Fast and Faithful: Real-Time Verification for Long-Document Retrieval-Augmented Generation Systems

ArXi:2603.23508v1 Announce Type: new Retrieval-augmented generation (RAG) is increasingly deployed in enterprise search and document-centric assistants, where responses must be grounded in long and complex source materials. In practice, verifying that generated answers faithfully reflect retrieved documents is difficult: large language models can check long contexts but are too slow and costly for interactive services, while lightweight classifiers operate within strict context limits and frequently miss evidence outside truncated passages.