RAGGate: Mitigating RAG Collapse through Admission Control

Retrieval-augmented generation (RAG) systems can suffer from RAG collapse when their own earlier outputs are retrieved and reused, causing later answers to concentrate on fewer alternatives. We study whether document admission control can mitigate this feedback while preserving adaptation to newly arriving external information. We propose RAGGate, which determines whether an incoming document becomes eligible for retrieval using a score intended to capture information beyond previously admitted evidence. We instantiate this framework in two ways: RAGGate-Geo uses an embedding-based residual, while RAGGate-LLM asks the generator whether a document adds information. In controlled feedback loops designed to isolate repeated self-retrieval, both variants more than halve answer collision probability and improve knowledge renewal relative to admitting all incoming documents. In a complementary real-text experiment, both variants reject most derived documents while still admitting many incoming human-written pages.