ReNA: Detecting Contextual Hallucinations in RAG via Reference-Contrast Neuron Atlas

Retrieval-Augmented Generation (RAG) reduces reliance on parametric knowledge by grounding language models in retrieved evidence. However, models may still override accurate context and generate responses from conflicting parametric memory. Existing detectors either evaluate outputs externally or analyze internal signals through representations or predefined component functions, leaving open the question of which specific components consistently support different knowledge sources. We instead investigate which specific neurons consistently participate in supporting contextual versus parametric generation. We introduce ReNA, a Reference-Contrast Neuron Atlas for detecting contextual hallucinations in RAG. Given the same context and question, ReNA contrasts gradient-based neuron attribution between a context-supported reference and a conflicting parametric reference. It then constructs a sparse atlas of attention and FFN neurons exhibiting strong and consistent source preferences. An actual response is classified from its attribution pattern over the discovered atlas. Experiments across multiple models and datasets show that ReNA consistently outperforms strong output-based and internal-state baselines. Ablation studies further demonstrate the effectiveness of reference contrast and identity-specific neuron attribution. Causal interventions identify the Atlas as a distributed, branch-conditioned readout and amplifier, suggesting that faithful/parametric information propagates as a broad network state expressed across many components rather than through a small, independently sufficient circuit.