Zhipeng Xu
Publications
CC-OCR V2: Fine-Grained Attribution of LMM Failures in Real-World Visual Document Understanding
Recent Large Multimodal Models (LMMs) have achieved remarkable progress on OCR-centric document understanding and processing tasks. Existing benchmarks primarily evaluate LMMs across diverse tasks to reflect practical document-processing workflows or analyze how document characteristics influence model performance. However, they provide limited insight into the reliability of LMMs under real-world document acquisition conditions, where factors such as lighting, screen displays, imaging quality, and capture methods can substantially affect performance. To bridge this gap, we present CC-OCR v2, a comprehensive benchmark for attributing LMM failures in real-world document processing. CC-OCR v2 provides a unified evaluation framework covering five core document-processing capabilities through 16 subtasks, 74 application scenarios, and 7,093 samples. The benchmark spans five evaluation tracks, ten document categories, and 32 languages in the recognition suite. Beyond task-level evaluation, each sample is annotated with ten fine-grained document factors, enabling systematic attribution of model failures across document types and acquisition conditions. Extensive experiments on 17 representative LMMs reveal substantial performance variation across tasks, document categories, and real-world conditions. Moreover, models with comparable overall accuracy often exhibit fundamentally different failure patterns under specific document factors, highlighting a significant gap between benchmark-level performance and reliable deployment in practical applications. The dataset and evaluation toolkit are publicly available at https://github.com/eioss/CC-OCR-V2.
Lang2Act: Fine-Grained Visual Reasoning through Self-Emergent Linguistic Toolchains
Visual Retrieval-Augmented Generation (VRAG) enhances Vision-Language Models (VLMs) by incorporating external visual documents to address a given query. Existing VRAG frameworks usually depend on rigid, pre-defined external tools to extend the perceptual capabilities of VLMs, typically by explicitly separating visual perception from subsequent reasoning processes. However, this decoupled design can lead to unnecessary loss of visual information, particularly when image-based operations such as cropping are applied. In this paper, we propose Lang2Act, which enables fine-grained visual perception and reasoning through self-emergent linguistic toolchains. Rather than invoking fixed external engines, Lang2Act collects self-emergent actions as linguistic tools and leverages them to enhance the visual perception capabilities of VLMs. To support this mechanism, we design a two-stage Reinforcement Learning (RL)-based training framework. Specifically, the first stage optimizes VLMs to self-explore high-quality actions for constructing a reusable linguistic toolbox, and the second stage further optimizes VLMs to exploit these linguistic tools for downstream reasoning effectively. Experimental results demonstrate the effectiveness of Lang2Act in substantially enhancing the visual perception capabilities of VLMs, achieving performance improvements of over 4%. All code and data are available at https://github.com/NEUIR/Lang2Act.