arXiv:2606. 09578v1 Announce Type: new Abstract: Large Language Models (LLMs) and Vision-Language Models (VLMs) are increasingly evaluated on table reasoning tasks, but the role of table representation remains under-explored.
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Table understanding is a core task in document intelligence, encompassing two key subtasks: table reconstruction and table visual question answering (TabVQA). While recent approaches predominantly rel...
arXiv:2609.17458v1 Announce Type: cross Abstract: Table understanding is a core task in document intelligence, encompassing two key subtasks: table reconstruction and table visual question answering...
The paper investigates pixel-level table compression for document question answering, comparing five vision‑language models across two benchmarks and varying visual‑token budgets. It finds that representing tables as native‑resolution images matches text in performance and efficiency, while highly downscaled images still allow the model to identify relevant tables but lose readability, leading to longer reasoning traces. A two‑step, training‑free method first selects relevant tables from compressed images and then reasons over them at native resolution, saving 41% of tokens and improving accuracy by 7 points over single‑step native‑resolution QA, while using 15% fewer tokens than the most efficient single‑step compressed setup without accuracy loss.
arXiv:2601. 04498v2 Announce Type: replace Abstract: Infographics are composite visual artifacts that combine data visualizations with textual and illustrative elements to communicate information.
arXiv:2609.13158v1 Announce Type: new Abstract: Large Vision--Language Models (LVLMs) are increasingly expected to perform visual question answering (VQA) over planar media. However, existing planar...
The paper introduces TEmBed, a unified benchmark for evaluating tabular embeddings across four representation levels—cell, row, column, and table—using a diverse set of models. It demonstrates that the best model depends on the specific task and representation level, providing practical guidance for selecting embeddings in real-world applications. The study aims to facilitate the development of more general-purpose tabular representation models.