Fast and Faithful: Real-Time Verification for Long-Document Retrieval-Augmented Generation Systems
Summary
This paper presents a real-time verification system for retrieval-augmented generation that processes long documents up to 32K tokens, using adaptive inference strategies to balance latency and verification coverage. It provides practical guidance for building reliable RAG systems.
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Paper page - Fast and Faithful: Real-Time Verification for Long-Document Retrieval-Augmented Generation Systems
Source: https://huggingface.co/papers/2603.23508
Abstract
A real-time verification system for retrieval-augmented generation that processes long documents and balances latency constraints with comprehensive answer validation.
Retrieval-augmented generation(RAG) is increasingly deployed in enterprise search anddocument-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 modelscan 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. We present the design of a real-time verification component integrated into a production RAG pipeline that enablesfull-document groundingunderlatency constraints. The system processes documents up to 32K tokens and employsadaptive inference strategiesto balance response time and verification coverage across workloads. We describe the architectural decisions, operational trade-offs, and evaluation methodology used to deploy the verifier, and show that full-context verification substantially improves detection of unsupported responses compared with truncated validation. Our experience highlights when long-context verification is necessary, whychunk-based checkingoften fails in real documents, and how latency budgets shape model design. These findings provide practical guidance for practitioners building reliable large-scale retrieval-augmented applications. (Model, benchmark, and code: https://huggingface.co/llm-semantic-router)
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