3 papers
cs.CL2026
MedPrune: Topology-Efficient Multimodal Multi-Agent Communication Evolution for Medical VQA Tasks
Jiuheng Wan, Runze Li, Chen Chen +5
While medical multimodal large language models (Med-MLLMs) advance medical visual question answering (VQA), existing clinical workflow-inspired multi-agent frameworks suffer from i…
cs.CL2026
An Empirical Study of Development-Tuned Representation Sensitivity for Medical QA Data Selection
Yuexin Wu, Dayou Yu, Vasile Rus
We study medical question-answering (QA) data selection when a developer already has questions, labels, and auxiliary explanatory texts but can retain only examples for a final…
cs.MM2026
MPrune: Hierarchical Collaborative Pruning for Efficient Multi-Modal Multi-Agent Retrieval-Augmented Generation
Taolin Zhang, Weizi shao, Zijie Zhou +5
Recent advances in multi-modal retrieval-augmented generation (mRAG), which augments multi-modal large language models (MLLMs) with external knowledge, have shown that collective i…