11 papers
SEMA-RAG: A Self-Evolving Multi-Agent Retrieval-Augmented Generation Framework for Medical Reasoning
Yongfeng Huang, Ruiying Chen, James Cheng
Retrieval-Augmented Generation (RAG) is widely employed to mitigate risks such as hallucinations and knowledge obsolescence in medical question answering, yet its predominantly sin…
EnCAgg: Enhanced Clustering Aggregation for Robust Federated Learning against Dynamic Model Poisoning
Tianyun Zhang, Zhen Yang, Haozhao Wang +2
Federated learning faces increasing threats from model poisoning attacks, which harms its application to improve privacy. Existing defense methods typically rely on fixed threshold…
Knowledge Poisoning Attacks on Medical Multi-Modal Retrieval-Augmented Generation
Peiru Yang, Haoran Zheng, Tong Ju +6
Retrieval-augmented generation (RAG) is a widely adopted paradigm for enhancing LLMs in medical applications by incorporating expert multimodal knowledge during generation. However…
ADEPT: An Entropy-Driven Dual-Strategy Agent for Interactive Video Retrieval
Ke Chen, Shengyuan Han, Yongfeng Huang +4
This research aims to solve the challenge of video retrieval from massive datasets, caused by ambiguous user queries. Prevailing single-round retrieval paradigms face a performance…
RedNote-Vibe: A Dataset for Capturing Temporal Dynamics of AI-Generated Text in Lifestyle Social Media
Yudong Li, Yufei Sun, Peiru Yang +7
We introduce RedNote-Vibe, a dataset spanning five years (pre-LLM to July 2025) sourced from lifestyle platform RedNote (Xiaohongshu), capturing the temporal dynamics of content cr…
LiveSecBench: A Dynamic and Event-Driven Safety Benchmark for Chinese Language Model Applications
Yudong Li, Peiru Yang, Feng Huang +18
We introduce LiveSecBench, a continuously updated safety benchmark specifically for Chinese-language LLM application scenarios. LiveSecBench constructs a high-quality and unique da…