InterTab：用于多模态表格推理的交错视觉-结构对齐

Computer Science > Computer Vision and Pattern Recognition arXiv:2609.32660 (cs) [Submitted on 26 Sep 2026] Title:InterTab: Interleaved Visual-Structure Alignment for Multi-Modal Table Reasoning Authors:Hanqian Li, Sirui Huang, Chen Ling, Jungang Li, Yu Huang, Kening Zheng, Yonghua Hei, Xiangrong He, Shiyi Wang, Pengcheng Zhu, Dongnan Liu, Wei Zhou, Linjian Mo, Nai Ding, Xuming Hu View a PDF of the paper titled InterTab: Interleaved Visual-Structure Alignment for Multi-Modal Table Reasoning, by Hanqian Li and 14 other authors View PDF HTML (experimental) Abstract:Table images preserve structural information that are often lost in text serialization, and reasoning over them requires locating relevant rows, columns, and cells step by step. Current multimodal large language models (MLLMs) encode the whole image once before reasoning, so they cannot pick up row-, column-, and cell-level evidence as the question unfolds. Encoder-side table structure and generic interleaved visual chain-of-thought still do not bind each reasoning step to that structure. We propose \textbf{InterTab}, an \textbf{Inter}leaved structure-aware framework for CoT reasoning over \textbf{Tab}le images, interleaves chain-of-thought with tool calls that crop structure-aligned table regions. First, we build InterTab-22K, includes reasoning trajectories in which each step is tied to both a structural location and a bounding box. InterTab is trained in two stages: supervised structure-aware alignment (SSA) on InterTab-22K teaches the model to interleave reasoning with structure-aligned crops, and active localization optimization (ALO) further optimizes answer correctness, localization IoU, and output format, while penalizing missing or excessive tool calls. Experiments on nine table benchmarks show that InterTab improves the average accuracy of its backbone from 68.28% to 73.17% and achieves the best average performance among all compared methods. Code and data will be released soon. Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) Cite as: arXiv:2609.32660 [cs.CV] (or arXiv:2609.32660v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2609.32660 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Hanqian Li [view email] [v1] Sat, 26 Sep 2026 14:18:58 UTC (1,847 KB) Full-text links: Access Paper: View a PDF of the paper titled InterTab: Interleaved Visual-Structure Alignment for Multi-Modal Table Reasoning, by Hanqian Li and 14 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV new | recent | 2026-09 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)