RADAR: Defending RAG Dynamically against Retrieval Corruption
摘要
While RAG systems are increasingly deployed in dynamic web search, temporal volatility amplifies their vulnerability to adversarial attacks. Existing static-oriented defenses struggle to handle evolving threats and incur prohibitive storage costs in dynamic settings. We propose RADAR, a framework that models reliable context selection as a graph-based energy minimization problem, solved exactly via Max-Flow Min-Cut. By incorporating a Bayesian memory node, RADAR recursively updates a belief state instead of archiving raw historical documents, effectively balancing stability against attacks with adaptability to genuine knowledge shifts. Experiments on a novel dynamic dataset show that RADAR achieves superior robustness and response quality with minimal storage overhead compared to the baselines.
引用
@article{arxiv.2605.22041,
title = {RADAR: Defending RAG Dynamically against Retrieval Corruption},
author = {Ziyuan Chen and Yueming Lyu and Yi Liu and Weixiang Han and Jing Dong and Caifeng Shan and Tieniu Tan},
journal= {arXiv preprint arXiv:2605.22041},
year = {2026}
}