Grounding large language models (LLMs) in verifiable external sources is a well-established strategy for generating reliable answers. Retrieval-augmented generation (RAG) is one such approach, particularly effective for tasks like question answering: it retrieves passages that are semantically related to the question and then conditions the model on this evidence. However, multi-hop questions, such as"Which company among NVIDIA, Apple, and Google made the biggest profit in 2023?,"challenge RAG because relevant facts are often distributed across multiple documents rather than co-occurring in onResearch goal: What is the accuracy drop of LLM-based answer generation in multi-hop RAG systems when using adversarial query perturbations (e.g., synonym substitution, negation) compared to single-hop queries, evaluated on the BEIR benchmark with dense vs. sparse retrievers?Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 7.8/10.