most citedLingshu: A Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoning

5 citations · 6 across the 9 of their papers we have counts for

collaborators

11 papers

cs.CV2026

World Embedding Benchmark

Yiqi Liu, Ruifeng Yuan, Yang Wang +7

Physical fidelity has received increasing attention in world models and video generation, yet how video representations encode physical information remains less understood. We intr…

cs.CL2026

AutoDataBench: A Data-centric Testbed for Accelerating Auto Research

Ruifeng Yuan, Yizhi Li, Yaxin Du +9

Existing auto-research benchmarks often entangle multiple sources of improvement, including training frameworks, hyperparameters, compute budgets, and data, making it difficult to…

cs.CV2026

On the Design Fundamentals of Pixel Text Representation Learning

Chaohao Yuan, Ruifeng Yuan, Zhuoxu Huang +4

Text-rich visual inputs require models that can read, retrieve, and compress language directly in pixel space, yet existing pixel-text encoders struggle with fixed resolution pretr…

cs.CV2026

RadSight: Towards Perceptually Reliable Multimodal Radiology Image Understanding

Jianqin Liu, Weiwei Cao, Wanxing Chang +7

Medical multimodal large language models (MLLMs) are increasingly expected to perform complex image understanding tasks, yet their reliability is often compromised by frequent erro…

cs.CE2026

AtomiMed: Hierarchical Atomic Fact-Checking for Universal Clinical-Aware Medical Report Evaluation

Yuan Wang, Wanxing Chang, Songtao Jiang +8

Traditional metrics for Medical Report Generation (MRG) predominantly rely on surface-level n-gram overlap, which fails to capture clinical factual accuracy and often overlooks cat…

cs.CV2026

Disease-Centric Vision-Language Pretraining with Hybrid Visual Encoding for 3D Computed Tomography

Bowen Shi, Weiwei Cao, Ruifeng Yuan +5

Vision-language pre-training (VLP) holds great promise for general-purpose medical AI by leveraging radiology reports as rich textual supervision, yet existing methods struggle wit…

cs.CL2026

Understanding the Behaviors of Environment-aware Information Retrieval

Ruifeng Yuan, Chaohao Yuan, David Dai +4

Recent retrieval-augmented generation (RAG) approaches have demonstrated strong capability in handling complex queries, yet current research overlooks a critical challenge: differe…

cs.AI2026★ 1 cited

CT-FineBench: A Diagnostic Fidelity Benchmark for Fine-Grained Evaluation of CT Report Generation

Ruifeng Yuan, Wanxing Chang, Weiwei Cao +4

The evaluation of generated reports remains a critical challenge in Computed Tomography (CT) report generation, due to the large volume of text, the diversity and complexity of fin…

cs.CV2026

Rethinking the Efficiency and Effectiveness of Reinforcement Learning for Radiology Report Generation

Zilin Lu, Ruifeng Yuan, Weiwei Cao +6

Radiologists highly desire fully automated AI for radiology report generation (R2G), yet existing approaches fall short in clinical utility. Reinforcement learning (RL) holds poten…

cs.CV2025

VL-Cogito: Progressive Curriculum Reinforcement Learning for Advanced Multimodal Reasoning

Ruifeng Yuan, Chenghao Xiao, Sicong Leng +9

Reinforcement learning has proven its effectiveness in enhancing the reasoning capabilities of large language models. Recent research efforts have progressively extended this parad…

cs.CL2025★ 5 cited

Lingshu: A Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoning

LASA Team, Weiwen Xu, Hou Pong Chan +16

Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities in understanding common visual elements, largely due to their large-scale datasets and advanced t…