Reasoning Hop Complexity and LLM Failure Rates in Retrieval-Augmented Generation Pipelines

This report synthesises findings from 13 peer-reviewed papers addressing the following research question: What is the correlation between the number of reasoning hops in datasets like HotPotQA and the failure rate of large language models when integrated with retrieval-augmented generation pipelines. 9 claims were extracted from source literature; 9 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.0/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: What is the correlation between the number of reasoning hops in datasets like HotPotQA and the failure rate of large language models when integrated with retrieval-augmented generation pipelines?

Autonomous literature synthesis. Automated review score: 8.0/10. Full text and citation available at Assignee Research.