Conformal Factuality for Multi-Hop Retrieval-Augmented Generation

Retrieval-augmented generation (RAG) improves the factual grounding of large language models by conditioning generation on retrieved evidence, yet generated responses may still contain unsupported claims. This challenge is particularly pronounced in multi-hop RAG, where retrieval and reasoning are performed iteratively and errors can propagate across intermediate steps. Conformal prediction offers a principled framework for controlling such errors by providing statistical guarantees under minimal distributional assumptions, but its application to multi-hop retrieval and reasoning remains underexplored. In this work, we investigate conformal factuality for multi-hop RAG. We formulate factuality control over claims produced through iterative retrieval and reasoning and study how conformal filtering affects the reliability and informativeness of generated responses. We develop an experimental framework that applies claim-level conformal calibration to both single-hop and multi-hop RAG, enabling controlled comparison across different target factuality levels. Our preliminary experiments show the expected reliability–informativeness trade-off: increasingly stringent conformal targets retain fewer claims while improving the factual reliability of the retained output. We further investigate how this behavior changes under multi-hop reasoning, where evidence acquisition and error propagation differ from standard single-hop retrieval. Our study provides an empirical characterization of conformal factuality in multi-hop RAG and examines when statistical factuality control can improve the reliability of retrieval-augmented language model outputs without excessively sacrificing informativeness. More broadly, the work connects conformal prediction with iterative retrieval and reasoning, providing a framework for studying statistically controlled generation in multi-step RAG systems.