activity
20212026
most citedLLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model

119 citations · 379 across the 62 of their papers we have counts for

collaborators

67 papers

cs.CV2026

From Diagnosis to Correction: Benchmarking and Improving Real-World Table Parsing

Jutao Xiao, Yuan Qu, Dongsheng Ma +7

Recent document parsers achieve table TEDS scores above 93 on OmniDocBench v1.6, yet community feedback and our audit reveal persistent failures on complex real-world tables. To qu…

cs.AI2026

PosterMELD: Multi-Agent Paper-to-Poster Generation for Controllable Design Diversity with Editable Print-Ready Outputs

Haojie Hu, Chenhao Dang, Yaojia Liu +3

Scientific poster construction compresses a long multimodal paper into a readable, editable canvas. Existing systems hide request-level failures by scoring only completed outputs;…

cs.MM2026

MLLM-DataEngine: Closing the Loop of Multimodal Instruction Tuning Data Generation

Zhiyuan Zhao, Bin Wang, Linke Ouyang +5

In this paper, we propose MLLM-DataEngine, a novel closed-loop system that bridges data generation, model training, and evaluation. Within each loop iteration, the MLLM-DataEngine…

cs.AI2026

ReMMD: Realistic Multilingual Multi-Image Agentic Verification for Multimodal Misinformation Detection

Chenhao Dang, Dantong Zhu, Jun Yang +2

Multimodal misinformation detection is increasingly important because viral posts now combine long multilingual narratives, several images, mixed provenance, and subtle cross-modal…

cs.CV2026

MinerU-Popo: Universal Post-Processing Model for Structured Document Parsing

Bangrui Xu, Ziyang Miao, Xuanhe Zhou +7

VLM-based OCR models have become the de facto choice for document parsing, as they can accurately extract page-level elements (e.g., paragraphs within individual pages) together wi…

cs.CV2026

Project Imaging-X: A Survey of 1000+ Open-Access Medical Imaging Datasets for Foundation Model Development

Zhongying Deng, Cheng Tang, Ziyan Huang +124

Foundation models have demonstrated remarkable success across diverse domains and tasks, primarily due to the thrive of large-scale, diverse, and high-quality datasets. However, in…

cs.AI2026

The Trinity of Consistency as a Defining Principle for General World Models

Jingxuan Wei, Siyuan Li, Yuhang Xu +21

The construction of World Models capable of learning, simulating, and reasoning about objective physical laws constitutes a foundational challenge in the pursuit of Artificial Gene…

cs.IR2026

MoDora: Tree-Based Semi-Structured Document Analysis System

Bangrui Xu, Qihang Yao, Zirui Tang +8

Semi-structured documents integrate diverse interleaved data elements (e.g., tables, charts, hierarchical paragraphs) arranged in various and often irregular layouts. These documen…

cs.DB2026

Can LLMs Clean Up Your Mess? A Survey of Application-Ready Data Preparation with LLMs

Wei Zhou, Jun Zhou, Haoyu Wang +16

Data preparation aims to denoise raw datasets, uncover cross-dataset relationships, and extract valuable insights from them, which is essential for a wide range of data-centric app…

cs.CL2025

OPV: Outcome-based Process Verifier for Efficient Long Chain-of-Thought Verification

Zijian Wu, Lingkai Kong, Wenwei Zhang +12

Large language models (LLMs) have achieved significant progress in solving complex reasoning tasks by Reinforcement Learning with Verifiable Rewards (RLVR). This advancement is als…

cs.CV2025

DOCR-Inspector: Fine-Grained and Automated Evaluation of Document Parsing with VLM

Qintong Zhang, Junyuan Zhang, Zhifei Ren +8

Document parsing aims to transform unstructured PDF images into semi-structured data, facilitating the digitization and utilization of information in diverse domains. While vision…

cs.CV2025

ARM-Thinker: Reinforcing Multimodal Generative Reward Models with Agentic Tool Use and Visual Reasoning

Shengyuan Ding, Xinyu Fang, Ziyu Liu +10

Reward models are critical for aligning vision-language systems with human preferences, yet current approaches suffer from hallucination, weak visual grounding, and an inability to…

cs.CL2025

AICC: Parse HTML Finer, Make Models Better -- A 7.3T AI-Ready Corpus Built by a Model-Based HTML Parser

Ren Ma, Jiantao Qiu, Chao Xu +26

While web data quality is crucial for large language models, most curation efforts focus on filtering and deduplication,treating HTML-to-text extraction as a fixed pre-processing s…

cs.CV2025

OmniDocLayout: Towards Diverse Document Layout Generation via Coarse-to-Fine LLM Learning

Hengrui Kang, Zhuangcheng Gu, Zhiyuan Zhao +4

Document AI has advanced rapidly and is attracting increasing attention. Yet, while most efforts have focused on document layout analysis (DLA), its generative counterpart, layout…

cs.CL2025

ATLAS: A High-Difficulty, Multidisciplinary Benchmark for Frontier Scientific Reasoning

Hongwei Liu, Junnan Liu, Shudong Liu +33

The rapid advancement of Large Language Models (LLMs) has led to performance saturation on many established benchmarks, questioning their ability to distinguish frontier models. Co…

cs.CL2025

Dripper: Token-Efficient Main HTML Extraction with a Lightweight LM

Mengjie Liu, Jiahui Peng, Wenchang Ning +14

High-quality main content extraction from web pages is a critical prerequisite for constructing large-scale training corpora. While traditional heuristic extractors are efficient,…

cs.AI2025★ 2 cited

LLM/Agent-as-Data-Analyst: A Survey

Zirui Tang, Weizheng Wang, Zihang Zhou +16

Large language models (LLMs) and agent techniques have brought a fundamental shift in the functionality and development paradigm of data analysis tasks (a.k.a LLM/Agent-as-Data-Ana…

cs.CV2025★ 2 cited

MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing

Junbo Niu, Zheng Liu, Zhuangcheng Gu +58

We introduce MinerU2.5, a 1.2B-parameter document parsing vision-language model that achieves state-of-the-art recognition accuracy while maintaining exceptional computational effi…

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.LG2025

Intern-S1: A Scientific Multimodal Foundation Model

Lei Bai, Zhongrui Cai, Yuhang Cao +173

In recent years, a plethora of open-source foundation models have emerged, achieving remarkable progress in some widely attended fields, with performance being quite close to that…

cs.CL2025★ 1 cited

A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontiers

Ming Hu, Chenglong Ma, Wei Li +117

Scientific Large Language Models (Sci-LLMs) are transforming how knowledge is represented, integrated, and applied in scientific research, yet their progress is shaped by the compl…

cs.CV2025

VRBench: A Benchmark for Multi-Step Reasoning in Long Narrative Videos

Jiashuo Yu, Yue Wu, Meng Chu +14

We present VRBench, the first long narrative video benchmark crafted for evaluating large models' multi-step reasoning capabilities, addressing limitations in existing evaluations…

cs.DB2025

A Survey of LLM DATA

Xuanhe Zhou, Junxuan He, Wei Zhou +14

The integration of large language model (LLM) and data management (DATA) is rapidly redefining both domains. In this survey, we comprehensively review the bidirectional relationshi…

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…