AI RESEARCH
RADAR: Defending RAG Dynamically against Retrieval Corruption
arXiv CS.LG
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ArXi:2605.22041v1 Announce Type: cross 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.