New system enables real-time verification of AI-generated answers against full documents up to 32K tokens while maintaining interactive response speeds.

Researchers developed a verification component for retrieval-augmented generation (RAG) systems that can ground responses in long, complex documents without sacrificing speed

The system handles documents up to 32,000 tokens, overcoming limitations of lightweight classifiers that typically work only with truncated passages

Uses adaptive inference strategies to balance verification accuracy and response latency for different workloads in production enterprise search and document-centric assistant applications

Addresses the critical problem of ensuring LLM-generated answers faithfully reflect retrieved source materials while remaining cost-effective for interactive services