32302856445312, 78.72186279296875], "content": "", "level": 1}, {"type": "text", "bbox": [91.88115163155169, 229.163901745627, 151.87622760428044, 238.3349197861353], "content": "Perceptron-Mark 1", "level": 1}, {"type": "text", "bbox": [453.90064820031716, 24.851230180224775, 482.7228978998333, 31.844509935668327], "content": "Our World", "level": 1}, {"type": "picture", "bbox": [155.6893310546875, 31.07280731201172, 639.4571533203125, 313.6360778808594], "content": "", "level": 1}, {"type": "text", "bbox": [246.08613027059516, 321.80334720875004, 502.6104277166469, 332.1889697687437], "content": "La puissance de calcul n\u00e9cessaire est colossale mais fois l'industrie est pr\u00eate", "level": 1}, {"type": "text", "bbox": [245.39389478171037, 333.54766747669754, 374.3546503592488, 342.3833668293469], "content": "La puissance de calcul n\u00e9cessaire est colossale mais fois l'industrie est pr\u00eate", "level": 1}, {"type": "text", "bbox": [33.000001056, 343.49401099180795, 359.1060114913921, 366.6864295237228], "content": "La puissance de calcul", "level": 1}, {"type": "text", "bbox": [389.7657499725036, 343.0980087291359, 681.2847593011118, 361.62881525204085], "content": "La puissance de calcul n\u00e9cessaire est colossale mais cette fois l'industrie est pr\u00eate", "level": 1}]}, {"page_number": 14, "width": 720.0, "height": 405.0, "elements": [{"type": "text", "bbox": [23.98186943123119, 25.308880317205592, 318.4259781925605, 35.29922780755311], "content": "Test scores of Al systems on various capabilities relative to human", "level": 1}, {"type": "picture", "bbox": [33.599212646484375, 43.95479965209961, 83.4095230102539, 76.85913848876953], "content": "", "level": 1}, {"type": "section_header", "bbox": [33.000001056, 45.45600145459201, 85.704002742528, 86.68696773355163], "content": "01", "level": 1}, {"type": "text", "bbox": [30.643504545045317, 90.57915050588804, 39.96978853296073, 97.90540533214283], "content": "20", "level": 1}, {"type": "text", "bbox": [27.31268871627643, 151.18725872611006, 39.96978841416163, 158.51351355236486], "content": "-20", "level": 1}, {"type": "text", "bbox": [27.978851997054377, 182.49034742374522, 39.30362541095166, 189.15057908397685], "content": "-40", "level": 1}, {"type": "text", "bbox": [27.312688671865562, 212.46139001134168, 39.303625227756804, 219.12162167157337], "content": "-60", "level": 1}, {"type": "text", "bbox": [27.978851940686727, 243.09845557540098, 39.30362535458401, 249.09266406960947], "content": "-80", "level": 1}, {"type": "text", "bbox": [23.300655002204678, 273.0332112037897, 39.31867994523085, 279.76601666022367], "content": "-100", "level": 1}, {"type": "text", "bbox": [41.278212955464475, 287.0039397130771, 58.64625863269563, 295.1003073263061], "content": "1998", "level": 1}, {"type": "text", "bbox": [149.88670620753402, 121.2162162495174, 209.17522584499625, 127.876447909749], "content": "-Human-performance", "level": 1}, {"type": "text", "bbox": [87.93353442344409, 271.737451657529, 271.79456161377635, 279.729729649807], "content": "The capability of each Al system is normalized to an initial performanke", "level": 1}, {"type": "text", "bbox": [111.24924484622356, 287.05598462258683, 128.56948653806646, 295.0482626148648], "content": "2005", "level": 1}, {"type": "picture", "bbox": [173.3278045654297, 28.25189208984375, 590.5933227539062, 317.173583984375], "content": "", "level": 1}, {"type": "text", "bbox": [167.20694857941842, 287.05598448938224, 185.1933534132553, 295.04826248166023], "content": "2010", "level": 1}, {"type": "text", "bbox": [106.54220516604938, 322.9768367319284, 440.4050866449957, 336.9905834096069], "content": "Le test de Turing vole en \u00e9clat, l'IA devient plus performante que I", "level": 1}, {"type": "text", "bbox": [33.000001056, 343.49401099180795, 225.69900722236807, 366.6864295237228], "content": "Les r\u00e9sultats", "level": 1}, {"type": "text", "bbox": [267.8189035702046, 343.0980087291359, 682.8629168516129, 361.62881525204085], "content": "Le test de Turing vole en \u00e9clat, l'IA devient plus performante que les humains dans de nombreux nouveaux domaines", "level": 1}, {"type": "text", "bbox": [389.68956092309077, 25.901737975146887, 417.034004713953, 32.04227738195647], "content": "Our World", "level": 1}]}, {"page_number": 15, "width": 720.0, "height": 405.0, "elements": [{"type": "code", "bbox": [192.8503936712124, 176.9940311638088, 400.5462128174785, 236.1864103477223], "content": "Qui fait l'IA aujourd'hui ?", "level": 1}]}, {"page_number": 16, "width": 720.0, "height": 405.0, "elements": [{"type": "picture", "bbox": [30.46066665649414, 43.92731857299805, 671.4515991210938, 400.1571350097656], "content": "", "level": 1}]}, {"page_number": 17, "width": 720.0, "height": 405.0, "elements": [{"type": "text", "bbox": [21.333333296969695, 9.333333303030315, 30.66666663030303, 15.999999969697], "content": "00", "level": 1}, {"type": "text", "bbox": [21.333333303030308, 45.99999996666668, 30.66666663636364, 53.33333329999999], "content": "Al", "level": 1}, {"type": "text", "bbox": [22.66666664, 103.33333326000002, 29.333333306666663, 110.66666659333333], "content": "XI", "level": 1}, {"type": "text", "bbox": [18.235772717155818, 142.1707321817579, 32.47967428779384, 149.39024384964532], "content": "Alolams", "level": 1}, {"type": "text", "bbox": [52.666666633333335, 9.999999982456131, 65.3333333, 16.666666649122817], "content": "Met", "level": 1}, {"type": "text", "bbox": [53.1669915945188, 27.46161074022683, 65.62381303616918, 34.382067308626915], "content": "Per", "level": 1}, {"type": "picture", "bbox": [31.647491455078125, 43.33370590209961, 669.1283569335938, 324.2569885253906], "content": "", "level": 1}, {"type": "text", "bbox": [147.6387197244389, 345.162011045184, 470.585917558749, 363.20055879222883], "content": "Qui fait l'IA aujourd'hui ?", "level": 2}]}, {"page_number": 18, "width": 720.0, "height": 405.0, "elements": [{"type": "code", "bbox": [192.8503936712124, 176.9940311638088, 415.19540078625243, 236.1864103477223], "content": "Est-ce que l'IA r\u00e9fl\u00e9chit ?", "level": 1}]}, {"page_number": 19, "width": 720.0, "height": 405.0, "elements": [{"type": "picture", "bbox": [30.776288986206055, 41.2465705871582, 648.7175903320312, 367.4737243652344], "content": "", "level": 1}]}, {"page_number": 20, "width": 720.0, "height": 405.0, "elements": [{"type": "list", "bbox": [103.25035830401136, 136.33004811956147, 455.2725444142211, 157.66655649337685], "content": "L'IA ne pense pas c'est un syst\u00e8me probabiliste 01", "level": 2}, {"type": "list", "bbox": [103.25035830401136, 188.408849786083, 567.8169577866422, 209.74535815989844], "content": "L'IA est devenue une ' chose sociale ' avec l'av\u00e8nement des LLM 02", "level": 2}, {"type": "list", "bbox": [103.25035830401136, 251.17559429461875, 537.1201449548441, 272.5121026684342], "content": "Ces syst\u00e8mes ont des limitations malgr\u00e9 leur performances 03", "level": 2}, {"type": "section_header", "bbox": [33.000001056, 343.49401099180795, 210.87600674803207, 366.6864295237228], "content": "Key takeaway", "level": 1}]}, {"page_number": 21, "width": 720.0, "height": 405.0, "elements": [{"type": "text", "bbox": [192.8503936712124, 176.9940311638088, 430.01840126058846, 236.1864103477223], "content": "Des cas d'usages concrets", "level": 1}]}, {"page_number": 22, "width": 720.0, "height": 405.0, "elements": [{"type": "picture", "bbox": [33.375003814697266, 43.81018829345703, 82.93656158447266, 76.82691955566406], "content": "", "level": 1}, {"type": "text", "bbox": [190.500006096, 89.98044669837424, 198.18600606745198, 102.00614476691487], "content": "'", "level": 1}, {"type": "text", "bbox": [190.500006096, 106.78044573597418, 574.883647965276, 152.4061441297148], "content": "Explique-moi, en fran\u00e7ais simple, les obligations d'\u00e9tiquetage pour un spiritueux import\u00e9 en Allemagne.", "level": 1}, {"type": "text", "bbox": [190.500006096, 173.98043588637378, 574.8000046685995, 219.60612753011418], "content": "Base-toi uniquement sur des sources officielles (textes UE, autorit\u00e9s allemandes) et cite-moi les r\u00e9f\u00e9rences exactes (nom du r\u00e8glement, article).", "level": 1}, {"type": "text", "bbox": [190.500006096, 241.18042303677328, 582.6463309586819, 286.8061146805137], "content": "Pr\u00e9sente la r\u00e9ponse en 5 points pour un public non juriste. '", "level": 1}, {"type": "text", "bbox": [190.500006096, 355.0980091131359, 664.836021274752, 373.6288156360408], "content": "Poser une questions bien formul\u00e9e, sur une r\u00e8gle pr\u00e9cise en donnant des indications claires. La partie 4 de la pr\u00e9sentation donne des indications suppl\u00e9mentaires", "level": 1}, {"type": "section_header", "bbox": [485.1935255261924, 36.76200117638405, 692.7155321668965, 54.800548923428835], "content": "Poser une question", "level": 1}]}, {"page_number": 23, "width": 720.0, "height": 405.0, "elements": [{"type": "picture", "bbox": [33.37525939941406, 43.85982894897461, 82.94666290283203, 76.80525207519531], "content": "", "level": 1}, {"type": "section_header", "bbox": [33.000001056, 45.45600145459201, 85.704002742528, 86.68696773355163], "content": "02", "level": 1}, {"type": "text", "bbox": [190.500006096, 89.98044669837424, 198.18600606745198, 102.00614476691487], "content": "'", "level": 1}, {"type": "text", "bbox": [190.500006096, 106.78044573597418, 590.2347370840109, 152.4061441297148], "content": "Liste-moi les nouveaut\u00e9s r\u00e9glementaires officielles sur l'\u00e9tiquetage des spiritueux dans l'UE et au Japon de la semaine, avec :", "level": 1}, {"type": "list", "bbox": [190.500006096, 157.18044284877402, 444.1380051539156, 169.20614091731468], "content": "lien vers la source officielle,", "level": 2}, {"type": "list", "bbox": [190.500006096, 173.98043588637378, 351.9060054964918, 186.00613395491445], "content": "r\u00e9sum\u00e9 en 5 lignes,", "level": 2}, {"type": "list", "bbox": [190.500006096, 190.78043642397375, 551.7420047542435, 219.60612753011418], "content": "impact probable pour une maison de Cognac de taille moyenne.", "level": 2}, {"type": "text", "bbox": [190.500006096, 241.18042303677328, 528.7397695376719, 286.8061146805137], "content": "Regroupe chaque \u00e9l\u00e9ment dans un tableau par importance. '", "level": 1}, {"type": "section_header", "bbox": [300.46308211481835, 36.76200117638405, 692.4490946583703, 54.800548923428835], "content": "Organiser une veille r\u00e9glementaire", "level": 1}]}, {"page_number": 24, "width": 720.0, "height": 405.0, "elements": [{"type": "section_header", "bbox": [33.000001056, 45.45600145459201, 85.704002742528, 86.68696773355163], "content": "02", "level": 1}, {"type": "section_header", "bbox": [158.08292005865326, 114.06220400749038, 238.25866959877715, 126.26876643747539], "content": "AVANTAGES", "level": 1}, {"type": "text", "bbox": [29.970530361046617, 194.00000003333332, 49.95088399562422, 206.66666669999998], "content": "02", "level": 1}, {"type": "table", "bbox": [51.216835021972656, 148.1437530517578, 314.2213134765625, 296.03851318359375], "content": "|   01 | Une veille automatis\u00e9e           |\n|------|----------------------------------|\n|   02 | Des r\u00e9f\u00e9rences d\u00e9j\u00e0 qualifi\u00e9es   |\n|   03 | Un r\u00e9sum\u00e9 g\u00e9n\u00e9r\u00e9 automatiquement |", "level": 1}, {"type": "section_header", "bbox": [530.177484465679, 114.06515150758469, 582.3692341663149, 126.2717139375697], "content": "LIMITES", "level": 1}, {"type": "table", "bbox": [407.55133056640625, 147.9605255126953, 667.1661376953125, 251.74072265625], "content": "|   01 | L'IA ne voit pas tout                                           |\n|------|-----------------------------------------------------------------|\n|   02 | Les responsabilit\u00e9 r\u00e9glementaires ne doivent pas \u00eatre d\u00e9l\u00e9gu\u00e9es |\n|   03 | Les r\u00e9sum\u00e9s ne sont qu'une 'indication'                         |", "level": 1}]}, {"page_number": 25, "width": 720.0, "height": 405.0, "elements": [{"type": "picture", "bbox": [33.541908264160156, 43.85692596435547, 83.05735778808594, 76.86279296875], "content": "", "level": 1}, {"type": "text", "bbox": [190.500006096, 89.98044669837424, 198.18600606745198, 102.00614476691487], "content": "'", "level": 1}, {"type": "section_header", "bbox": [190.500006096, 106.78044573597418, 236.61600592471197, 118.80614380451482], "content": "Cas 1", "level": 1}, {"type": "text", "bbox": [190.500006096, 123.58044177357402, 520.9980048684356, 169.20614091731468], "content": "Utilisation de l'IA propos\u00e9e par Canva : Elle vous aidera \u00e0 r\u00e9diger et pr\u00e9parer vos communication", "level": 1}, {"type": "section_header", "bbox": [190.500006096, 190.78043642397375, 236.61600592471197, 202.80613449251442], "content": "Cas 2", "level": 1}, {"type": "text", "bbox": [190.500006096, 207.58042946157352, 475.1259746550307, 219.60612753011418], "content": "Utilisation de l'IA dans Powerpoint :", "level": 1}, {"type": "text", "bbox": [190.500006096, 224.38042249917328, 551.7420047542435, 236.40612056771394], "content": "Elle vous aidera \u00e0 formaliser vos pr\u00e9sentations", "level": 1}, {"type": "section_header", "bbox": [190.500006096, 257.980416074373, 236.61600592471197, 270.0061141429137], "content": "Cas 3", "level": 1}, {"type": "text", "bbox": [190.500006096, 274.780416611973, 559.4280047256955, 337.2061687933154], "content": "Utilisation de l'IA dans Notion : Elle vous aidera dans vos prise de notes et vos compte-rendus '", "level": 1}, {"type": "text", "bbox": [190.500006096, 355.0980091131359, 635.1900203260801, 373.6288156360408], "content": "Int\u00e9grer l'IA dans un outil existant, soit par des connecteurs soit par des modules mis \u00e0 disposition", "level": 1}, {"type": "section_header", "bbox": [277.3712863758809, 36.76200117638405, 692.4152996572889, 54.800548923428835], "content": "Int\u00e9grer l'IA dans un outil existant", "level": 1}]}, {"page_number": 26, "width": 720.0, "height": 405.0, "elements": [{"type": "section_header", "bbox": [33.000001056, 45.45600145459201, 85.704002742528, 86.68696773355163], "content": "02", "level": 1}, {"type": "text", "bbox": [52.05413416573224, 150.38222281223094, 71.81813479818024, 165.8438351668408], "content": "01", "level": 1}, {"type": "text", "bbox": [29.97053035196464, 194.00000003666665, 49.95088398654224, 206.66666670333333], "content": "02", "level": 1}, {"type": "text", "bbox": [52.05413416573224, 194.25718671622977, 71.81813479818024, 209.71879907083962], "content": "02", "level": 1}, {"type": "section_header", "bbox": [158.08292005865326, 114.06220400749038, 238.25866959877715, 126.26876643747539], "content": "AVANTAGES", "level": 1}, {"type": "text", "bbox": [85.73322274346303, 153.14783290073052, 256.631228212199, 162.83448705685728], "content": "Automatisation de t\u00e2che fastidieuses", "level": 1}, {"type": "text", "bbox": [85.73382274348225, 197.02318680474178, 299.21982957503417, 206.70984096086855], "content": "Possibilit\u00e9 d'am\u00e9liorer la qualit\u00e9 de production", "level": 1}, {"type": "list", "bbox": [52.05511666576368, 269.69775863032805, 346.36439608366004, 295.56492463664875], "content": "\u2192 Implication : Il faut ma\u00eetriser le niveau 1 et comprendre comment l'int\u00e9grer dans vos outils", "level": 2}, {"type": "section_header", "bbox": [530.177484465679, 114.06515150758469, 582.3692341663149, 126.2717139375697], "content": "LIMITES", "level": 1}, {"type": "table", "bbox": [409.3624267578125, 148.3838653564453, 669.6690673828125, 206.67929077148438], "content": "|   01 | Il faut toujours bien relire la production de l'IA   |\n|------|------------------------------------------------------|\n|   02 | Attention aux informations donn\u00e9es                   |", "level": 1}]}, {"page_number": 27, "width": 720.0, "height": 405.0, "elements": [{"type": "picture", "bbox": [33.26869201660156, 43.75563430786133, 83.02775573730469, 76.86121368408203], "content": "", "level": 1}, {"type": "section_header", "bbox": [33.000001056, 45.45600145459201, 85.704002742528, 86.68696773355163], "content": "02", "level": 1}, {"type": "section_header", "bbox": [265.82538700641214, 36.76200117638405, 692.8884521724295, 54.800548923428835], "content": "Construire un assistant \u00e0 VOS process", "level": 1}, {"type": "list", "bbox": [190.500006096, 89.98044669837424, 582.5557123872821, 286.8061146805137], "content": "' Un outil sur mesure qui conna\u00eet : - vos documentations, - vos contraintes, Qui sait : - g\u00e9n\u00e9rer des devis sur vos gammes de produits, - publier des communications avec votre charte graphiques - r\u00e9pondre automatiquement aux demande d'un client selon votre SAV/FAQ '", "level": 2}, {"type": "text", "bbox": [190.500006096, 355.0980091131359, 679.659021749088, 373.6288156360408], "content": "On int\u00e8gre cette fois-ci l'IA dans vos donn\u00e9es d'entreprise, vos processus propre. C'est le niveau le plus exigeant en terme de maturit\u00e9", "level": 1}]}, {"page_number": 28, "width": 720.0, "height": 405.0, "elements": [{"type": "section_header", "bbox": [33.000001056, 45.45600145459201, 85.704002742528, 86.68696773355163], "content": "02", "level": 1}, {"type": "text", "bbox": [29.970530435216105, 149.33333340333334, 50.61689585761297, 163.33333340333328], "content": "01", "level": 1}, {"type": "text", "bbox": [29.97053036583988, 193.3333333666667, 50.61689578823674, 206.66666670000004], "content": "02", "level": 1}, {"type": "text", "bbox": [29.970530544368035, 237.3333334111111, 50.61689596676488, 251.3333334111111], "content": "03", "level": 1}, {"type": "section_header", "bbox": [158.08292005865326, 114.06220400749038, 238.25866959877715, 126.26876643747539], "content": "AVANTAGES", "level": 1}, {"type": "section_header", "bbox": [530.177484465679, 114.06515150758469, 582.3692341663149, 126.2717139375697], "content": "LIMITES", "level": 1}, {"type": "table", "bbox": [50.244747161865234, 144.0395050048828, 671.7445068359375, 256.70684814453125], "content": "|   01 | C'est l\u00e0 qu'on a les meilleurs ROI                   | Le niveau de maturit\u00e9 de l'entreprise doit \u00eatre suffisamment bon   |\n|------|------------------------------------------------------|--------------------------------------------------------------------|\n|   02 | Offre de v\u00e9ritables opportunit\u00e9s business            | Il faut \u00eatre accompagn\u00e9s par des gens comp\u00e9tents                   |\n|   03 | Permet la mise en valeur des donn\u00e9es de l'entreprise | Les donn\u00e9es de l'entreprises doivent \u00eatre 'activables'             |", "level": 1}, {"type": "list", "bbox": [38.38386122828352, 269.6968286302982, 349.53439118509976, 295.563994636619], "content": "\u2192 Implication : Il faut ma\u00eetriser les niveau 1,2 et 3 et avoir un \u00e9quipe pr\u00eate \u00e0 conduire le projet", "level": 2}]}, {"page_number": 29, "width": 720.0, "height": 405.0, "elements": [{"type": "text", "bbox": [192.8503936712124, 176.9940311638088, 444.8414017349245, 236.1864103477223], "content": "Les limitations, les pi\u00e8ges", "level": 1}]}, {"page_number": 30, "width": 720.0, "height": 405.0, "elements": [{"type": "section_header", "bbox": [192.8503936712124, 194.99401373980822, 459.6644022092605, 218.18643227172302], "content": "Un impact soci\u00e9tal", "level": 1}]}, {"page_number": 31, "width": 720.0, "height": 405.0, "elements": [{"type": "text", "bbox": [13.323354842252954, 14.651452254408696, 199.18413328536676, 21.977178395487556], "content": "FRED 2 \u2014 Software Development Job Postings on Indeed in the United States", "level": 1}, {"type": "text", "bbox": [26.646706695078592, 23.309128697302924, 36.63922166513847, 28.63692952717838], "content": "240", "level": 1}, {"type": "picture", "bbox": [33.623985290527344, 43.7328987121582, 83.6491928100586, 77.09943389892578], "content": "", "level": 1}, {"type": "section_header", "bbox": [33.000001056, 45.45600145459201, 85.704002742528, 86.68696773355163], "content": "03", "level": 1}, {"type": "text", "bbox": [26.64670646191991, 109.21991704574691, 36.639221431979784, 114.54771787562242], "content": "180", "level": 1}, {"type": "text", "bbox": [27.31287440693612, 138.52282153236513, 36.639221712325345, 142.51867215477182], "content": "160", "level": 1}, {"type": "text", "bbox": [26.646706686751497, 167.15975100404563, 36.63922165681138, 171.82157673018673], "content": "140", "level": 1}, {"type": "text", "bbox": [26.646706686751497, 195.79668046462658, 36.63922165681138, 200.45850619076768], "content": "120", "level": 1}, {"type": "text", "bbox": [26.646706493562878, 224.433609991805, 35.973053798952094, 229.09543571794606], "content": "100", "level": 1}, {"type": "text", "bbox": [29.311377337107032, 253.0705393941131, 36.63922164848428, 257.73236512025414], "content": "80", "level": 1}, {"type": "text", "bbox": [29.977544832460083, 281.70746880197095, 35.973053814496005, 286.369294528112], "content": "60", "level": 1}, {"type": "picture", "bbox": [172.55902099609375, 28.558191299438477, 594.2506713867188, 316.8949890136719], "content": "", "level": 1}, {"type": "text", "bbox": [45.22661113939693, 288.7559661779374, 64.6910540121125, 294.6382246129453], "content": "2020-07", "level": 1}, {"type": "text", "bbox": [84.59836993948457, 289.01591486914225, 103.26091180864238, 294.37827593620585], "content": "2021-01", "level": 1}, {"type": "text", "bbox": [123.24101811951095, 288.3672198892635, 141.8937127302894, 294.3609958228734], "content": "2021-07", "level": 1}, {"type": "text", "bbox": [161.87874232844314, 289.0331950473859, 180.5314369392216, 294.3609958772614], "content": "2022-01", "level": 1}, {"type": "picture", "bbox": [693.6666259765625, 374.01129150390625, 710.192626953125, 399.2247619628906], "content": "", "level": 1}]}, {"page_number": 32, "width": 720.0, "height": 405.0, "elements": [{"type": "text", "bbox": [15.354664556429043, 15.330073464739087, 499.43022607986654, 30.666666456885878], "content": "Al jobs are on the rise in the Software Development sector", "level": 1}, {"type": "text", "bbox": [14.54651218204313, 55.67237152085272, 31.517441645747557, 62.93398536667786], "content": "23%", "level": 1}, {"type": "text", "bbox": [14.546512133554762, 106.50366736437348, 31.517441597259193, 113.76528121019868], "content": "21%", "level": 1}, {"type": "text", "bbox": [14.546512133554762, 157.33496320789428, 31.517441597259193, 164.59657705371944], "content": "19%", "level": 1}, {"type": "text", "bbox": [14.546512133554762, 208.16625906572781, 31.517441597259193, 214.62102692868356], "content": "17%", "level": 1}, {"type": "text", "bbox": [14.546512022435579, 258.9975553439397, 31.517441486140005, 266.2591685539821], "content": "15%", "level": 1}, {"type": "picture", "bbox": [33.66847610473633, 43.781349182128906, 83.5766830444336, 77.15392303466797], "content": "", "level": 1}, {"type": "section_header", "bbox": [33.000001056, 45.45600145459201, 85.704002742528, 86.68696773355163], "content": "03", "level": 1}, {"type": "picture", "bbox": [141.95504760742188, 56.50746536254883, 670.9074096679688, 310.82989501953125], "content": "", "level": 1}, {"type": "text", "bbox": [136.50046049063005, 276.5342948321003, 156.85419101024266, 284.2236513181029], "content": "2020", "level": 1}, {"type": "picture", "bbox": [693.7492065429688, 374.0291748046875, 710.1932373046875, 399.221435546875], "content": "", "level": 1}]}, {"page_number": 33, "width": 720.0, "height": 405.0, "elements": [{"type": "text", "bbox": [192.8503936712124, 176.9940311638088, 504.41585364130685, 236.1864103477223], "content": "Le ' shadow IA ' et la propri\u00e9t\u00e9 des donn\u00e9es", "level": 1}]}, {"page_number": 34, "width": 720.0, "height": 405.0, "elements": [{"type": "text", "bbox": [31.33333372499999, 26.640926607625488, 94.00000039166666, 33.30115826785715], "content": "= L'USINEDIGITALE", "level": 1}, {"type": "text", "bbox": [31.96313416217357, 27.306949762548243, 94.55760420825652, 33.9671814227799], "content": "= L'USINEDIGITALE", "level": 1}, {"type": "picture", "bbox": [33.51954650878906, 43.596275329589844, 83.59996032714844, 77.1068115234375], "content": "", "level": 1}, {"type": "text", "bbox": [101.21658982968938, 69.93243242626556, 119.86175112001196, 73.26254825638136], "content": "CHATBOTI", "level": 1}, {"type": "section_header", "bbox": [31.68000238894456, 99.90347498672782, 255.999999825, 135.86872598527992], "content": "avoir trait\u00e9 ill\u00e9galement les donn\u00e9es personnelles dans ChatGPT violation des donn\u00e9es personnelles Un avocat a saisi la Cnil, reprochant \u00e0 la start-up d'exploiter les", "level": 1}, {"type": "text", "bbox": [32.60307832055364, 137.08085814104777, 248.40552214570843, 146.13719696310693], "content": "informations des utilisateurs de son chat gratuit pour entrainer plus de clart\u00e9 sur le sujet.", "level": 1}, {"type": "text", "bbox": [33.96083038539168, 168.50386101052123, 123.19124513193547, 177.16216216882242], "content": "informations des utilisateurs de son chat gratuit pour entrainer plus de clart\u00e9 sur le sujet.", "level": 1}, {"type": "text", "bbox": [32.61730926000704, 142.52895760111005, 243.05299748262675, 172.0891291195975], "content": "L'autorit\u00e9 italienne de protection des donn\u00e9es a inflig\u00e9 une amende de 15 millions d'euros \u00e0 OpenAl pour ne pas avoir trait\u00e9 l\u00e9galement les donn\u00e9es des utilisateurs dans son des mod\u00e8les d'IA. Celle-ci d\u00e9ment fermement. Elle vient de mettre \u00e0 jour ses conditions g\u00e9n\u00e9rales d'utilisation pour offrir chatbot.", "level": 1}, {"type": "text", "bbox": [32.61770600506477, 174.29723538192684, 62.04896007407518, 182.02515849901587], "content": "L'autorit\u00e9 italienne de protection des donn\u00e9es a inflig\u00e9 une amende de 15 millions d'euros \u00e0 OpenAl pour ne pas avoir trait\u00e9 l\u00e9galement les donn\u00e9es des utilisateurs dans son des mod\u00e8les d'IA. Celle-ci d\u00e9ment fermement. Elle vient de mettre \u00e0 jour ses conditions g\u00e9n\u00e9rales d'utilisation pour offrir chatbot.", "level": 1}, {"type": "text", "bbox": [33.96082992592164, 207.79922778257722, 68.58755803652072, 211.79536677871621], "content": "Samia Lokmane", "level": 1}, {"type": "text", "bbox": [33.33333301666668, 213.12741316071433, 58.666666350000014, 217.1235521568533], "content": "Alice Vitard", "level": 1}, {"type": "text", "bbox": [33.96082952591354, 218.45559845841262, 81.90552998674302, 222.45173745455165], "content": "13 f\u00e9vrier 2025 | 14h00", "level": 1}, {"type": "text", "bbox": [44.666667333333315, 223.78378371718154, 90.00000066666662, 227.7799227133205], "content": "CLUB DATA PROTECTION", "level": 1}, {"type": "text", "bbox": [44.0000005222222, 227.77992274662157, 75.33333385555552, 231.11003857673745], "content": "BY L'USINE DIGITALE", "level": 1}, {"type": "text", "bbox": [33.294930154185884, 227.11389963610043, 76.57834029243473, 231.1100386322394], "content": "Mis \u00e0 jour 13 F\u00e9vr. 2025", "level": 1}, {"type": "text", "bbox": [33.3333333, 239.7683397659325, 88.66666663333335, 243.76447876207146], "content": "30 d\u00e9cembre 2024 14h15", "level": 1}, {"type": "text", "bbox": [34.62673004659849, 247.76061774951737, 74.58064455708809, 252.42277991167953], "content": "\u00b7 2 min. de lecture", "level": 1}, {"type": "text", "bbox": [33.999999533333344, 260.41505784845566, 73.99999953333334, 265.743243176641], "content": "\u00a9 2 min. de lecture", "level": 1}, {"type": "text", "bbox": [34.59936805418645, 266.9092515581321, 61.95593239578867, 271.9034896990925], "content": "\u00b7 R\u00e9agir \u2192", "level": 1}, {"type": "text", "bbox": [33.99320942752954, 279.68966048141215, 61.3401230831285, 284.4319611460083], "content": "\u00b7 R\u00e9agir \u2192", "level": 1}, {"type": "text", "bbox": [41.28571425241935, 281.7277991745173, 44.61520733997696, 287.0559845027027], "content": "f", "level": 1}, {"type": "text", "bbox": [51.94009209930875, 281.7277991528716, 58.59907827442396, 287.7220076470801], "content": "in", "level": 1}, {"type": "text", "bbox": [40.66666669166667, 295.0482626148648, 44.000000025, 300.3764479430502], "content": "f", "level": 1}, {"type": "picture", "bbox": [143.91578674316406, 20.09053611755371, 384.9136047363281, 319.3006286621094], "content": "", "level": 1}, {"type": "text", "bbox": [51.33333328, 295.0482624949807, 57.99999994666667, 300.376447823166], "content": "in", "level": 1}, {"type": "section_header", "bbox": [33.000001056, 355.494011375808, 418.3980133887361, 378.6864299077228], "content": "Utilisation de vos donn\u00e9es", "level": 1}, {"type": "text", "bbox": [129.18433097596008, 47.287644721042454, 179.79262590683564, 51.28378371718145], "content": "Reconnaissance vocale", "level": 1}, {"type": "text", "bbox": [127.333332725, 47.28764480429538, 175.99999939166665, 51.28378380043438], "content": "Reconnaissance vocale", "level": 1}, {"type": "text", "bbox": [131.68205131112842, 233.63068006912602, 153.76410290618048, 240.3421444760269], "content": "\u20ac OpenAI", "level": 1}, {"type": "text", "bbox": [113.92479908041942, 266.71933649071406, 176.79990080567984, 282.3323121488509], "content": "OpenAl is an Al res company: Our m", "level": 1}, {"type": "text", "bbox": [110.04055572043659, 273.9978080810565, 163.32123605897826, 286.54918060176846], "content": "OpenAl is an Al res company: Our m", "level": 1}, {"type": "text", "bbox": [106.89288263524479, 280.59630724680295, 139.12209762141015, 289.36237025671306], "content": "general in", "level": 1}, {"type": "picture", "bbox": [441.8902893066406, 20.766611099243164, 683.239501953125, 321.18011474609375], "content": "", "level": 1}, {"type": "text", "bbox": [434.07876639052006, 349.69800969033594, 676.1877741380081, 379.0288158088408], "content": "Les donn\u00e9es sont centrales \u00e0 la course \u00e0 la qualit\u00e9 qui est en cours. Vos conversations sont des mines d'or pour l'entra\u00eenement.", "level": 1}, {"type": "picture", "bbox": [688.8504028320312, 373.25811767578125, 715.0690307617188, 399.4991149902344], "content": "", "level": 1}]}, {"page_number": 35, "width": 720.0, "height": 405.0, "elements": [{"type": "list", "bbox": [103.25035830401136, 136.33004811956147, 573.390134814484, 157.66655649337685], "content": "Ne mettez pas n'importe quelle donn\u00e9es dans les moteurs de LLM 01", "level": 2}, {"type": "list", "bbox": [103.25035830401136, 193.75282495709018, 516.9702816175486, 215.08933333090562], "content": "Ne croyez pas toutes les informations qui viennent des IA 02", "level": 2}, {"type": "list", "bbox": [103.25035830401136, 251.17559429461875, 463.8691104838112, 272.5121026684342], "content": "Validez l'utilisation des outils au niveau entreprise 03", "level": 2}, {"type": "section_header", "bbox": [33.000001056, 343.49401099180795, 210.87600674803207, 366.6864295237228], "content": "Key takeaway", "level": 1}]}, {"page_number": 36, "width": 720.0, "height": 405.0, "elements": [{"type": "text", "bbox": [192.8503936712124, 176.9940311638088, 415.19540078625243, 236.1864103477223], "content": "Une technologie \u00e9nergivore", "level": 1}]}, {"page_number": 37, "width": 720.0, "height": 405.0, "elements": [{"type": "text", "bbox": [30.212211226687362, 24.610687211706477, 108.92296396709897, 31.755725180659965], "content": "\u00b7 DeepSeek/DeepSeek R1", "level": 1}, {"type": "text", "bbox": [27.82703484491451, 38.900763366454555, 62.014534844914515, 43.664122118387354], "content": "SEMI-OUVERT", "level": 1}, {"type": "text", "bbox": [69.17006063874545, 38.90076333243053, 131.9796502338199, 43.66666664638524], "content": "671 MDS DE PARAM\u00c8TRES", "level": 1}, {"type": "picture", "bbox": [33.74888610839844, 43.68434524536133, 83.70018768310547, 77.148193359375], "content": "", "level": 1}, {"type": "section_header", "bbox": [33.000001056, 45.45600145459201, 85.704002742528, 86.68696773355163], "content": "03", "level": 1}, {"type": "picture", "bbox": [101.88833618164062, 43.099815368652344, 112.1905288696289, 78.69847106933594], "content": "", "level": 1}, {"type": "text", "bbox": [35.71862354772252, 95.72739589723972, 83.54009833626283, 105.1275659055812], "content": "671 milliards param.", "level": 1}, {"type": "text", "bbox": [40.547966312531074, 104.66666673638844, 79.50581373118258, 110.35114486037116], "content": "taille du mod\u00e8le", "level": 1}, {"type": "text", "bbox": [25.441860779273046, 127.00000001962246, 82.68604505258743, 133.37404570952322], "content": "Ce qui correspond \u00e0 :", "level": 1}, {"type": "text", "bbox": [62.809593498278495, 143.6946566268375, 77.91569746971567, 152.4274809523991], "content": "20g", "level": 1}, {"type": "text", "bbox": [62.797467473539996, 151.59845731285995, 83.49322981974032, 158.8137562834003], "content": "COz \u00e9mis", "level": 1}, {"type": "text", "bbox": [123.21057109864087, 95.96877729703994, 154.26471897927868, 104.09229147245497], "content": "705 tokens taille du texte", "level": 1}, {"type": "text", "bbox": [121.64389481321855, 105.5877863125832, 155.03633691010995, 110.351145064516], "content": "705 tokens taille du texte", "level": 1}, {"type": "text", "bbox": [133.56976765777475, 143.69465655968907, 143.1104650366605, 150.83969452864247], "content": "6h ampoule LED", "level": 1}, {"type": "text", "bbox": [133.56976823073055, 152.33333335320668, 161.39680246427915, 157.33333335320668], "content": "6h ampoule LED", "level": 1}, {"type": "text", "bbox": [127.96842449692929, 173.72079284836823, 149.50686638146146, 179.5616497183837], "content": "Voir plus why the sky is blue", "level": 1}, {"type": "text", "bbox": [212.28052520727832, 4.302074785300647e-08, 336.3095885912101, 8.732824368582271], "content": "Voir plus why the sky is blue", "level": 1}, {"type": "section_header", "bbox": [333.8615364335688, 16.616378031724082, 467.7455407178568, 27.671378385484104], "content": "'Why the sky is blue ?'", "level": 1}, {"type": "text", "bbox": [295.7616255378487, 53.190839882376906, 508.8372045689345, 58.7480914947223], "content": "Mod\u00e8le de troisi\u00e8me g\u00e9n\u00e9ration de la famille Gemma de Google. Malgr\u00e9 sa moyenne taille, il pouvait rivaliser \u00e0 sa sortie avec de nombreux mod\u00e8les propri\u00e9taires suppos\u00e9s bien plus gros.", "level": 1}, {"type": "text", "bbox": [295.7616364291932, 60.99999986809257, 509.6322631970567, 66.6870227130429], "content": "Mod\u00e8le de troisi\u00e8me g\u00e9n\u00e9ration de la famille Gemma de Google. Malgr\u00e9 sa moyenne taille, il pouvait rivaliser \u00e0 sa sortie avec de nombreux mod\u00e8les propri\u00e9taires suppos\u00e9s bien plus gros.", "level": 1}, {"type": "text", "bbox": [394.34883814079643, 95.99999991154226, 424.5610460836708, 103.99999991154226], "content": "492 tokens taille du texte", "level": 1}, {"type": "text", "bbox": [392.75872263935145, 105.58778624642548, 426.1511621946602, 110.35114499835822], "content": "492 tokens taille du texte", "level": 1}, {"type": "text", "bbox": [403.83846881231835, 143.54455171694588, 424.6121128774784, 150.98979904920088], "content": "37 min ampoule LED", "level": 1}, {"type": "text", "bbox": [403.88953486096915, 152.33333328688184, 431.7165690945178, 157.33333328688184], "content": "37 min ampoule LED", "level": 1}, {"type": "text", "bbox": [398.3241287813699, 173.86259566049398, 420.5857551515757, 179.41984727283935], "content": "Voir plus", "level": 1}, {"type": "picture", "bbox": [140.2661590576172, 30.715187072753906, 391.4759521484375, 215.63479614257812], "content": "", "level": 1}, {"type": "text", "bbox": [282.56716654214904, 237.47868226831756, 519.1201822688454, 250.0514367064944], "content": "Eau (L) \u2248 Energie (Wh)\u00d74 L/kWh / 1000", "level": 2}, {"type": "picture", "bbox": [411.0509033203125, 30.875667572021484, 662.96044921875, 215.4976348876953], "content": "", "level": 1}, {"type": "text", "bbox": [219.24170201573423, 276.04793383353365, 318.17758018168223, 294.57874185643857], "content": "L'\u00e9quivalent de 3-4 gorg\u00e9es d'eau", "level": 1}, {"type": "text", "bbox": [470.34838505114783, 276.04793383353365, 603.864019323648, 294.57874185643857], "content": "L'\u00e9quivalent d' une cuill\u00e8re \u00e0 caf\u00e9 d'eau", "level": 1}, {"type": "picture", "bbox": [688.908935546875, 373.7386474609375, 714.2738647460938, 399.5003967285156], "content": "", "level": 1}]}, {"page_number": 38, "width": 720.0, "height": 405.0, "elements": [{"type": "section_header", "bbox": [192.8503936712124, 194.99401373980822, 533.7794045809405, 218.18643227172302], "content": "Comment bien d\u00e9marrer ?", "level": 1}]}, {"page_number": 39, "width": 720.0, "height": 405.0, "elements": [{"type": "text", "bbox": [192.8503936712124, 176.9940311638088, 489.701483170447, 236.1864103477223], "content": "Devenir un pro du Prompt Engineering !", "level": 1}]}, {"page_number": 40, "width": 720.0, "height": 405.0, "elements": [{"type": "picture", "bbox": [33.56183624267578, 43.928253173828125, 85.3149185180664, 77.02198791503906], "content": "", "level": 1}, {"type": "section_header", "bbox": [33.000001056, 45.45600145459201, 85.704002742528, 86.68696773355163], "content": "04", "level": 1}, {"type": "picture", "bbox": [100.78213500976562, 177.2477264404297, 618.6835327148438, 288.79547119140625], "content": "", "level": 1}]}, {"page_number": 41, "width": 720.0, "height": 405.0, "elements": [{"type": "text", "bbox": [45.00000144, 275.2228880571321, 131.055754193784, 282.3094603863283], "content": "LES QUATRES PILIERS", "level": 1}, {"type": "text", "bbox": [45.00000144, 295.81688371613996, 254.10450813134403, 361.8230395447548], "content": "La pr\u00e9cision de la r\u00e9ponse d\u00e9pend de la qualit\u00e9 de votre question", "level": 1}, {"type": "section_header", "bbox": [439.00992654831725, 36.76200117638405, 692.6479346647334, 54.800548923428835], "content": "Structurer sa question", "level": 1}, {"type": "text", "bbox": [295.500009456, 153.33619165675796, 345.321761050296, 160.42276398595416], "content": "INSTRUCTION", "level": 1}, {"type": "text", "bbox": [289.50000926399997, 172.78244802903816, 442.2960141534719, 203.50692512416168], "content": "La t\u00e2che que l'on souhaite ex\u00e9cuter", "level": 1}, {"type": "text", "bbox": [295.500009456, 323.8205946122587, 367.968011774976, 330.9071669414549], "content": "DONNEES D'ENTREE", "level": 1}, {"type": "text", "bbox": [295.500009456, 343.2670159845442, 420.70801346265597, 353.8314939345477], "content": "L'information \u00e0 traiter", "level": 1}, {"type": "text", "bbox": [506.2500162, 153.33619165675796, 537.954767214552, 160.42276398595416], "content": "CONTEXT", "level": 1}, {"type": "text", "bbox": [506.2500162, 172.7826805290456, 641.946020542272, 203.5071576241691], "content": "Les \u00e9l\u00e9ments utiles \u00e0 la r\u00e9alisation de la t\u00e2che", "level": 1}, {"type": "text", "bbox": [506.2500162, 323.8205946122587, 578.718018518976, 330.9071669414549], "content": "FORMAT DE SORTIE", "level": 1}, {"type": "text", "bbox": [506.2500162, 343.2670159845442, 675.6420216205439, 353.8314939345477], "content": "Le format de r\u00e9ponse attendu", "level": 1}]}, {"page_number": 42, "width": 720.0, "height": 405.0, "elements": [{"type": "text", "bbox": [90.70866665267724, 161.02700690286406, 633.4446615202286, 251.39371946250918], "content": "Steering, en prompt engineering, fait r\u00e9f\u00e9rence \u00e0 l a capacit\u00e9 de guider ou orienter le mod\u00e8le d'IA vers un type de r\u00e9ponse ou un comportement  souhait\u00e9  par  des instructions  ou  contraintes sp\u00e9cifiques dans le prompt.", "level": 1}, {"type": "text", "bbox": [33.000001056, 355.494011375808, 255.34500817104006, 378.6864299077228], "content": "Prompt steering", "level": 1}]}, {"page_number": 43, "width": 720.0, "height": 405.0, "elements": [{"type": "section_header", "bbox": [33.000001056, 45.45600145459201, 85.704002742528, 86.68696773355163], "content": "04", "level": 1}, {"type": "picture", "bbox": [50.61665344238281, 108.63518524169922, 617.981201171875, 317.9120788574219], "content": "", "level": 1}, {"type": "section_header", "bbox": [33.000001056, 355.494011375808, 255.34500817104006, 378.6864299077228], "content": "Prompt chaining", "level": 1}, {"type": "text", "bbox": [282.750009048, 360.49800853593587, 574.2690183766082, 368.2288147132408], "content": "Encha\u00eener les sorties d'un prompt pour le passer au suivant", "level": 1}]}, {"page_number": 44, "width": 720.0, "height": 405.0, "elements": [{"type": "picture", "bbox": [33.45256042480469, 43.84069061279297, 85.4837417602539, 77.18612670898438], "content": "", "level": 1}, {"type": "text", "bbox": [33.000001056, 45.45600145459201, 85.704002742528, 86.68696773355163], "content": "04", "level": 1}, {"type": "picture", "bbox": [34.250335693359375, 353.8175964355469, 150.1731719970703, 373.8562316894531], "content": "", "level": 1}, {"type": "text", "bbox": [162.83072021058285, 32.819999902739994, 547.6907187811022, 78.74521881275734], "content": "Tu es un assistant Ma\u00eetre de Chai. Tu g\u00e9n\u00e8res des notes de d\u00e9gustation courtes, en 2 phrases, au format : Nez / Bouche.", "level": 1}, {"type": "section_header", "bbox": [162.83072021058285, 100.01999605313978, 233.26471994897076, 112.34521988795734], "content": "Exemples :", "level": 1}, {"type": "section_header", "bbox": [162.83072021058285, 133.6199896283395, 405.1847193104106, 145.94521346315713], "content": "F\u00fbt 14 - Ch\u00eane du Limousin, 12 ans", "level": 1}, {"type": "text", "bbox": [162.83072021058285, 150.4199826659393, 569.166718701334, 179.54519953835663], "content": "Nez : fruits confits, vanille, l\u00e9g\u00e8re touche de noix de coco grill\u00e9e. Bouche : attaque ronde, finale \u00e9pic\u00e9e sur le poivre blanc, long.", "level": 2}, {"type": "section_header", "bbox": [162.83072021058285, 200.81996927873882, 382.0007193965226, 213.1451931135564], "content": "F\u00fbt 27 - Ch\u00eane am\u00e9ricain, 8 ans", "level": 1}, {"type": "text", "bbox": [162.83072021058285, 217.61996981633882, 415.7827192710465, 229.94519365115642], "content": "Nez : caramel, banane m\u00fbre, bois doux.", "level": 2}, {"type": "text", "bbox": [162.83072021058285, 234.4199853539393, 589.7467186248941, 246.7452091887569], "content": "Bouche : moelleux en entr\u00e9e, caramel beurr\u00e9, finale sur la cannelle.", "level": 2}, {"type": "text", "bbox": [162.83072021058285, 268.0200239291405, 180.80672014381483, 280.34524776395807], "content": "---", "level": 1}, {"type": "section_header", "bbox": [162.83072021058285, 284.820039466741, 380.4887194021386, 297.14526330155854], "content": "Maintenant g\u00e9n\u00e8re la note pour :", "level": 1}, {"type": "text", "bbox": [162.83072021058285, 301.62006250434166, 546.6827187848464, 313.9452863391593], "content": "F\u00fbt 61 - Ch\u00eane du Tron\u00e7ais, 18 ans, Grande Champagne.", "level": 1}]}, {"page_number": 45, "width": 720.0, "height": 405.0, "elements": [{"type": "picture", "bbox": [29.714176177978516, 42.799102783203125, 684.077880859375, 306.8143615722656], "content": "", "level": 1}, {"type": "section_header", "bbox": [33.000001056, 343.49401099180795, 299.8140095940481, 366.6864295237228], "content": "L'architecture RAG", "level": 1}]}, {"page_number": 46, "width": 720.0, "height": 405.0, "elements": [{"type": "section_header", "bbox": [192.8503936712124, 194.99401373980822, 430.01840126058846, 218.18643227172302], "content": "Savoir se situer", "level": 1}]}, {"page_number": 47, "width": 720.0, "height": 405.0, "elements": [{"type": "text", "bbox": [23.33333345555555, 37.26829258310107, 52.00000012222222, 43.25783962142856], "content": "enterprises", "level": 1}, {"type": "picture", "bbox": [30.57718276977539, 17.780881881713867, 684.5956420898438, 299.0409240722656], "content": "", "level": 1}, {"type": "text", "bbox": [33.000001056, 343.49401099180795, 284.9910091197121, 366.6864295237228], "content": "O\u00f9 en \u00eates vous ?", "level": 1}, {"type": "text", "bbox": [302.1771846696696, 343.0980087291359, 677.6931966861821, 361.62881525204085], "content": "Il faut avoir conscience de son propre niveau de maturit\u00e9 et de celui de sa structure", "level": 1}]}, {"page_number": 48, "width": 720.0, "height": 405.0, "elements": [{"type": "picture", "bbox": [33.543052673339844, 43.99225997924805, 85.27201843261719, 77.08345031738281], "content": "", "level": 1}, {"type": "section_header", "bbox": [33.000001056, 45.45600145459201, 85.704002742528, 86.68696773355163], "content": "04", "level": 1}, {"type": "section_header", "bbox": [263.80869619187797, 67.88060318867923, 465.0426550948645, 82.19221367227948], "content": "LES NIVEAUX D'ADOPTION", "level": 1}, {"type": "table", "bbox": [196.71485900878906, 84.02304077148438, 572.3555297851562, 307.08038330078125], "content": "| Championnes   | V\u00e9ritable appr\u00e9hension et int\u00e9gration de la technologie dans les processus   |\n|---------------|------------------------------------------------------------------------------|\n| Exploratrices | Elles utilisent l'IA dans des condition plus complexes                       |\n| Optimiseurs   | Elles utilisent l'IA pour faire mieux, pas encore sur des cas complexes      |\n| Novices       | Elles valident les premiers niveau de compr\u00e9hension et d'utilisation         |", "level": 1}, {"type": "text", "bbox": [33.000001056, 343.49401099180795, 284.9910091197121, 366.6864295237228], "content": "O\u00f9 en \u00eates vous ?", "level": 1}, {"type": "text", "bbox": [302.1771846696696, 343.0980087291359, 677.6931966861821, 361.62881525204085], "content": "Il faut avoir conscience de son propre niveau de maturit\u00e9 et de celui de sa structure", "level": 1}]}, {"page_number": 49, "width": 720.0, "height": 405.0, "elements": [{"type": "picture", "bbox": [33.56199264526367, 44.03595733642578, 85.24000549316406, 77.0110092163086], "content": "", "level": 1}, {"type": "section_header", "bbox": [83.44422117021499, 108.10199064026358, 291.92698635116335, 118.62447725552897], "content": "P\u00c9RIM\u00c8TRE D'APPLICATION DE l'IA", "level": 1}, {"type": "section_header", "bbox": [386.88188738021995, 107.79883313056251, 561.7384001126283, 118.3213197458279], "content": "COMPLEXIT\u00c9 D'USAGE DE L'IA", "level": 1}, {"type": "table", "bbox": [28.646465301513672, 125.4211654663086, 634.3558959960938, 300.90618896484375], "content": "| Toute l'organisation   | L'ensemble de l'entreprise utilise des solutions transverses   | Frontier    | Toute l'organisation                                       |\n|------------------------|----------------------------------------------------------------|-------------|------------------------------------------------------------|\n| Transverse             | Il y a des ponts entre les services                            | Sur mesure  | Transverse                                                 |\n| Par service            | Certains service ont automatis\u00e9 des processus                  | Cl\u00e9 en main | Des produits complets qui sont install\u00e9s mis \u00e0 disposition |\n| Ponctuel               | Certains \u00e9l\u00e9ments utilisent l'IA                               | Embarqu\u00e9e   | Utilisation de l'IA dans un outil existant                 |\n| Aucun                  | Aucun                                                          | Aucun       | Aucun                                                      |", "level": 1}, {"type": "text", "bbox": [302.1771846696696, 343.0980087291359, 677.6931966861821, 361.62881525204085], "content": "Il faut avoir conscience de son propre niveau de maturit\u00e9 et de celui de sa structure", "level": 1}]}, {"page_number": 50, "width": 720.0, "height": 405.0, "elements": [{"type": "section_header", "bbox": [33.000001056, 45.45600145459201, 85.704002742528, 86.68696773355163], "content": "04", "level": 1}, {"type": "picture", "bbox": [206.85545349121094, 31.194061279296875, 612.8921508789062, 302.4150390625], "content": "", "level": 1}, {"type": "section_header", "bbox": [33.000001056, 343.49401099180795, 225.69900722236807, 366.6864295237228], "content": "Formez vous !", "level": 1}, {"type": "text", "bbox": [245.92718286966956, 343.0980087291359, 670.8531964673018, 361.62881525204085], "content": "Pour progresser il faut des cl\u00e9s de lecture qui permettront une mise en pratique avec un ROI \u00e0 la cl\u00e9", "level": 1}]}, {"page_number": 51, "width": 720.0, "height": 405.0, "elements": [{"type": "picture", "bbox": [32.47541427612305, 31.306257247924805, 111.26930236816406, 78.71282196044922], "content": "", "level": 1}, {"type": "section_header", "bbox": [192.8503936712124, 194.99401373980822, 415.19540078625243, 218.18643227172302], "content": "Section Bonus !", "level": 1}]}, {"page_number": 52, "width": 720.0, "height": 405.0, "elements": [{"type": "picture", "bbox": [33.70164108276367, 41.79378128051758, 83.21745300292969, 77.13180541992188], "content": "", "level": 1}, {"type": "text", "bbox": [33.000001056, 45.45600145459201, 85.704002742528, 86.68696773355163], "content": "0*", "level": 1}, {"type": "text", "bbox": [38.61189846635976, 98.66666672500003, 75.89235172415012, 103.33333339166666], "content": "Nicholas G. Otis", "level": 1}, {"type": "text", "bbox": [38.61189754266998, 104.6666666916667, 76.55807318006374, 109.33333335833333], "content": "Sol\u00e8ne Delecourt", "level": 1}, {"type": "text", "bbox": [38.60599552949071, 110.62966030083766, 75.8982525196587, 116.70367305718531], "content": "Katelyn Cranney", "level": 1}, {"type": "text", "bbox": [38.47273700240951, 116.09509161778755, 80.04262800537792, 122.7592990762837], "content": "Rembrand Koning", "level": 1}, {"type": "text", "bbox": [39.943342959277615, 231.999999975, 54.5892353105524, 235.33333330833332], "content": "00 00 00", "level": 1}, {"type": "text", "bbox": [65.2239144109347, 229.92866226350245, 95.8809018362228, 237.4046709966161], "content": "Harvard", "level": 1}, {"type": "text", "bbox": [65.22084748585199, 237.90252021447472, 100.54402453172277, 245.43081318831096], "content": "Business", "level": 1}, {"type": "text", "bbox": [65.2407935589589, 245.99999995833332, 93.8668558819051, 253.33333329166666], "content": "School", "level": 1}, {"type": "section_header", "bbox": [33.000001056, 343.49401099180795, 284.9910091197121, 366.6864295237228], "content": "L'IA au travail ?", "level": 1}, {"type": "text", "bbox": [268.505821092186, 55.83439003670043, 334.385823200346, 81.60374396105021], "content": "25 %", "level": 1}, {"type": "section_header", "bbox": [194.22727671527264, 121.43839025802833, 412.48329051996495, 156.1903948560925], "content": "moins de femmes utilisent l'IA g\u00e9n\u00e9rative au travail", "level": 1}, {"type": "text", "bbox": [155.52095372667034, 197.8724185819172, 451.9809632133906, 227.20321795042187], "content": "'Otis, N.G., Delecourt, S., Cranney, K. & Koning, R. (2024). \"Global Evidence on Gender Gaps and Generative AI.\" Harvard Business School Working'", "level": 1}, {"type": "picture", "bbox": [471.42083740234375, 187.25830078125, 672.7784423828125, 306.07275390625], "content": "", "level": 1}, {"type": "text", "bbox": [311.25000996, 343.0980087291359, 671.9430215021764, 361.62881525204085], "content": "Une adoption moindre chez les femmes qui serait d\u00fb essentiellement \u00e0 des probl\u00e9matiques de confiance, perception du risques etc..", "level": 1}]}, {"page_number": 53, "width": 720.0, "height": 405.0, "elements": [{"type": "text", "bbox": [23.297214886996887, 17.333333306666646, 149.767803122291, 21.333333306666646], "content": "We have a new name - Stiftung Neue Verantwortung (SNV) is now interface.", "level": 1}, {"type": "picture", "bbox": [33.68130111694336, 41.706634521484375, 83.3157958984375, 77.18802642822266], "content": "", "level": 1}, {"type": "text", "bbox": [33.000001056, 45.45600145459201, 85.704002742528, 86.68696773355163], "content": "0*", "level": 1}, {"type": "text", "bbox": [268.505821092186, 55.83439003670043, 334.385823200346, 81.60374396105021], "content": "22 %", "level": 1}, {"type": "text", "bbox": [24.628482938854486, 141.9999999948718, 41.9349844868421, 144.66666666153844], "content": "DATA-BRIEF", "level": 1}, {"type": "text", "bbox": [23.96284712680601, 150.66666654999997, 192.368419882224, 164.66666655], "content": "Al's Missing Link: The Gender", "level": 1}, {"type": "section_header", "bbox": [190.6461226006757, 121.43839025802833, 416.070136858744, 156.1903948560925], "content": "de femmes dans les m\u00e9tiers qui con\u00e7oivent l'IA", "level": 1}, {"type": "text", "bbox": [25.29411696478329, 166.66666677222224, 147.1052624756192, 179.3333334388889], "content": "Gap in the Talent Pool", "level": 1}, {"type": "text", "bbox": [24.628484103715135, 238.66666662222224, 92.52322094582037, 242.66666662222224], "content": "Ruggero Marino Lazzaroni, Siddhi Pal", "level": 1}, {"type": "text", "bbox": [24.628483313273986, 244.66666664166667, 51.919504985100595, 247.999999975], "content": "October 01, 2024", "level": 1}, {"type": "text", "bbox": [23.962847243292085, 260.6666666777778, 87.19814136093913, 264.0000000111111], "content": "Tech analysis and policy ideas for Europe", "level": 1}, {"type": "text", "bbox": [160.4691958850141, 178.67241046751695, 447.04720505551035, 208.00320983602165], "content": "'Interface EU - \"AI's Missing Link: The Gender Gap in the Talent Pool\", 2024 interface-eu.org - 1,6M profils analys\u00e9s'", "level": 1}, {"type": "text", "bbox": [157.06483351934008, 259.2193357715999, 193.0590062208657, 267.44733069042314], "content": "interface I", "level": 1}, {"type": "section_header", "bbox": [33.000001056, 343.49401099180795, 284.9910091197121, 366.6864295237228], "content": "L'IA au travail ?", "level": 1}, {"type": "picture", "bbox": [481.0766296386719, 40.41532516479492, 671.4205322265625, 310.68310546875], "content": "", "level": 1}, {"type": "text", "bbox": [311.25000996, 343.0980087291359, 671.9430215021764, 361.62881525204085], "content": "Une adoption moindre chez les femmes qui serait d\u00fb essentiellement \u00e0 des probl\u00e9matiques de confiance, perception du risques etc..", "level": 1}]}, {"page_number": 54, "width": 720.0, "height": 405.0, "elements": [{"type": "picture", "bbox": [48.13143539428711, 35.20896911621094, 80.10084533691406, 63.65740203857422], "content": "", "level": 1}, {"type": "text", "bbox": [45.00000144, 36.300001161599994, 83.17500266159999, 109.2000034944], "content": "\"", "level": 1}, {"type": "text", "bbox": [60.00000192, 142.03604704515334, 647.4389007180442, 257.2483968857946], "content": "L'IA n'a pas de nez, pas de m\u00e9moire, pas de passion. Elle a du temps . \u00c0 vous de savoir le lui confier.", "level": 1}, {"type": "text", "bbox": [602.2149192708769, 360.4002861578088, 608.7129194788129, 368.6915364231288], "content": "K", "level": 1}]}, {"page_number": 55, "width": 720.0, "height": 405.0, "elements": [{"type": "picture", "bbox": [40.95355987548828, 40.840145111083984, 150.5867919921875, 72.26514434814453], "content": "", "level": 1}, {"type": "section_header", "bbox": [42.000001344, 183.63338087626798, 272.58000872256, 219.71047637035764], "content": "questions?", "level": 1}, {"type": "text", "bbox": [42.000001344, 320.7215945130907, 178.404005708928, 333.15846991107065], "content": "Pier-Jean Malandrino", "level": 1}, {"type": "picture", "bbox": [48.22868347167969, 339.5574645996094, 122.52705383300781, 371.239013671875], "content": "", "level": 1}, {"type": "text", "bbox": [396.00001267199997, 123.06666318813313, 450.351014411232, 130.15323551732928], "content": "ME CONTACTER", "level": 1}, {"type": "list", "bbox": [396.00001267199997, 158.62958457614653, 485.8503155472096, 179.34383559884083], "content": "01 Linkedin :", "level": 2}, {"type": "text", "bbox": [429.6790637497296, 174.14333282258647, 677.2795716729456, 184.521890847008], "content": "https://www.linkedin.com/in/pier-jean-malandrino/", "level": 1}, {"type": "list", "bbox": [396.00001267199997, 207.75717964822954, 614.243427155789, 223.2187920028394], "content": "02 Mail : pierjean.malandrino@scub.net", "level": 2}, {"type": "list", "bbox": [396.00001267199997, 251.63222605223095, 530.9465344902885, 267.0938384068408], "content": "03 T\u00e9l : 06 58 27 88 49", "level": 2}]}]


