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20242026
most citedInternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models

8 citations · 15 across the 38 of their papers we have counts for

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12 papers · 1 filter

cs.CV2026

MinerU.Chem: A High-Precision System for Optical Chemical Structure and Reaction Recognition

Haote Yang, Jiang Wu, Jingchao Wang +42

In organic chemistry papers and patents, molecular structures, reaction schemes, and experimental conditions are often presented as molecular structure depictions, reaction diagram…

cs.CV2026★ 1 cited

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…

cs.CV2026

Scientific Graphics Program Synthesis via Dual Self-Consistency Reinforcement Learning

Juekai Lin, Yun Zhu, Honglin Lin +6

Graphics Program Synthesis is pivotal for interpreting and editing visual data, effectively facilitating the reverse-engineering of static visuals into editable TikZ code. While Ti…

cs.CV2026

Molecular Identifier Visual Prompt and Verifiable Reinforcement Learning for Chemical Reaction Diagram Parsing

Jiahe Song, Chuang Wang, Yinfan Wang +11

Reaction diagram parsing (RxnDP) is critical for extracting chemical synthesis information from literature. Although recent Vision-Language Models (VLMs) have emerged as a promisin…

cs.CV2026

MMFineReason: Closing the Multimodal Reasoning Gap via Open Data-Centric Methods

Honglin Lin, Zheng Liu, Yun Zhu +6

Recent advances in Vision Language Models (VLMs) have driven significant progress in visual reasoning. However, open-source VLMs still lag behind proprietary systems, largely due t…

cs.CV2026

Scientific Image Synthesis: Benchmarking, Methodologies, and Downstream Utility

Honglin Lin, Zheng Liu, Chonghan Qin +7

While synthetic data has proven effective for improving scientific reasoning in the text domain, multimodal reasoning remains constrained by the difficulty of synthesizing scientif…

cs.CV2026

ChartVerse: Scaling Chart Reasoning via Reliable Programmatic Synthesis from Scratch

Zheng Liu, Honglin Lin, Chonghan Qin +13

Chart reasoning is a critical capability for Vision Language Models (VLMs). However, the development of open-source models is severely hindered by the lack of high-quality training…

cs.CV2025

Envision: Benchmarking Unified Understanding & Generation for Causal World Process Insights

Juanxi Tian, Siyuan Li, Conghui He +2

Current multimodal models aim to transcend the limitations of single-modality representations by unifying understanding and generation, often using text-to-image (T2I) tasks to cal…

cs.CV2025

RxnCaption: Reformulating Reaction Diagram Parsing as Visual Prompt Guided Captioning

Jiahe Song, Chuang Wang, Bowen Jiang +13

Large-scale chemical reaction datasets are crucial for AI research in chemistry. However, existing chemical reaction data often exist as images within papers, making them not machi…

cs.CV2025★ 4 cited

InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency

Weiyun Wang, Zhangwei Gao, Lixin Gu +72

We introduce InternVL 3.5, a new family of open-source multimodal models that significantly advances versatility, reasoning capability, and inference efficiency along the InternVL…

cs.CV2025★ 8 cited

InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models

Jinguo Zhu, Weiyun Wang, Zhe Chen +48

We introduce InternVL3, a significant advancement in the InternVL series featuring a native multimodal pre-training paradigm. Rather than adapting a text-only large language model…

cs.CV2025

Benchmarking and Boosting Multilingual Capabilities of LVLMs via OCR-Centric Reinforcement Learning

Junyuan Gao, Jiahe Song, Jiang Wu +9

Evaluating the multilingual capabilities of Large Vision-Language Models (LVLMs) remains challenging because most benchmarks rely on non-parallel corpora, making it unclear whether…