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arxiv: 2604.13041 · v1 · submitted 2026-02-27 · 💻 cs.DB · cs.AI

TableNet A Large-Scale Table Dataset with LLM-Powered Autonomous

Ruilin Zhang , Kai Yang This is my paper

Pith reviewed 2026-05-15 19:06 UTC · model grok-4.3

classification 💻 cs.DB cs.AI
keywords table structure recognitionLLM multi-agent systemactive learningdataset generationtable datasetcontrollable parameters
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The pith

An LLM-powered autonomous system generates large-scale table datasets and uses active learning to recognize table structures with fewer training samples.

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

The paper introduces TableNet, a large-scale dataset for table structure recognition created using an LLM-powered multi-agent system. This system generates table images by integrating controllable visual, structural, and semantic parameters, enabling theoretically infinite production of annotated tables from multiple sources. The recognition component applies a diversity-based active learning paradigm to selectively sample informative data for finetuning, resulting in competitive performance on the TableNet test set with significantly reduced training samples compared to baselines and superior results on real-world web-crawled tables.

Core claim

The central claim is that the first LLM-powered autonomous table generation and recognition multi-agent system can produce a wide array of semantically coherent tables with annotations and support efficient model finetuning through diversity-based active learning, achieving competitive performance on the new dataset while reducing the number of training samples by a large margin and improving generalization to real-world tables.

What carries the argument

The LLM-powered multi-agent system that synthesizes table images using controllable parameters for generation and applies diversity-based active learning to select training data for recognition.

If this is right

  • Supports theoretically infinite and domain-agnostic generation of table images with annotations.
  • Achieves competitive performance on TableNet with much fewer training samples than traditional methods.
  • Delivers higher performance on web-crawled real-world tables compared to models trained on existing predominant datasets.
  • Enables a comprehensive annotation taxonomy for advancing table-related research.

Where Pith is reading between the lines

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

  • Similar active learning approaches could be adapted for other complex document structure tasks beyond tables.
  • The controllable parameters might allow customization for specific domains like financial or scientific tables.
  • Reducing training data needs could make table recognition more accessible for resource-constrained applications.

Load-bearing premise

LLM-generated tables with controllable parameters accurately capture the diversity, complexity, and distribution of real-world tables including merged cells and varied layouts.

What would settle it

Test the active learning model on a fresh collection of diverse web-crawled tables and check if its performance advantage over baseline models trained on standard datasets disappears or reverses.

Figures

Figures reproduced from arXiv: 2604.13041 by Kai Yang, Ruilin Zhang.

Figure 1
Figure 1. Figure 1: Workflow of our multi-agent system. checks row equality; (4) a fallback HTML constructor for regenerating compliant tables; and (5) a Selenium tool for rendering table images and producing annotations. Memory The system incorporates a two-level memory mechanism. Outer memory retains the multi-turn dialog his￾tory between the core LLM and the user, ensuring continuity and refinement. Inner memory tracks pre… view at source ↗
Figure 2
Figure 2. Figure 2: Data collection pipeline. From top to bottom: agent generating, web crawling and open source augmenting. Anno￾tation explanation: is simple denotes whether the table contains cells spanning. is colored denotes whether the table includes any background color, colored borders, or non-black font colors. is lined specifies whether the cell borders are fully present; tables with only horizontal or vertical line… view at source ↗
Figure 3
Figure 3. Figure 3: Invalid tables span-free tables based on orientation, multi-level headers, and body-level spans, all supported by our system. Semanti￾cally, we capture scenario-dependent complexity (e.g., tech comparisons vs. financial reports), and our filling checker improves authenticity by detecting header–body mismatches and hallucinations. Visually, we regulate border thickness, line style, font and background color… view at source ↗
Figure 4
Figure 4. Figure 4: examples in TableNet [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Generated data composition [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Compared with existing CoSyn pipeline, which is [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: TSR Experiment results samples of the same performance. For example, model fine￾tuned on 10k actively selected samples achieved a TEDS performance around 0.973 while other baselines used more than 20k, even 40k training samples. Limitations Although our system supports configurable parameters, it is still bounded by the pretraining distribution and reasoning capabilities of the underlying LLM. As a result,… view at source ↗
Figure 8
Figure 8. Figure 8: Detailed TableNet composition Document Crawling Keywords We crawled Microsoft search engine using Selenium by querying in format: company abbreviation + telecom-related keyword + filetype:pdf/doc Keywords are shown in [PITH_FULL_IMAGE:figures/full_fig_p011_8.png] view at source ↗
read the original abstract

Table Structure Recognition (TSR) requires the logical reasoning ability of large language models (LLMs) to handle complex table layouts, but current datasets are limited in scale and quality, hindering effective use of this reasoning capacity. We thus present TableNet dataset, a new table structure recognition dataset collected and generated through multiple sources. Central to our approach is the first LLM-powered autonomous table generation and recognition multi-agent system that we developed. The generation part of our system integrates controllable visual, structural, and semantic parameters into the synthesis of table images. It facilitates the creation of a wide array of semantically coherent tables, adaptable to user-defined configurations along with annotations, thereby supporting large-scale and detailed dataset construction. This capability enables a comprehensive and nuanced table image annotation taxonomy, potentially advancing research in table-related domains. In contrast to traditional data collection methods, This approach facilitates the theoretically infinite, domain-agnostic, and style-flexible generation of table images, ensuring both efficiency and precision. The recognition part of our system is a diversity-based active learning paradigm that utilizes tables from multiple sources and selectively samples most informative data to finetune a model, achieving a competitive performance on TableNet test set while reducing training samples by a large margin compared with baselines, and a much higher performance on web-crawled real-world tables compared with models trained on predominant table datasets. To the best of our knowledge, this is the first work which employs active learning into the structure recognition of tables which is diverse in numbers of rows or columns, merged cells, cell contents, etc, which fits better for diversity-based active learning.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 2 minor

Summary. The paper introduces TableNet, a large-scale dataset for table structure recognition generated and annotated via a novel LLM-powered autonomous multi-agent system. The generation component synthesizes table images using controllable visual, structural, and semantic parameters; the recognition component applies diversity-based active learning to sample informative tables from multiple sources for model finetuning. The central claims are that this yields competitive performance on the TableNet test set while using far fewer training samples than baselines, plus substantially higher performance on web-crawled real-world tables than models trained on existing predominant datasets.

Significance. If the performance claims are substantiated with rigorous metrics, the work could enable scalable, annotation-light dataset construction for TSR and demonstrate the first application of active learning to tables with high structural and semantic diversity. This would reduce dependence on manually curated corpora and improve generalization to real-world layouts, with potential downstream benefits for document understanding pipelines.

major comments (3)
  1. [Abstract] Abstract: The claims of 'competitive performance on TableNet test set while reducing training samples by a large margin' and 'much higher performance on web-crawled real-world tables' are presented without any numerical results, baseline names, evaluation metrics (e.g., F1, IoU), error bars, or protocol details, rendering the central empirical assertions unverifiable from the manuscript.
  2. [Recognition part of the system] Recognition / Active Learning section: No ablation or distributional analysis (e.g., histograms of row/column counts, merge-cell frequency, cell-value entropy) is provided to confirm that LLM-generated tables with controllable parameters match the joint distribution of the web-crawled evaluation set; without this, the reported generalization lift cannot be attributed to the method rather than domain mismatch or leakage.
  3. [Recognition part of the system] Evaluation: The manuscript supplies no details on the active-learning selection criterion (specific diversity metric), the finetuning protocol, or the composition of the 'multiple sources' used for sampling, all of which are load-bearing for the claim that diversity-based active learning is particularly suited to tables varying in rows, columns, and merged cells.
minor comments (2)
  1. [Abstract] Abstract: Capitalization error in 'In contrast to traditional data collection methods, This approach' (should be lowercase 'this').
  2. [Abstract] Abstract: The final sentence is grammatically awkward and repetitive ('which is diverse in numbers of rows or columns, merged cells, cell contents, etc, which fits better for diversity-based active learning'); rephrase for clarity.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive comments, which highlight opportunities to strengthen the verifiability and transparency of our empirical claims. We address each point below and will incorporate the requested details and analyses into the revised manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The claims of 'competitive performance on TableNet test set while reducing training samples by a large margin' and 'much higher performance on web-crawled real-world tables' are presented without any numerical results, baseline names, evaluation metrics (e.g., F1, IoU), error bars, or protocol details, rendering the central empirical assertions unverifiable from the manuscript.

    Authors: We agree that the abstract should be self-contained with specific numbers. In the revision we will insert concrete metrics (e.g., F1 and IoU scores on the TableNet test set, the exact percentage reduction in training samples relative to baselines such as TableFormer and PubTabNet, and the absolute performance gains on the web-crawled set), along with a brief statement of the evaluation protocol and error-bar information where available. revision: yes

  2. Referee: [Recognition part of the system] Recognition / Active Learning section: No ablation or distributional analysis (e.g., histograms of row/column counts, merge-cell frequency, cell-value entropy) is provided to confirm that LLM-generated tables with controllable parameters match the joint distribution of the web-crawled evaluation set; without this, the reported generalization lift cannot be attributed to the method rather than domain mismatch or leakage.

    Authors: We acknowledge the need for explicit distributional evidence. We will add a new subsection containing histograms and summary statistics (row/column counts, merge-cell frequency, cell-value entropy) comparing the LLM-generated tables against the web-crawled evaluation set. These plots will be accompanied by a short discussion confirming alignment of the joint distributions, thereby supporting attribution of the observed generalization gains to the active-learning procedure rather than domain shift. revision: yes

  3. Referee: [Recognition part of the system] Evaluation: The manuscript supplies no details on the active-learning selection criterion (specific diversity metric), the finetuning protocol, or the composition of the 'multiple sources' used for sampling, all of which are load-bearing for the claim that diversity-based active learning is particularly suited to tables varying in rows, columns, and merged cells.

    Authors: We agree these implementation details are essential for reproducibility and for justifying the suitability of diversity-based active learning. In the revised manuscript we will (1) state the precise diversity metric (including its mathematical formulation over structural and semantic feature vectors), (2) provide the complete finetuning protocol (hyperparameters, optimizer, number of epochs, early-stopping criteria), and (3) report the exact composition of the sampled sources (e.g., proportions drawn from each generation configuration and external corpora). These additions will directly address why the diversity criterion is well-matched to tables that vary in row/column count and merge-cell structure. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical claims rest on independent evaluation of new data and active learning

full rationale

The paper describes an LLM-powered table generation system with controllable parameters and a diversity-based active learning pipeline for table structure recognition. All performance numbers (competitive results on TableNet test set with fewer samples, higher accuracy on web-crawled tables) are presented as experimental outcomes from training on the synthesized data and evaluating on held-out and external real-world tables. No equations, fitted parameters renamed as predictions, self-definitional loops, or load-bearing self-citations appear in the derivation; the active-learning selection criterion is described procedurally rather than as a tautology that forces the reported metrics. The central claims therefore remain self-contained empirical statements.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 1 invented entities

The central claims depend on the assumption that LLMs can reliably produce accurate table annotations and that diversity sampling selects truly informative examples for generalization.

free parameters (1)
  • controllable visual, structural, and semantic parameters
    User-defined settings for synthesizing table images; values chosen to control diversity but not specified numerically.
axioms (1)
  • domain assumption LLMs have the logical reasoning ability to handle complex table layouts for both generation and recognition
    Stated directly in the abstract as the motivation for using LLMs in TSR.
invented entities (1)
  • LLM-powered autonomous table generation and recognition multi-agent system no independent evidence
    purpose: To synthesize large-scale annotated table images and apply active learning for model fine-tuning
    New system introduced as the core technical contribution; no independent evidence provided beyond the paper's description.

pith-pipeline@v0.9.0 · 5583 in / 1385 out tokens · 46353 ms · 2026-05-15T19:06:25.856739+00:00 · methodology

discussion (0)

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Reference graph

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