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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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14 papers · 1 filter

cs.AI2026

MedEvo: Medical Reasoning Evolution through Knowledge Navigation

Xiaoliang Wang, Zhuo Chen, Chuanyang Zheng +11

Medical multimodal reasoning models are typically trained on teacher-generated rationales, discarding the valid reasoning in students' own failed trajectories, while general-domain…

cs.AI2026

SPARK: Skeleton-Guided Reasoning Synthesis from Large-Scale Scientific Literature

Yu Li, Wei Li, Xin Gao +4

Scientific reasoning remains challenging for open-source models, largely due to the lack of high-quality scientific reasoning data. Existing datasets are often dominated by factual…

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.AI2026

MolRecBench-Wild: A Real-World Benchmark for Optical Chemical Structure Recognition

Haote Yang, Hui Wang, Chen Zhu +14

Optical Chemical Structure Recognition (OCSR) aims to translate molecular diagrams in scientific literature into machine-readable formats, but current systems remain unreliable on…

cs.AI2026

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs

Yu Li, Xiaoran Shang, Qizhi Pei +11

Post-training data plays a pivotal role in shaping the capabilities of Large Language Models (LLMs), yet datasets are often treated as isolated artifacts, overlooking the systemic…

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.LG2026

Unlocking Data Value in Finance: A Study on Distillation and Difficulty-Aware Training

Chuxue Cao, Honglin Lin, Zhanping Zhong +5

Large Language Models (LLMs) have demonstrated strong general capabilities, yet their deployment in finance remains challenging due to dense domain-specific terminology, stringent…

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.AI2026

LRAS: Advanced Legal Reasoning with Agentic Search

Yujin Zhou, Chuxue Cao, Jinluan Yang +4

While Large Reasoning Models (LRMs) have demonstrated exceptional logical capabilities in mathematical domains, their application to the legal field remains hindered by the strict…

cs.LG2026

Pushing the Boundaries of Natural Reasoning: Interleaved Bonus from Formal-Logic Verification

Chuxue Cao, Jinluan Yang, Haoran Li +8

Large Language Models (LLMs) show remarkable capabilities, yet their stochastic next-token prediction creates logical inconsistencies and reward hacking that formal symbolic system…

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…