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FLARE: Fully Integration of Vision-Language Representations for Deep Cross-Modal Understanding
Zheng Liu, Mengjie Liu, Jingzhou Chen +4
We introduce FLARE, a family of vision language models (VLMs) with a fully vision-language alignment and integration paradigm. Unlike existing approaches that rely on single MLP pr…
MinerU2.5-Pro: Pushing the Limits of Data-Centric Document Parsing at Scale
Bin Wang, Tianyao He, Linke Ouyang +40
Current document parsing methods advance primarily through model architecture innovation, while systematic engineering of training data remains underexplored. Yet state-of-the-art…
MinerU-Diffusion: Rethinking Document OCR as Inverse Rendering via Diffusion Decoding
Hejun Dong, Junbo Niu, Bin Wang +3
Optical character recognition (OCR) has evolved from line-level transcription to structured document parsing, requiring models to recover long-form sequences containing layout, tab…
Native Visual Understanding: Resolving Resolution Dilemmas in Vision-Language Models
Junbo Niu, Yuanhong Zheng, Ziyang Miao +8
Vision-Language Models (VLMs) face significant challenges when dealing with the diverse resolutions and aspect ratios of real-world images, as most existing models rely on fixed, l…
Multi-Step Visual Reasoning with Visual Tokens Scaling and Verification
Tianyi Bai, Zengjie Hu, Fupeng Sun +7
Multi-modal large language models (MLLMs) have achieved remarkable capabilities by integrating visual perception with language understanding, enabling applications such as image-gr…
Hallucination at a Glance: Controlled Visual Edits and Fine-Grained Multimodal Learning
Tianyi Bai, Yuxuan Fan, Jiantao Qiu +7
Multimodal large language models (MLLMs) have achieved strong performance on vision-language tasks but still struggle with fine-grained visual differences, leading to hallucination…