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cs.CV2026

Beyond Correctness: Benchmarking and Aligning Response Behaviors in Hybrid-Thinking MLLMs

Xinming Wang, Weinong Wang, Hongming Yang +13

Hybrid-thinking multimodal large language models (MLLMs) allow a single model to alternate between deliberative thinking and latency-efficient non-thinking inference. Although thes…

cs.CV2026

HunyuanOCR-1.5: Making Lightweight OCR VLMs Faster and Better

Gengluo Li, Xingyu Wan, Shangpin Peng +20

We present HunyuanOCR-1.5, a lightweight end-to-end OCR-specialized vision-language model. HunyuanOCR unifies document parsing, text spotting, information extraction, text-image tr…

cs.CV2026

HyMobileAgent: Data-Environment Co-Scaling for Efficient GUI Agents

Hy Vision Team, Huawen Shen, Zhengyang Tang +20

As large multimodal models move from understanding content to operating on digital environments, mobile GUI has emerged as a challenging and consequential testbed for digital embod…

cs.CV2026

ChartArena: Benchmarking Chart Parsing across Languages, Scenarios, and Formats

Shangpin Peng, Gengluo Li, Xingyu Wan +10

Charts are a primary medium for conveying quantitative and relational information, yet systematically evaluating chart parsing models remains difficult. Existing benchmarks focus o…

cs.CV2026

StrucTab: A Structured Optimization Framework for Table Parsing

Gengluo Li, Shangpin Peng, Chengquan Zhang +10

Table parsing aims to convert table images into structured, machine-readable representations, a task requiring the joint perception of complex spatial layouts and textual content.…

cs.CV2026

Towards Real-World Document Parsing via Realistic Scene Synthesis and Document-Aware Training

Gengluo Li, Pengyuan Lyu, Chengquan Zhang +7

Document parsing has recently advanced with multimodal large language models (MLLMs) that directly map document images to structured outputs. Traditional cascaded pipelines depend…

cs.CV2026

Enhancing MLLM Spatial Understanding via Active 3D Scene Exploration for Multi-Perspective Reasoning

Jiahua Chen, Qihong Tang, Weinong Wang +1

Although Multimodal Large Language Models have achieved remarkable progress, they still struggle with complex 3D spatial reasoning due to the reliance on 2D visual priors. Existing…

cs.CV2026

CF-VLA: Efficient Coarse-to-Fine Action Generation for Vision-Language-Action Policies

Fan Du, Feng Yan, Jianxiong Wu +8

Flow-based vision-language-action (VLA) policies offer strong expressivity for action generation, but suffer from a fundamental inefficiency: multi-step inference is required to re…

cs.CV2026

MMTIT-Bench: A Multilingual and Multi-Scenario Benchmark with Cognition-Perception-Reasoning Guided Text-Image Machine Translation

Gengluo Li, Chengquan Zhang, Yupu Liang +9

End-to-end text-image machine translation (TIMT), which directly translates textual content in images across languages, is crucial for real-world multilingual scene understanding.…

cs.CV2026

VideoTIR: Accurate Understanding for Long Videos with Efficient Tool-Integrated Reasoning

Zhe Gao, Shiyu Shen, Taifeng Chai +7

Existing Multimodal Large Language Models (MLLMs) often suffer from hallucinations in long video understanding (LVU), primarily due to the imbalance between textual and visual toke…

cs.CV2025

HunyuanOCR Technical Report

Hunyuan Vision Team, Pengyuan Lyu, Xingyu Wan +29

This paper presents HunyuanOCR, a commercial-grade, open-source, and lightweight (1B parameters) Vision-Language Model (VLM) dedicated to OCR tasks. The architecture comprises a Na…

cs.CV2025

Exploring Implicit Visual Misunderstandings in Multimodal Large Language Models through Attention Analysis

Pengfei Wang, Guohai Xu, Weinong Wang +3

Recent advancements have enhanced the capability of Multimodal Large Language Models (MLLMs) to comprehend multi-image information. However, existing benchmarks primarily evaluate…

cs.CV2025

SedarEval: Automated Evaluation using Self-Adaptive Rubrics

Zhiyuan Fan, Weinong Wang, Xing Wu +1

The evaluation paradigm of LLM-as-judge gains popularity due to its significant reduction in human labor and time costs. This approach utilizes one or more large language models (L…