MERIT is a three‑stage framework that enables language models to reason over table images by first adapting a table‑specific encoder through reconstruction, localization, and color‑conditioned supervision. It then fine‑tunes the model with supervised evidence selection and reasoning, and finally refines the policy using reinforcement learning with distinct rewards for evidence correctness, calculation, final answers, and output format. The approach is evaluated on table question answering and fact verification, demonstrating the benefits of visual inputs and training components across diverse table structures and visual presentations.
MERIT (Multimodal Evidence Reasoning over Image Tables) is a three‑stage framework designed to enable language models to reason over table images, which preserve visual cues and structural details that serialized textual tables often lose. The approach first adapts a table‑specific encoder through reconstruction, localization, and color‑conditioned supervision to address visual evidence acquisition. It then fine‑tunes the model with supervised evidence selection and reasoning, followed by reinforcement learning that rewards evidence correctness, calculation, final answers, and output format, thereby improving retrieval and computational accuracy in table question answering and fact verification tasks.
By Haoyang Jiang, Zhuotong Fan, Mingyang Yi, Ju Fan, Lanqing Hu, Xiao Zan
LADBench is a new benchmark comprising over 1,000 synthetic images that contain logical anomalies across four domains—Residential, Urban, Collaborative, and Nature. It introduces a Tiered Prompting Protocol that progressively discloses information to test how much explicit help a model needs to locate and reason about these faults. Evaluation of leading foundation models shows that even the best only reaches 70.11% accuracy, indicating that implicit logical fault detection remains largely unsolved.
By Sahasra Kondapalli, Lara Radovanovic, Aadi Palnitkar, Mingyang Mao, Xiaomin Lin
The paper introduces Self-RefineVQA, a training‑free, inference‑time framework that lets a single Vision‑Language Model iteratively generate, critique, and refine its own answers for table visual question answering. By forming a closed self‑feedback loop guided by prompts, the method corrects common reasoning errors such as spatial misalignment and logical hallucinations without requiring additional labeled data or model updates. Experiments on tabular VQA benchmarks show consistent accuracy gains across monolingual and cross‑lingual settings, especially for lightweight models, while keeping inference cost linear and eliminating the need for expensive supervised fine‑tuning.
By Somraj Gautam, Gaurav Harit
The paper introduces the Very Big Video Reasoning (VBVR) Dataset, a large-scale collection of over one million video clips organized into 200 curated reasoning tasks. It also presents VBVR-Bench, a benchmark framework that uses rule-based, human-aligned scorers for reproducible evaluation of video reasoning models. The authors conduct a large-scale scaling study, noting early signs of emergent generalization to unseen reasoning tasks, and make all resources publicly available.
By Maijunxian Wang, Ruisi Wang, Juyi Lin, Ran Ji, Thadd\"aus Wiedemer, Qingying Gao, Dezhi Luo, Yaoyao Qian, Lianyu Huang, Zelong Hong, Jiahui Ge, Qianli Ma, Hang He, Yifan Zhou, Lingzi Guo, Lantao Mei, Jiachen Li, Hanwen Xing, Tianqi Zhao, Fengyuan Yu, Weihang Xiao, Yizheng Jiao, Jianheng Hou, Danyang Zhang, Pengcheng Xu, Boyang Zhong, Zehong Zhao, Gaoyun Fang, John Kitaoka, Yile Xu, Hua Xu, Kenton Blacutt, Tin Nguyen, Siyuan Song, Haoran Sun, Shaoyue Wen, Linyang He, Runming Wang, Yanzhi Wang, Mengyue Yang, Ziqiao Ma, Rapha\"el Milli\`ere, Freda Shi, Nuno Vasconcelos, Daniel Khashabi, Alan Yuille, Yilun Du, Ziming Liu, Bo Li, Dahua Lin, Ziwei Liu, Vikash Kumar, Yijiang Li, Lei Yang, Zhongang Cai, Hokin Deng
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...
By Jahanvi Rajput, Dhruv Kudale, Saikiran Kasturi, Utkarsh Verma, Ganesh Ramakrishnan
arXiv:2607. 09068v1 Announce Type: cross Abstract: Recent advancements in LVLMs necessitate robust benchmarks for complex, visually grounded reasoning.
By Yang Chen, Yunwen Li, Yufan Shen, Minghao Liu, Tianyu Zheng, Bin Fu, Qunshu Lin, Zhi Yu, Botian Shi
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:2610.27341v1 Announce Type: new
Abstract: Vision-language models (VLMs) have advanced rapidly in multimodal reasoning, yet they remain limited on tasks requiring fine-grained understanding of s...
By Zerui Chen, Changrui Chen, Fei Ni, Jiankang Deng
arXiv:2506. 18421v3 Announce Type: replace-cross Abstract: The majority of data in businesses and industries is stored in tables, databases, and data warehouses.
By Ce Li, Xiaofan Liu, Zhiyan Song, Ce Chi, Boshen Shi, Chen Zhao, Guanguang Chang, Zhendong Wang, Kexin Yang, Xing Wang, Chao Deng, Junlan Feng
arXiv:2606. 26794v1 Announce Type: cross Abstract: CLIP and its variants are widely adopted visual backbones in multimodal systems, but their pretraining remains dominated by descriptive image-text alignment.
By Sicheng Zhang, Muzammal Naseer, Binzhu Xie, Naufal Suryanto, Shi Qiu, Jamal Bentahar, Naveed Akhtar, Mubarak Shah
DRScaffold introduces a supervised fine‑tuning framework that decomposes dense‑scene reasoning into four causally ordered stages, enabling lightweight vision‑language models to ground each reasoning step in visual entities and relations. The authors first present DRBench, a benchmark of 14,573 questions over 2,943 images covering five task categories and three reasoning layers. Experiments show that applying DRScaffold to three lightweight VLMs yields significant gains on DRBench while maintaining or improving general‑purpose benchmark performance, with Qwen2.5‑VL‑3B surpassing the frozen Qwen2.5‑VL‑32B under zero‑shot prompting.
By Xinrui Shi, Kai Liu, Ziqing Zhang, Jianze Li, Anqi Li, Yulun Zhang