arXiv AI By Zhuoran Yu, Le Thien Phuc Nguyen, Jaden Park, Xinyi Gu, Zexue He, Soochahn Lee, Rogerio Feris, Yong Jae Lee

DocHop: Benchmarking Out-of-domain Multi-hop Reasoning in Information-Dense Documents

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DocHop is a new benchmark that tests multimodal large language models on integrated chart‑context reasoning within document‑style images. The benchmark presents narrative text that imposes multi‑step compositional constraints, while charts supply the data needed to answer questions grounded in semantic reference labels. It contains 2,074 examples across six task categories, generated via a stochastic logic‑first pipeline that controls reasoning depth and visual density, and shows a large performance gap between humans (over 90% accuracy) and the best models (62.83%).

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arXiv AI
Aug 5

DocTrace: Towards Traceable Long Document VQA via Hierarchical Evidence Graph Reasoning

arXiv:2608. 03292v1 Announce Type: new Abstract: Long Document Visual Question Answering (LongDocVQA) requires Multimodal Large Language Models (MLLMs) to locate, integrate, and reason over heterogeneous document elements distributed across multiple pages.

By Le Xiang, Zhicheng Guan, Hong Chen, Xiaocong Lin, Zhenghua Lei, Teng Hu, Bolei He, Long Zeng
arXiv Computation and Language
3d ago

CoVA-SFT: A Large-Scale Dataset for Chain of Visual Abstractions

CoVA‑SFT is a new large‑scale dataset comprising 51.9K samples and over 222K multimodal reasoning steps that teach models to interleave text and visual abstractions across five layout families and 17 complex tasks. It includes explicit rationale formulations, agentic renderings, and verification loops to help models build and maintain internal visual workspaces for purely textual reasoning problems. A companion benchmark, CoVA‑Bench, contains 1,700 held‑out test samples for reproducible evaluation, and models fine‑tuned on CoVA‑SFT outperform all interleaved CoT baselines by more than 2× on average, though they still lag behind strong text‑only CoT baselines.

By Tsung-Han Wu, Heekyung Lee, Anya Ji, Haoming Chen, Trevor Darrell, Joseph E. Gonzalez, David M. Chan
arXiv Computer Vision
4d ago

Doc-CoB: Enhancing Document Understanding with Visual Chain-of-Boxes Reasoning

Doc‑CoB introduces a Chain‑of‑Boxes framework that enhances document understanding by progressively focusing on query‑relevant layout regions while preserving global context. It selects key layout boxes and then applies visual prompting for deeper analysis, supported by two new reasoning tasks and an automatic pipeline that generates 249k training samples with intermediate visual supervision. Experiments across seven benchmarks and four popular models demonstrate significant performance gains, underscoring the method’s effectiveness and broad applicability.

By Ye Mo, Kai Ye, Xianwei Mao, Zirui Shao, Gang Huang, Bo Zhang, Hangdi Xing, Kehan Chen, Huan Zhou, Zixu Yan, Jiajun Bu, Sheng Zhou