7 papers
WeVisDoc: From Coverage to Capability for Robust End-to-End Document Parsing
Hao Yu, Kang Liu, Linnan Zhao +4
Document parsing converts document images into structured content and requires reliable performance across diverse layouts and acquisition conditions. Yet training corpora are bias…
OCR-EDR: Rendering-Aware Diagnosis and Repair for Closed-Loop OCR Improvement
Linnan Zhao, Kang Liu, Hao Yu +3
Although document OCR systems perform increasingly well on routine documents, complex formulas, structured text, and long-tail formats remain error-prone. OCR predictions may omit…
PaDoc: Layout-Grounded Parallel Decoding for Document Parsing
Hao Yu, Jiabo Zhan, Kang Liu +8
End-to-end document parsers provide a unified interface, but serialize page layouts and regional contents into one autoregressive sequence. This formulation forces independent regi…
V-Thinker: Interactive Thinking with Images
Runqi Qiao, Qiuna Tan, Minghan Yang +11
Empowering Large Multimodal Models (LMMs) to deeply integrate image interaction with long-horizon reasoning capabilities remains a long-standing challenge in this field. Recent adv…
VACoT: Rethinking Visual Data Augmentation with VLMs
Zhengzhuo Xu, Chong Sun, SiNan Du +3
While visual data augmentation remains a cornerstone for training robust vision models, it has received limited attention in visual language models (VLMs), which predominantly rely…
Patch-as-Decodable-Token: Towards Unified Multi-Modal Vision Tasks in MLLMs
Yongyi Su, Haojie Zhang, Shijie Li +11
Multimodal large language models (MLLMs) have advanced rapidly in recent years. However, existing approaches for vision tasks often rely on indirect representations, such as genera…
We-Math 2.0: A Versatile MathBook System for Incentivizing Visual Mathematical Reasoning
Runqi Qiao, Qiuna Tan, Peiqing Yang +11
Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities across various tasks, but still struggle with complex mathematical reasoning. Existing research p…