This report synthesises findings from 7 peer-reviewed papers addressing the following research question: What is the impact of varying the number of hops on the robustness of multi-hop retrieval for Llama-3-8B-128K when evaluated on adversarial examples from HotPotQA and SQuAD. Selective state-space models (SSMs) like Mamba overcome some of the shortcomings of Transformers, such as quadratic computational complexity with sequence length and large inference-time memory requirements from the key-value cache. Moreover, recent studies have shown that SSMs. 13 claims were extracted from source literature; 13 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 9.3/10. This report is a machine-generated literature synthesis and does not constitute original research.Research goal: What is the impact of varying the number of hops on the robustness of multi-hop retrieval for Llama-3-8B-128K when evaluated on adversarial examples from HotPotQA and SQuAD?Autonomous literature synthesis. Automated review score: 9.3/10. Full text and citation available at Assignee Research.