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20242026
most citedCARES: A Comprehensive Benchmark of Trustworthiness in Medical Vision Language Models

4 citations · 12 across the 11 of their papers we have counts for

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6 papers · 1 filter

cs.LG2026

MedVerse: Efficient and Reliable Medical Reasoning via DAG-Structured Parallel Execution

Jianwen Chen, Xinyu Yang, Peng Xia +7

Large language models (LLMs) have demonstrated strong performance and rapid progress in a wide range of medical reasoning tasks. However, their sequential autoregressive decoding f…

cs.LG2025

MMedAgent-RL: Optimizing Multi-Agent Collaboration for Multimodal Medical Reasoning

Peng Xia, Jinglu Wang, Yibo Peng +10

Medical Large Vision-Language Models (Med-LVLMs) have shown strong potential in multimodal diagnostic tasks. However, existing single-agent models struggle to generalize across div…

cs.LG20254 cited

MDocAgent: A Multi-Modal Multi-Agent Framework for Document Understanding

Siwei Han, Peng Xia, Ruiyi Zhang +4

Document Question Answering (DocQA) is a very common task. Existing methods using Large Language Models (LLMs) or Large Vision Language Models (LVLMs) and Retrieval Augmented Gener…

cs.LG20241 cited

RULE: Reliable Multimodal RAG for Factuality in Medical Vision Language Models

Peng Xia, Kangyu Zhu, Haoran Li +5

The recent emergence of Medical Large Vision Language Models (Med-LVLMs) has enhanced medical diagnosis. However, current Med-LVLMs frequently encounter factual issues, often gener…

cs.LG20244 cited

CARES: A Comprehensive Benchmark of Trustworthiness in Medical Vision Language Models

Peng Xia, Ze Chen, Juanxi Tian +21

Artificial intelligence has significantly impacted medical applications, particularly with the advent of Medical Large Vision Language Models (Med-LVLMs), sparking optimism for the…

cs.LG20241 cited

Calibrated Self-Rewarding Vision Language Models

Yiyang Zhou, Zhiyuan Fan, Dongjie Cheng +7

Large Vision-Language Models (LVLMs) have made substantial progress by integrating pre-trained large language models (LLMs) and vision models through instruction tuning. Despite th…