most citedHM-RAG: Hierarchical Multi-Agent Multimodal Retrieval Augmented Generation

1 citations · 1 across the 4 of their papers we have counts for

collaborators

8 papers

cs.AI2025

EpiPlanAgent: Agentic Automated Epidemic Response Planning

Kangkun Mao, Fang Xu, Jinru Ding +8

Epidemic response planning is essential yet traditionally reliant on labor-intensive manual methods. This study aimed to design and evaluate EpiPlanAgent, an agent-based system usi…

cs.CV2025

ReBrain: Brain MRI Reconstruction from Sparse CT Slice via Retrieval-Augmented Diffusion

Junming Liu, Yifei Sun, Weihua Cheng +4

Magnetic Resonance Imaging (MRI) plays a crucial role in brain disease diagnosis, but it is not always feasible for certain patients due to physical or clinical constraints. Recent…

cs.LG2025

TimeMKG: Knowledge-Infused Causal Reasoning for Multivariate Time Series Modeling

Yifei Sun, Junming Liu, Yirong Chen +2

Multivariate time series data typically comprises two distinct modalities: variable semantics and sampled numerical observations. Traditional time series models treat variables as…

cs.CL2025

From Ranking to Selection: A Simple but Efficient Dynamic Passage Selector for Retrieval Augmented Generation

Siyuan Meng, Junming Liu, Yirong Chen +5

Retrieval-augmented generation (RAG) systems are often bottlenecked by their reranking modules, which typically score passages independently and select a fixed Top-K size. This app…

cs.CV2025

Boosting Adversarial Transferability via Commonality-Oriented Gradient Optimization

Yanting Gao, Yepeng Liu, Junming Liu +4

Exploring effective and transferable adversarial examples is vital for understanding the characteristics and mechanisms of Vision Transformers (ViTs). However, adversarial examples…

cs.CL20251 cited

HM-RAG: Hierarchical Multi-Agent Multimodal Retrieval Augmented Generation

Pei Liu, Xin Liu, Ruoyu Yao +4

While Retrieval-Augmented Generation (RAG) augments Large Language Models (LLMs) with external knowledge, conventional single-agent RAG remains fundamentally limited in resolving c…